LSTMlayermae10_10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LSTMlayermae10/10.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOrtq+bVzUSOBa8hBspMghE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VishnuBhaarath/Rainfallpred/blob/master/LSTMlayermae10_10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YGfJ9tYkM94",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from numpy.random import seed\n",
        "seed(1)\n",
        "from tensorflow import set_random_seed\n",
        "set_random_seed(2)\n",
        "from sklearn.metrics import r2_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cou_fBaEkP6G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from pandas import DataFrame\n",
        "import numpy as np\n",
        "from numpy import hstack\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn import preprocessing"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACH71at4kV9t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.read_excel('trend_anna_imd_21.xlsx')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hp4FJfQkk9o",
        "colab_type": "code",
        "outputId": "c715f06f-7f97-4897-a7f0-d1bd13681d03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 466,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1. Cuddalore</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1965-01-01 00:00:00</td>\n",
              "      <td>10.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1965-01-02 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1965-01-03 00:00:00</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1965-01-04 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Unnamed: 0  Unnamed: 1\n",
              "0         1. Cuddalore         NaN\n",
              "1  1965-01-01 00:00:00        10.6\n",
              "2  1965-01-02 00:00:00         0.0\n",
              "3  1965-01-03 00:00:00        13.0\n",
              "4  1965-01-04 00:00:00         0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 466
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RB9D0KOIkl-g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cuddalore=df[3655:19360]\n",
        "Panruti=df[23016:38721]\n",
        "Dharmapuri=df[42408:58082]\n",
        "Harur=df[61738:77443]\n",
        "Palacode=df[81158:96804]\n",
        "Pennagaram=df[100460:116165]\n",
        "Chengam=df[119821:135526]\n",
        "Sathanur=df[138817:154522]\n",
        "Tiruvanamalai=df[158178:173883]\n",
        "Sankarapuram=df[177539:193244]\n",
        "Tirukoilur=df[196899:212604]\n",
        "ulundurpet=df[216260:231965]\n",
        "Villupuram=df[235621:251326]\n",
        "Denkanikottai=df[274343:290048]\n",
        "Hosur=df[293703:309408]\n",
        "Krishnagiri=df[313063:328768]\n",
        "Marandahalli=df[332059:347764]\n",
        "Rayakottai=df[353250:368955]\n",
        "Uthangarai=df[372611:388316]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lT9ZtR0mBO1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cuddalore.columns = ['Date', 'Rainfall']\n",
        "Panruti.columns= ['Date', 'Rainfall']\n",
        "Dharmapuri.columns= ['Date', 'Rainfall']\n",
        "Harur.columns= ['Date', 'Rainfall']\n",
        "Palacode.columns= ['Date', 'Rainfall']\n",
        "Pennagaram.columns= ['Date', 'Rainfall']\n",
        "Chengam.columns= ['Date', 'Rainfall']\n",
        "Sathanur.columns= ['Date', 'Rainfall']\n",
        "Tiruvanamalai.columns= ['Date', 'Rainfall']\n",
        "Sankarapuram.columns= ['Date', 'Rainfall']\n",
        "Tirukoilur.columns= ['Date', 'Rainfall']\n",
        "ulundurpet.columns= ['Date', 'Rainfall']\n",
        "Villupuram.columns= ['Date', 'Rainfall']\n",
        "Denkanikottai.columns= ['Date', 'Rainfall']\n",
        "Hosur.columns= ['Date', 'Rainfall']\n",
        "Krishnagiri.columns= ['Date', 'Rainfall']\n",
        "Marandahalli.columns= ['Date', 'Rainfall']\n",
        "Rayakottai.columns= ['Date', 'Rainfall']\n",
        "Uthangarai.columns= ['Date', 'Rainfall']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ot2FqhbOm6dJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train=Cuddalore[0:12000]\n",
        "test=Cuddalore[12000:15704]\n",
        "train1=Panruti[0:12000]\n",
        "test1=Panruti[12000:15705]\n",
        "train2=Dharmapuri[0:12000]\n",
        "test2=Dharmapuri[12000:15674]\n",
        "train3=Harur[0:12000]\n",
        "test3=Harur[12000:15704]\n",
        "train4=Palacode[0:12000]\n",
        "test4=Palacode[12000:15646]\n",
        "train5=Pennagaram[0:12000]\n",
        "test5=Pennagaram[12000:15705]\n",
        "train6=Chengam[0:12000]\n",
        "test6=Chengam[12000:15705]\n",
        "train7=Sathanur[0:12000]\n",
        "test7=Sathanur[12000:15705]\n",
        "train8=Tiruvanamalai[0:12000]\n",
        "test8=Tiruvanamalai[12000:15165]\n",
        "train9=Sankarapuram[0:12000]\n",
        "test9=Sankarapuram[12000:15705]\n",
        "train10=Tirukoilur[0:12000]\n",
        "test10=Tirukoilur[12000:15705]\n",
        "train11=ulundurpet[0:12000]\n",
        "test11=ulundurpet[12000:15345]\n",
        "train12=Villupuram[0:12000]\n",
        "test12=Villupuram[12000:15705]\n",
        "train13=Denkanikottai[0:12000]\n",
        "test13=Denkanikottai[12000:15705]\n",
        "train14=Hosur[0:12000]\n",
        "test14=Hosur[12000:15678]\n",
        "train15=Krishnagiri[0:12000]\n",
        "test15=Krishnagiri[12000:15705]\n",
        "train16=Marandahalli[0:12000]\n",
        "test16=Marandahalli[12000:15705]\n",
        "train17=Rayakottai[0:12000]\n",
        "test17=Rayakottai[12000:15705]\n",
        "train18=Uthangarai[0:12000]\n",
        "test18=Uthangarai[12000:15705]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWdgMaVPnV7A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train=train['Rainfall']\n",
        "X_test=test['Rainfall']\n",
        "X_train1=train1['Rainfall']\n",
        "X_test1=test1['Rainfall']\n",
        "X_train2=train2['Rainfall']\n",
        "X_test2=test2['Rainfall']\n",
        "X_train3=train3['Rainfall']\n",
        "X_test3=test3['Rainfall']\n",
        "X_train4=train4['Rainfall']\n",
        "X_test4=test4['Rainfall']\n",
        "X_train5=train5['Rainfall']\n",
        "X_test5=test5['Rainfall']\n",
        "X_train6=train6['Rainfall']\n",
        "X_test6=test6['Rainfall']\n",
        "X_train7=train7['Rainfall']\n",
        "X_test7=test7['Rainfall']\n",
        "X_train8=train8['Rainfall']\n",
        "X_test8=test8['Rainfall']\n",
        "X_train9=train9['Rainfall']\n",
        "X_test9=test9['Rainfall']\n",
        "X_train10=train10['Rainfall']\n",
        "X_test10=test10['Rainfall']\n",
        "X_train11=train11['Rainfall']\n",
        "X_test11=test11['Rainfall']\n",
        "X_train12=train12['Rainfall']\n",
        "X_test12=test12['Rainfall']\n",
        "X_train13=train13['Rainfall']\n",
        "X_test13=test13['Rainfall']\n",
        "X_train14=train14['Rainfall']\n",
        "X_test14=test14['Rainfall']\n",
        "X_train15=train15['Rainfall']\n",
        "X_test15=test15['Rainfall']\n",
        "X_train16=train16['Rainfall']\n",
        "X_test16=test16['Rainfall']\n",
        "X_train17=train17['Rainfall']\n",
        "X_test17=test17['Rainfall']\n",
        "X_train18=train18['Rainfall']\n",
        "X_test18=test18['Rainfall']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdTZBwX0mnEs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "\n",
        "x_scaled = min_max_scaler.fit_transform(X_train.values.reshape(-1,1))\n",
        "X_train = pd.DataFrame(x_scaled)\n",
        "\n",
        "x_scaled1 = min_max_scaler.fit_transform(X_train1.values.reshape(-1,1))\n",
        "X_train1 = pd.DataFrame(x_scaled1)\n",
        "\n",
        "x_scaled2 = min_max_scaler.fit_transform(X_train2.values.reshape(-1,1))\n",
        "X_train2 = pd.DataFrame(x_scaled2)\n",
        "\n",
        "x_scaled3 = min_max_scaler.fit_transform(X_train3.values.reshape(-1,1))\n",
        "X_train3 = pd.DataFrame(x_scaled3)\n",
        "\n",
        "x_scaled4 = min_max_scaler.fit_transform(X_train4.values.reshape(-1,1))\n",
        "X_train4 = pd.DataFrame(x_scaled4)\n",
        "\n",
        "x_scaled5 = min_max_scaler.fit_transform(X_train5.values.reshape(-1,1))\n",
        "X_train5 = pd.DataFrame(x_scaled5)\n",
        "\n",
        "x_scaled6 = min_max_scaler.fit_transform(X_train6.values.reshape(-1,1))\n",
        "X_train6 = pd.DataFrame(x_scaled6)\n",
        "\n",
        "x_scaled7 = min_max_scaler.fit_transform(X_train7.values.reshape(-1,1))\n",
        "X_train7 = pd.DataFrame(x_scaled7)\n",
        "\n",
        "x_scaled8 = min_max_scaler.fit_transform(X_train8.values.reshape(-1,1))\n",
        "X_train8 = pd.DataFrame(x_scaled8)\n",
        "\n",
        "x_scaled9 = min_max_scaler.fit_transform(X_train9.values.reshape(-1,1))\n",
        "X_train9 = pd.DataFrame(x_scaled9)\n",
        "\n",
        "x_scaled10 = min_max_scaler.fit_transform(X_train10.values.reshape(-1,1))\n",
        "X_train10 = pd.DataFrame(x_scaled10)\n",
        "\n",
        "x_scaled11 = min_max_scaler.fit_transform(X_train11.values.reshape(-1,1))\n",
        "X_train11 = pd.DataFrame(x_scaled11)\n",
        "\n",
        "x_scaled1112 = min_max_scaler.fit_transform(X_train12.values.reshape(-1,1))\n",
        "X_train12 = pd.DataFrame(x_scaled1112)\n",
        "\n",
        "x_scaled13 = min_max_scaler.fit_transform(X_train13.values.reshape(-1,1))\n",
        "X_train13 = pd.DataFrame(x_scaled13)\n",
        "\n",
        "x_scaled14 = min_max_scaler.fit_transform(X_train14.values.reshape(-1,1))\n",
        "X_train14 = pd.DataFrame(x_scaled14)\n",
        "\n",
        "x_scaled15 = min_max_scaler.fit_transform(X_train15.values.reshape(-1,1))\n",
        "X_train15 = pd.DataFrame(x_scaled15)\n",
        "\n",
        "x_scaled16 = min_max_scaler.fit_transform(X_train16.values.reshape(-1,1))\n",
        "X_train16 = pd.DataFrame(x_scaled16)\n",
        "\n",
        "x_scaled17 = min_max_scaler.fit_transform(X_train17.values.reshape(-1,1))\n",
        "X_train17 = pd.DataFrame(x_scaled17)\n",
        "\n",
        "x_scaled18 = min_max_scaler.fit_transform(X_train18.values.reshape(-1,1))\n",
        "X_train18 = pd.DataFrame(x_scaled18)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CxbM6N2-oCPd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_scaled1 = min_max_scaler.fit_transform(X_test.values.reshape(-1,1))\n",
        "X_test = pd.DataFrame(x_scaled1)\n",
        "\n",
        "x_scaled11 = min_max_scaler.fit_transform(X_test1.values.reshape(-1,1))\n",
        "X_test1 = pd.DataFrame(x_scaled11)\n",
        "\n",
        "x_scaled12 = min_max_scaler.fit_transform(X_test2.values.reshape(-1,1))\n",
        "X_test2 = pd.DataFrame(x_scaled12)\n",
        "\n",
        "x_scaled13 = min_max_scaler.fit_transform(X_test3.values.reshape(-1,1))\n",
        "X_test3 = pd.DataFrame(x_scaled13)\n",
        "\n",
        "x_scaled14 = min_max_scaler.fit_transform(X_test4.values.reshape(-1,1))\n",
        "X_test4 = pd.DataFrame(x_scaled14)\n",
        "\n",
        "x_scaled15 = min_max_scaler.fit_transform(X_test5.values.reshape(-1,1))\n",
        "X_test5 = pd.DataFrame(x_scaled15)\n",
        "\n",
        "x_scaled16 = min_max_scaler.fit_transform(X_test6.values.reshape(-1,1))\n",
        "X_test6 = pd.DataFrame(x_scaled16)\n",
        "\n",
        "x_scaled17 = min_max_scaler.fit_transform(X_test7.values.reshape(-1,1))\n",
        "X_test7 = pd.DataFrame(x_scaled17)\n",
        "\n",
        "x_scaled18 = min_max_scaler.fit_transform(X_test8.values.reshape(-1,1))\n",
        "X_test8 = pd.DataFrame(x_scaled18)\n",
        "\n",
        "x_scaled19 = min_max_scaler.fit_transform(X_test9.values.reshape(-1,1))\n",
        "X_test9 = pd.DataFrame(x_scaled19)\n",
        "\n",
        "x_scaled110 = min_max_scaler.fit_transform(X_test10.values.reshape(-1,1))\n",
        "X_test10 = pd.DataFrame(x_scaled110)\n",
        "\n",
        "x_scaled111 = min_max_scaler.fit_transform(X_test11.values.reshape(-1,1))\n",
        "X_test11 = pd.DataFrame(x_scaled111)\n",
        "\n",
        "x_scaled112 = min_max_scaler.fit_transform(X_test12.values.reshape(-1,1))\n",
        "X_test12 = pd.DataFrame(x_scaled112)\n",
        "\n",
        "x_scaled113 = min_max_scaler.fit_transform(X_test13.values.reshape(-1,1))\n",
        "X_test13 = pd.DataFrame(x_scaled113)\n",
        "\n",
        "x_scaled114 = min_max_scaler.fit_transform(X_test14.values.reshape(-1,1))\n",
        "X_test14 = pd.DataFrame(x_scaled114)\n",
        "\n",
        "x_scaled115= min_max_scaler.fit_transform(X_test15.values.reshape(-1,1))\n",
        "X_test15 = pd.DataFrame(x_scaled115)\n",
        "\n",
        "x_scaled116 = min_max_scaler.fit_transform(X_test16.values.reshape(-1,1))\n",
        "X_test16 = pd.DataFrame(x_scaled116)\n",
        "\n",
        "x_scaled117 = min_max_scaler.fit_transform(X_test17.values.reshape(-1,1))\n",
        "X_test17 = pd.DataFrame(x_scaled117)\n",
        "\n",
        "x_scaled118 = min_max_scaler.fit_transform(X_test18.values.reshape(-1,1))\n",
        "X_test18 = pd.DataFrame(x_scaled118)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZNCwdZhrTQD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "in_seq = np.array([X_train[0]])\n",
        "in_seqtest1 = np.array([X_test[0]])\n",
        "\n",
        "in_seq1 = np.array([X_train1[0]])\n",
        "in_seqt11 = np.array([X_test1[0]])\n",
        "\n",
        "in_seq12 = np.array([X_train2[0]])\n",
        "in_seqt112 = np.array([X_test2[0]])\n",
        "\n",
        "in_seq13 = np.array([X_train3[0]])\n",
        "in_seqt113 = np.array([X_test3[0]])\n",
        "\n",
        "in_seq14 = np.array([X_train4[0]])\n",
        "in_seqt114 = np.array([X_test4[0]])\n",
        "\n",
        "in_seq15 = np.array([X_train5[0]])\n",
        "in_seqt115 = np.array([X_test5[0]])\n",
        "\n",
        "in_seq16 = np.array([X_train6[0]])\n",
        "in_seqt116 = np.array([X_test6[0]])\n",
        "\n",
        "in_seq17 = np.array([X_train7[0]])\n",
        "in_seqt117 = np.array([X_test7[0]])\n",
        "\n",
        "in_seq18 = np.array([X_train8[0]])\n",
        "in_seqt118 = np.array([X_test8[0]])\n",
        "\n",
        "in_seq19 = np.array([X_train9[0]])\n",
        "in_seqt119 = np.array([X_test9[0]])\n",
        "\n",
        "in_seq110 = np.array([X_train10[0]])\n",
        "in_seqt1110 = np.array([X_test10[0]])\n",
        "\n",
        "in_seq111 = np.array([X_train11[0]])\n",
        "in_seqt1111 = np.array([X_test11[0]])\n",
        "\n",
        "in_seq112 = np.array([X_train12[0]])\n",
        "in_seqt1112 = np.array([X_test12[0]])\n",
        "\n",
        "in_seq113 = np.array([X_train13[0]])\n",
        "in_seqt1113 = np.array([X_test13[0]])\n",
        "\n",
        "in_seq114 = np.array([X_train14[0]])\n",
        "in_seqt1114 = np.array([X_test14[0]])\n",
        "\n",
        "in_seq115 = np.array([X_train15[0]])\n",
        "in_seqt1115 = np.array([X_test15[0]])\n",
        "\n",
        "in_seq116 = np.array([X_train16[0]])\n",
        "in_seqt1116 = np.array([X_test16[0]])\n",
        "\n",
        "in_seq117 = np.array([X_train17[0]])\n",
        "in_seqt1117 = np.array([X_test17[0]])\n",
        "\n",
        "in_seq118 = np.array([X_train18[0]])\n",
        "in_seqt1118 = np.array([X_test18[0]])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vtw9-GtrbhY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "in_seq = in_seq.reshape((12000, 1))\n",
        "in_seqtest1 = in_seqtest1.reshape((3704, 1))\n",
        "\n",
        "in_seq1 = in_seq1.reshape((12000, 1))\n",
        "in_seqt11 = in_seqt11.reshape((3705, 1))\n",
        "\n",
        "in_seq12 = in_seq12.reshape((12000, 1))\n",
        "in_seqt112 = in_seqt112.reshape((3674, 1))\n",
        "\n",
        "in_seq13 = in_seq13.reshape((12000, 1))\n",
        "in_seqt113 = in_seqt113.reshape((3704, 1))\n",
        "\n",
        "in_seq14 = in_seq14.reshape((12000, 1))\n",
        "in_seqt114 = in_seqt114.reshape((3646, 1))\n",
        "\n",
        "in_seq15 = in_seq15.reshape((12000, 1))\n",
        "in_seqt115 = in_seqt115.reshape((3705, 1))\n",
        "\n",
        "in_seq16 = in_seq16.reshape((12000, 1))\n",
        "in_seqt116 = in_seqt116.reshape((3705, 1))\n",
        "\n",
        "in_seq17 = in_seq17.reshape((12000, 1))\n",
        "in_seqt117 = in_seqt117.reshape((3705, 1))\n",
        "\n",
        "in_seq18 = in_seq18.reshape((12000, 1))\n",
        "in_seqt118 = in_seqt118.reshape((3165, 1))\n",
        "\n",
        "in_seq19 = in_seq19.reshape((12000, 1))\n",
        "in_seqt119 = in_seqt119.reshape((3705, 1))\n",
        "\n",
        "in_seq110 = in_seq110.reshape((12000, 1))\n",
        "in_seqt1110 = in_seqt1110.reshape((3705, 1))\n",
        "\n",
        "in_seq111 = in_seq111.reshape((12000, 1))\n",
        "in_seqt1111 = in_seqt1111.reshape((3345, 1))\n",
        "\n",
        "in_seq112 = in_seq112.reshape((12000, 1))\n",
        "in_seqt1112 = in_seqt1112.reshape((3705, 1))\n",
        "\n",
        "in_seq113 = in_seq113.reshape((12000, 1))\n",
        "in_seqt1113 = in_seqt1113.reshape((3705, 1))\n",
        "\n",
        "in_seq114 = in_seq114.reshape((12000, 1))\n",
        "in_seqt1114 = in_seqt1114.reshape((3678, 1))\n",
        "\n",
        "in_seq115 = in_seq115.reshape((12000, 1))\n",
        "in_seqt1115 = in_seqt1115.reshape((3705, 1))\n",
        "\n",
        "in_seq116 = in_seq116.reshape((12000, 1))\n",
        "in_seqt1116 = in_seqt1116.reshape((3705, 1))\n",
        "\n",
        "in_seq117 = in_seq117.reshape((12000, 1))\n",
        "in_seqt1117 = in_seqt1117.reshape((3705, 1))\n",
        "\n",
        "in_seq118 = in_seq118.reshape((12000, 1))\n",
        "in_seqt1118 = in_seqt1118.reshape((3704, 1))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcthfPpSoN2u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_sequence(sequence, n_steps):\n",
        "\tX, y = list(), list()\n",
        "\tfor i in range(len(sequence)):\n",
        "\t\t# find the end of this pattern\n",
        "\t\tend_ix = i + n_steps\n",
        "\t\t# check if we are beyond the sequence\n",
        "\t\tif end_ix > len(sequence)-1:\n",
        "\t\t\tbreak\n",
        "\t\t# gather input and output parts of the pattern\n",
        "\t\tseq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
        "\t\tX.append(seq_x)\n",
        "\t\ty.append(seq_y)\n",
        "\treturn np.array(X), np.array(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLCLOXnyptMN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cuddataset1 = hstack((in_seqtest1))\n",
        "cuddataset = hstack((in_seq))\n",
        "\n",
        "pandataset1 = hstack((in_seqt11))\n",
        "pandataset = hstack((in_seq1))\n",
        "\n",
        "dhadataset1 = hstack((in_seqt112))\n",
        "dhadataset = hstack((in_seq12))\n",
        "\n",
        "hardataset1 = hstack((in_seqt113))\n",
        "hardataset = hstack((in_seq13))\n",
        "\n",
        "paldataset1 = hstack((in_seqt114))\n",
        "paldataset = hstack((in_seq14))\n",
        "\n",
        "pendataset1 = hstack((in_seqt115))\n",
        "pendataset = hstack((in_seq15))\n",
        "\n",
        "chedataset1 = hstack((in_seqt116))\n",
        "chedataset = hstack((in_seq16))\n",
        "\n",
        "satdataset1 = hstack((in_seqt117))\n",
        "satdataset = hstack((in_seq17))\n",
        "\n",
        "Tirdataset1 = hstack((in_seqt118))\n",
        "Tirdataset = hstack((in_seq18))\n",
        "\n",
        "Sandataset1 = hstack((in_seqt119))\n",
        "Sandataset = hstack((in_seq19))\n",
        "\n",
        "Tirudataset1 = hstack((in_seqt1110))\n",
        "Tirudataset = hstack((in_seq110))\n",
        "\n",
        "uludataset1 = hstack((in_seqt1111))\n",
        "uludataset = hstack((in_seq111))\n",
        "\n",
        "vildataset1 = hstack((in_seqt1112))\n",
        "vildataset = hstack((in_seq112))\n",
        "\n",
        "bardataset1 = hstack((in_seqt1113))\n",
        "bardataset = hstack((in_seq113))\n",
        "\n",
        "Dendataset1 = hstack((in_seqt1114))\n",
        "Dendataset = hstack((in_seq114))\n",
        "\n",
        "kridataset1 = hstack((in_seqt1115))\n",
        "kridataset = hstack((in_seq115))\n",
        "\n",
        "mardataset1 = hstack((in_seqt1116))\n",
        "mardataset = hstack((in_seq116))\n",
        "\n",
        "raydataset1 = hstack((in_seqt1117))\n",
        "raydataset = hstack((in_seq117))\n",
        "\n",
        "uthdataset1 = hstack((in_seqt1118))\n",
        "uthdataset = hstack((in_seq118))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXevl6Wdrz2e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "n_steps = 3\n",
        "# convert into input/output\n",
        "X, y = split_sequence(cuddataset, n_steps)\n",
        "X1 , y1 = split_sequence(pandataset, n_steps)\n",
        "X2 , y2 = split_sequence(dhadataset, n_steps)\n",
        "X3 , y3 = split_sequence(hardataset, n_steps)\n",
        "X4 , y4 = split_sequence(paldataset, n_steps)\n",
        "X5 , y5 = split_sequence(pendataset, n_steps)\n",
        "X6, y6 = split_sequence(chedataset, n_steps)\n",
        "X7, y7 = split_sequence(satdataset, n_steps)\n",
        "X8, y8 = split_sequence(Tirdataset, n_steps)\n",
        "X9, y9 = split_sequence(Sandataset, n_steps)\n",
        "X10, y10 = split_sequence(Tirudataset, n_steps)\n",
        "X11, y11 = split_sequence(uludataset, n_steps)\n",
        "X12 , y12 = split_sequence(vildataset, n_steps)\n",
        "X13, y13 = split_sequence(bardataset, n_steps)\n",
        "X14, y14 = split_sequence(Dendataset, n_steps)\n",
        "X15, y15 = split_sequence(kridataset, n_steps)\n",
        "X16, y16 = split_sequence(mardataset, n_steps)\n",
        "X17, y17 = split_sequence(raydataset, n_steps)\n",
        "X18, y18 = split_sequence(uthdataset, n_steps)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcgWCIqIr44M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test,y_test = split_sequence(cuddataset, n_steps)\n",
        "X_test1,y_test1 = split_sequence(pandataset, n_steps)\n",
        "X_test2,y_test2= split_sequence(dhadataset, n_steps)\n",
        "X_test3,y_test3 = split_sequence(hardataset, n_steps)\n",
        "X_test4,y_test4 = split_sequence(paldataset, n_steps)\n",
        "X_test5,y_test5 = split_sequence(pendataset, n_steps)\n",
        "X_test6,y_test6 = split_sequence(chedataset, n_steps)\n",
        "X_test7,y_test7 = split_sequence(satdataset, n_steps)\n",
        "X_test8,y_test8 = split_sequence(Tirdataset, n_steps)\n",
        "X_test9,y_test9 = split_sequence(Sandataset, n_steps)\n",
        "X_test10,y_test10 = split_sequence(Tirudataset, n_steps)\n",
        "X_test11,y_test11= split_sequence(uludataset, n_steps)\n",
        "X_test12,y_test12 = split_sequence(vildataset, n_steps)\n",
        "X_test13,y_test13 = split_sequence(bardataset, n_steps)\n",
        "X_test14,y_test14 = split_sequence(Dendataset, n_steps)\n",
        "X_test15,y_test15 = split_sequence(kridataset, n_steps)\n",
        "X_test16,y_test16 = split_sequence(mardataset, n_steps)\n",
        "X_test17,y_test17= split_sequence(raydataset, n_steps)\n",
        "X_test18,y_test18= split_sequence(uthdataset, n_steps)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZ26OGqHr95T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = keras.Sequential()\n",
        "model.add(keras.layers.LSTM(10, activation='relu',return_sequences=True, input_shape=(n_steps, 1)))\n",
        "model.add(keras.layers.LSTM(10, activation='relu'))\n",
        "model.add(keras.layers.Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUwsDYObsG7O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = X.reshape((X.shape[0], X.shape[1], 1))\n",
        "X1 = X1.reshape((X1.shape[0], X1.shape[1], 1))\n",
        "X2 = X2.reshape((X2.shape[0], X2.shape[1], 1))\n",
        "X3 = X3.reshape((X3.shape[0], X3.shape[1], 1))\n",
        "X4 = X4.reshape((X4.shape[0], X4.shape[1], 1))\n",
        "X5 = X5.reshape((X5.shape[0], X5.shape[1], 1))\n",
        "X6 = X6.reshape((X6.shape[0], X6.shape[1], 1))\n",
        "X7 = X7.reshape((X7.shape[0], X7.shape[1], 1))\n",
        "X8 = X8.reshape((X8.shape[0], X8.shape[1], 1))\n",
        "X9 = X9.reshape((X9.shape[0], X9.shape[1], 1))\n",
        "X10 = X10.reshape((X10.shape[0], X10.shape[1], 1))\n",
        "X11 = X11.reshape((X11.shape[0], X11.shape[1], 1))\n",
        "X12 = X12.reshape((X12.shape[0], X12.shape[1], 1))\n",
        "X13 = X13.reshape((X13.shape[0], X13.shape[1], 1))\n",
        "X14 = X14.reshape((X14.shape[0], X14.shape[1], 1))\n",
        "X15 = X15.reshape((X15.shape[0], X15.shape[1], 1))\n",
        "X16 = X16.reshape((X16.shape[0], X16.shape[1], 1))\n",
        "X17 = X17.reshape((X17.shape[0], X17.shape[1], 1))\n",
        "X18 = X18.reshape((X18.shape[0], X18.shape[1], 1))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3GPbJFSslfM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test=X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
        "X_test1=X_test1.reshape((X_test1.shape[0], X_test1.shape[1], 1))\n",
        "X_test2=X_test2.reshape((X_test2.shape[0], X_test2.shape[1], 1))\n",
        "X_test3=X_test3.reshape((X_test3.shape[0], X_test3.shape[1], 1))\n",
        "X_test4=X_test4.reshape((X_test4.shape[0], X_test4.shape[1], 1))\n",
        "X_test5=X_test5.reshape((X_test5.shape[0], X_test5.shape[1], 1))\n",
        "X_test6=X_test6.reshape((X_test6.shape[0], X_test6.shape[1], 1))\n",
        "X_test7=X_test7.reshape((X_test7.shape[0], X_test7.shape[1], 1))\n",
        "X_test8=X_test8.reshape((X_test8.shape[0], X_test8.shape[1], 1))\n",
        "X_test9=X_test9.reshape((X_test9.shape[0], X_test9.shape[1], 1))\n",
        "X_test10=X_test10.reshape((X_test10.shape[0], X_test10.shape[1], 1))\n",
        "X_test11=X_test11.reshape((X_test11.shape[0], X_test11.shape[1], 1))\n",
        "X_test12=X_test12.reshape((X_test12.shape[0], X_test12.shape[1], 1))\n",
        "X_test13=X_test13.reshape((X_test13.shape[0], X_test13.shape[1], 1))\n",
        "X_test14=X_test14.reshape((X_test14.shape[0], X_test14.shape[1], 1))\n",
        "X_test15=X_test15.reshape((X_test15.shape[0], X_test15.shape[1], 1))\n",
        "\n",
        "X_test16=X_test16.reshape((X_test16.shape[0], X_test16.shape[1], 1))\n",
        "X_test17=X_test17.reshape((X_test17.shape[0], X_test17.shape[1], 1))\n",
        "X_test18=X_test18.reshape((X_test18.shape[0], X_test18.shape[1], 1))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJ0AjGmYsCUs",
        "colab_type": "code",
        "outputId": "a3272637-ae01-4a7e-c71c-6b71dd688d6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.fit(X, y, validation_data=(X_test,y_test),epochs=50)"
      ],
      "execution_count": 482,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 4s 338us/sample - loss: 0.0013 - acc: 0.7724 - val_loss: 0.0012 - val_acc: 0.7724\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0012 - acc: 0.7724 - val_loss: 0.0012 - val_acc: 0.7724\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0012 - acc: 0.7724 - val_loss: 0.0012 - val_acc: 0.7724\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0012 - acc: 0.7724 - val_loss: 0.0012 - val_acc: 0.7724\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0012 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0011 - acc: 0.7724 - val_loss: 0.0011 - val_acc: 0.7724\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3de47fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 482
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qW849L-NsFhU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "a90f54af-6afd-4f65-f399-8199ee59d346"
      },
      "source": [
        "ypred = model.predict(X)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 483,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.402748\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 483
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noGIobrktgmd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "6fab8cd2-f3fc-40df-9922-4b924b16dc6e"
      },
      "source": [
        "ypred1=model.predict(X_test)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 484,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.402748\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 484
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAqZPa33aTfo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpEoHv92aYGB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "243c8449-908d-4f28-9604-436d0a7adf92"
      },
      "source": [
        "mean_absolute_error(y,ypred)"
      ],
      "execution_count": 486,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.010832907377002458"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 486
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UutDAI0Oaah6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "78fd283c-ed44-4130-fe20-13266014c494"
      },
      "source": [
        "mean_absolute_error(y_test,ypred1)"
      ],
      "execution_count": 487,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.010832907377002458"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 487
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xg8kvCOYtkrq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b09203e6-87a1-4a31-810a-c1673da38bdf"
      },
      "source": [
        " from sklearn.metrics import r2_score\n",
        " r2_score(y,ypred)"
      ],
      "execution_count": 488,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.15352589113834425"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 488
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7rV7tCmtn70",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b0005041-25a7-417b-a5a3-d4674a286a1f"
      },
      "source": [
        "r2_score(y_test,ypred1)"
      ],
      "execution_count": 489,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.15352589113834425"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 489
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpGZF90Nw8oU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ef26e3b7-6076-425a-dfaa-a630968b50ec"
      },
      "source": [
        "model.fit(X1, y1, validation_data=(X_test1,y_test1),epochs=50)"
      ],
      "execution_count": 490,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 270us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0014 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0014 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0014 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0014 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0014 - val_acc: 0.8519\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0015 - acc: 0.8519 - val_loss: 0.0015 - val_acc: 0.8519\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3ae00160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 490
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbC1-ArQHzOa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "9f0d3907-a376-48b2-d101-5a98216cb07d"
      },
      "source": [
        "ypred = model.predict(X1)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y1)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 491,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.358671\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 491
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjJslfLMJEbs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "5bf4d437-ff9c-4f7a-872d-b596ee609019"
      },
      "source": [
        "ypred1=model.predict(X_test1)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test1)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 492,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.358671\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 492
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iot6np04aiT_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8753b33f-0ff8-4d25-be67-83b19ff483ae"
      },
      "source": [
        "mean_absolute_error(y1,ypred)"
      ],
      "execution_count": 493,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01685128834393564"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 493
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8iPWieZam7l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4a26ee64-c1d8-48c9-dca6-898ab72d18a1"
      },
      "source": [
        "mean_absolute_error(y_test1,ypred1)"
      ],
      "execution_count": 494,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01685128834393564"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 494
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbLt0Hz1Mop2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7b36cae9-3293-4f83-9fad-fd10946c0bd8"
      },
      "source": [
        "r2_score(y1,ypred)"
      ],
      "execution_count": 495,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11982962321349011"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 495
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7U_mkgGMrxT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e293e11d-6da1-48f5-ff28-e9e91bdd0ed1"
      },
      "source": [
        "r2_score(y_test1,ypred1)"
      ],
      "execution_count": 496,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11982962321349011"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 496
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dyKBYmkUtKUU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "35e362c2-93e8-4a62-f3ed-15cbce22d412"
      },
      "source": [
        "\n",
        "model.fit(X2, y2, validation_data=(X_test2,y_test2),epochs=50)"
      ],
      "execution_count": 497,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0048 - val_acc: 0.8414\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0048 - val_acc: 0.8414\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0047 - acc: 0.8414 - val_loss: 0.0047 - val_acc: 0.8414\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e06a7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 497
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEu6KMnhH0z8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "cc4e2723-52cd-49b8-bb71-da50a1273efb"
      },
      "source": [
        "ypred = model.predict(X2)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y2)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 498,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.244894\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 498
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zh49J42kJF0i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "1f8bb962-57d1-4858-cb7c-f0630d79cf97"
      },
      "source": [
        "ypred1=model.predict(X_test2)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test2)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 499,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.244894\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 499
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xwk6R9otatSo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ad6c25b1-472e-41e7-e65c-78b1cb2d5be2"
      },
      "source": [
        "mean_absolute_error(y2,ypred)"
      ],
      "execution_count": 500,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.030115819048776065"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 500
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqGICblJaxX-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "47ce7d4e-f34c-418b-e983-223b14d687ae"
      },
      "source": [
        "mean_absolute_error(y_test2,ypred1)"
      ],
      "execution_count": 501,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.030115819048776065"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 501
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jFG77mOGMjRL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "827c3d10-76d4-4ba7-df39-05e46ebcd9b2"
      },
      "source": [
        "r2_score(y2,ypred)"
      ],
      "execution_count": 502,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05888761520326036"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 502
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOkYZ-wjMj_C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "39b8f913-28b4-4e56-fe60-69a39a5d0c66"
      },
      "source": [
        "r2_score(y_test2,ypred1)"
      ],
      "execution_count": 503,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05888761520326036"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 503
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFNvtKzUtMus",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "dca1128d-6f7d-48fd-eb2d-5734d1609df9"
      },
      "source": [
        "\n",
        "model.fit(X3, y3, validation_data=(X_test3,y_test3),epochs=50)"
      ],
      "execution_count": 504,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 278us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0031 - val_acc: 0.8605\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0031 - val_acc: 0.8605\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0031 - val_acc: 0.8605\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0031 - val_acc: 0.8605\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0031 - val_acc: 0.8605\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0031 - val_acc: 0.8605\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0031 - val_acc: 0.8605\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0031 - val_acc: 0.8605\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0032 - acc: 0.8605 - val_loss: 0.0032 - val_acc: 0.8605\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3dfdfeb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 504
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkOlpfGdH2gZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "324d5e8e-ba78-4405-a61e-3d2522e3acd2"
      },
      "source": [
        "ypred = model.predict(X3)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y3)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 505,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.255005\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 505
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DCzVU6iJHQ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "d77bba82-554e-49c2-a46e-c1b116820390"
      },
      "source": [
        "ypred1=model.predict(X_test3)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test3)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 506,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.255005\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 506
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FO_LLgNyMcxl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5026d217-5ee9-40cb-a267-c9ae5f635190"
      },
      "source": [
        "\n",
        "r2_score(y3,ypred)"
      ],
      "execution_count": 507,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06232406696299597"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 507
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Dou4U6tMfBa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ca11115a-f2ba-4f51-fdf9-e371603221f4"
      },
      "source": [
        "r2_score(y_test3,ypred1)"
      ],
      "execution_count": 508,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06232406696299597"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 508
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vg9UDalba2r6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d0f911bd-21c5-4ceb-c139-f8aeab89995b"
      },
      "source": [
        "mean_absolute_error(y3,ypred)"
      ],
      "execution_count": 509,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.026445021789039555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 509
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PKHNKah8a6y4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9efb7b20-926e-40e1-f870-d2f5bb068c65"
      },
      "source": [
        "mean_absolute_error(y_test3,ypred1)"
      ],
      "execution_count": 510,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.026445021789039555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 510
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgByKfhitNMk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b2d52727-20a0-4116-8f6d-ac21d78b2071"
      },
      "source": [
        "model.fit(X4, y4, validation_data=(X_test4,y_test4),epochs=50)"
      ],
      "execution_count": 511,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8360 - val_loss: 0.0028 - val_acc: 0.8360\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0028 - acc: 0.8360 - val_loss: 0.0027 - val_acc: 0.8360\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e11a828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 511
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsI-w74FH36K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "1e84a78f-5bac-4e74-bb97-590cd1606303"
      },
      "source": [
        "ypred = model.predict(X4)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y4)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 512,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.287969\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 512
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BppViBEjJI1p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c5a84ed1-c141-47b4-b025-faf6ab8da4d4"
      },
      "source": [
        "ypred1=model.predict(X_test4)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test4)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 513,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.287969\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 513
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wd6c1LKXa_r6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "80957a6e-63b0-47ca-9141-be12e6466f36"
      },
      "source": [
        "mean_absolute_error(y4,ypred)"
      ],
      "execution_count": 514,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02192474652799972"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 514
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYBttiDKbDd4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "affbc100-36ae-43d1-c6bd-1ff34c1c12a0"
      },
      "source": [
        "mean_absolute_error(y_test4,ypred1)"
      ],
      "execution_count": 515,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02192474652799972"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 515
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7U3xQIlMU7j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "46146bc9-cbbf-487e-a46d-e714d4b5ed2b"
      },
      "source": [
        "r2_score(y4,ypred)"
      ],
      "execution_count": 516,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08265896630559344"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 516
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "te0hYyPUMXwG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b8d9203e-cc4a-42dc-d208-20ad447feca4"
      },
      "source": [
        "r2_score(y_test4,ypred1)"
      ],
      "execution_count": 517,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08265896630559344"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 517
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pwz3PEtJtNcq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "aef4df34-285a-4f20-d858-07d01a8f45f5"
      },
      "source": [
        "model.fit(X5, y5, validation_data=(X_test5,y_test5),epochs=50)"
      ],
      "execution_count": 518,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0018 - val_acc: 0.8467\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0018 - acc: 0.8467 - val_loss: 0.0017 - val_acc: 0.8467\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e11a748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 518
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jK30TTmsH5Xa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "a0684443-d1c2-48d4-8d33-9d7e6db80308"
      },
      "source": [
        "ypred = model.predict(X5)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y5)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 519,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.245729\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 519
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELbd5s3jML2y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3d471b1a-d69f-48a8-9114-dee1b24d294b"
      },
      "source": [
        "r2_score(y5,ypred)"
      ],
      "execution_count": 520,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.060013381815130495"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 520
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixBEfQXVJKU4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "281d0a66-ca04-4a34-fa58-b371a8cd1656"
      },
      "source": [
        "ypred1=model.predict(X_test5)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test5)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 521,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.245729\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 521
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDE8ckrObIuQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b63b60e7-f7af-445a-87c4-6fc26525b9e5"
      },
      "source": [
        "mean_absolute_error(y5,ypred)"
      ],
      "execution_count": 522,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01707023641685253"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 522
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcrMGkNXbMX4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c25ad0f7-bd8e-4398-e275-39fc8e5a71cd"
      },
      "source": [
        "mean_absolute_error(y_test5,ypred1)"
      ],
      "execution_count": 523,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01707023641685253"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 523
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "70Aaq_luMOxY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "427a4555-280e-4db0-9021-d8fc9301685f"
      },
      "source": [
        "r2_score(y_test5,ypred1)"
      ],
      "execution_count": 524,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.060013381815130495"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 524
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mJL5gGhXtNqT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5ea21075-f02e-4224-bb8c-dbe97823949f"
      },
      "source": [
        "model.fit(X6, y6, validation_data=(X_test6,y_test6),epochs=50)"
      ],
      "execution_count": 525,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0032 - val_acc: 0.8141\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0030 - val_acc: 0.8141\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0030 - val_acc: 0.8141\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0030 - val_acc: 0.8141\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0030 - val_acc: 0.8141\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0031 - val_acc: 0.8141\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0030 - val_acc: 0.8141\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0030 - val_acc: 0.8141\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0031 - acc: 0.8141 - val_loss: 0.0030 - val_acc: 0.8141\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e0ed6a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 525
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtgC2LPgH7Gj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "4f088399-d6ea-43ba-966f-e8fdda53db78"
      },
      "source": [
        "ypred = model.predict(X6)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y6)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 526,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.315657\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 526
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0pz7S13uMF-1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "64fa4913-6d49-42ee-84e9-f3c050643858"
      },
      "source": [
        "r2_score(y6,ypred)"
      ],
      "execution_count": 527,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0978757218740195"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 527
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YnWGLzXNJMYO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "86793751-64cd-4f4e-f89f-a10323a76852"
      },
      "source": [
        "ypred1=model.predict(X_test6)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test6)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 528,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.315657\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 528
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_sfckB5bT_O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5cb3cc1a-0493-40e4-baad-13048750c9bf"
      },
      "source": [
        "mean_absolute_error(y6,ypred)"
      ],
      "execution_count": 529,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.025737995929348976"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 529
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcZfNKSYbXj6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "097c193c-8e4c-47fb-fc38-a092aa112e36"
      },
      "source": [
        "mean_absolute_error(y_test6,ypred1)"
      ],
      "execution_count": 530,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.025737995929348976"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 530
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHt09FiPMHsf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "153fe267-cc7b-4d73-fffe-0c9375e30faf"
      },
      "source": [
        "r2_score(y_test6,ypred1)"
      ],
      "execution_count": 531,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0978757218740195"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 531
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMeE8OZ5tN17",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "73d9a7f5-022d-4d9e-a927-74cd5d172ad6"
      },
      "source": [
        "model.fit(X7, y7, validation_data=(X_test7,y_test7),epochs=50)"
      ],
      "execution_count": 532,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0035 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0035 - acc: 0.8333 - val_loss: 0.0035 - val_acc: 0.8333\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0035 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0035 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0035 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0035 - val_acc: 0.8333\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0035 - val_acc: 0.8333\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0034 - acc: 0.8333 - val_loss: 0.0034 - val_acc: 0.8333\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e0ed048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 532
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgPagHEqH8-M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "ebd6b1e2-3a29-4eaa-a3bd-d4f53a81a8f2"
      },
      "source": [
        "ypred = model.predict(X7)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y7)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 533,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.337386\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 533
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6a79eEsL-Bp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4c8fab9d-fa64-4c96-b12c-b306be3e2ef2"
      },
      "source": [
        "r2_score(y7,ypred)"
      ],
      "execution_count": 534,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11339589998838384"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 534
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcKAsBAdJNw0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "20011036-c1da-4069-9a65-19a17024716b"
      },
      "source": [
        "ypred1=model.predict(X_test7)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test7)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 535,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.337386\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 535
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7lPJIH0MBZQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "17f0ed7c-b458-4be7-8d32-5e30f1805725"
      },
      "source": [
        "r2_score(y_test7,ypred1)"
      ],
      "execution_count": 536,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11339589998838384"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 536
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiVfe-7WbeVn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "678497b8-fcdd-4de8-8c9c-a94fe496f567"
      },
      "source": [
        "mean_absolute_error(y7,ypred)"
      ],
      "execution_count": 537,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.024296583486229196"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 537
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yDQLYQrKbjHy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8155f020-22a7-44d8-f565-667984a9bcf8"
      },
      "source": [
        "mean_absolute_error(y_test7,ypred1)"
      ],
      "execution_count": 538,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.024296583486229196"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 538
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Oi8Ac5UtOAC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "04c8be83-bb21-4e26-c6ff-f9ee59672a14"
      },
      "source": [
        "model.fit(X8, y8, validation_data=(X_test8,y_test8),epochs=50)"
      ],
      "execution_count": 539,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0036 - acc: 0.8384 - val_loss: 0.0036 - val_acc: 0.8384\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e0ed9b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 539
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMLxjFOLH-PS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "cf9183d4-5e53-4164-c2ae-37dc3d9ab706"
      },
      "source": [
        "ypred = model.predict(X8)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y8)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 540,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.311858\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 540
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmVt8ALiL4ae",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "06b8d269-2783-468c-9240-200da83da5d0"
      },
      "source": [
        "r2_score(y8,ypred)"
      ],
      "execution_count": 541,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09719647429695966"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 541
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysPaeNMUJP6D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "91dbc094-2744-4bf6-ddbd-9b11166cc984"
      },
      "source": [
        "ypred1=model.predict(X_test8)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test8)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 542,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.311858\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 542
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7bVrtCdbq_p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5e060bf4-4fc7-488c-8dec-c44246d6d2a0"
      },
      "source": [
        "mean_absolute_error(y8,ypred)"
      ],
      "execution_count": 543,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02482902570435316"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 543
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bdk4ywxAbsZF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "43da625e-450f-4d89-ca17-e2361083c89d"
      },
      "source": [
        "mean_absolute_error(y_test8,ypred1)"
      ],
      "execution_count": 544,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02482902570435316"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 544
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VdoqG1uCL6Up",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "39a601ae-8ee5-4352-f2be-e0cc3b2c876a"
      },
      "source": [
        "r2_score(y_test8,ypred1)"
      ],
      "execution_count": 545,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09719647429695966"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 545
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLn0vU66tOKK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fd271ea2-25b1-4a17-8101-3cb1f5eef8f2"
      },
      "source": [
        "model.fit(X9, y9, validation_data=(X_test9,y_test9),epochs=50)"
      ],
      "execution_count": 546,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 3.6563e-04 - acc: 0.8766 - val_loss: 3.6096e-04 - val_acc: 0.8766\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 3.5960e-04 - acc: 0.8766 - val_loss: 3.5824e-04 - val_acc: 0.8766\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 3.5854e-04 - acc: 0.8766 - val_loss: 3.5668e-04 - val_acc: 0.8766\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 3.5856e-04 - acc: 0.8766 - val_loss: 3.5598e-04 - val_acc: 0.8766\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 3.5819e-04 - acc: 0.8766 - val_loss: 3.5788e-04 - val_acc: 0.8766\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 3.5718e-04 - acc: 0.8766 - val_loss: 3.5577e-04 - val_acc: 0.8766\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 3.5847e-04 - acc: 0.8766 - val_loss: 3.5629e-04 - val_acc: 0.8766\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 3.5790e-04 - acc: 0.8766 - val_loss: 3.5550e-04 - val_acc: 0.8766\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 3.5805e-04 - acc: 0.8766 - val_loss: 3.5808e-04 - val_acc: 0.8766\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 3.5736e-04 - acc: 0.8766 - val_loss: 3.6113e-04 - val_acc: 0.8766\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 3.5807e-04 - acc: 0.8766 - val_loss: 3.5453e-04 - val_acc: 0.8766\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 3.5862e-04 - acc: 0.8766 - val_loss: 3.5459e-04 - val_acc: 0.8766\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 3.5662e-04 - acc: 0.8766 - val_loss: 3.5844e-04 - val_acc: 0.8766\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 3.5840e-04 - acc: 0.8766 - val_loss: 3.5414e-04 - val_acc: 0.8766\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 3.5791e-04 - acc: 0.8766 - val_loss: 3.5365e-04 - val_acc: 0.8766\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 3.5842e-04 - acc: 0.8766 - val_loss: 3.5816e-04 - val_acc: 0.8766\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 3.5767e-04 - acc: 0.8766 - val_loss: 3.5444e-04 - val_acc: 0.8766\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 3.5643e-04 - acc: 0.8766 - val_loss: 3.5579e-04 - val_acc: 0.8766\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 3.5621e-04 - acc: 0.8766 - val_loss: 3.5438e-04 - val_acc: 0.8766\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 3.5771e-04 - acc: 0.8766 - val_loss: 3.5356e-04 - val_acc: 0.8766\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 3.5926e-04 - acc: 0.8766 - val_loss: 3.5442e-04 - val_acc: 0.8766\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 3.5566e-04 - acc: 0.8766 - val_loss: 3.6616e-04 - val_acc: 0.8766\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 3.5738e-04 - acc: 0.8766 - val_loss: 3.5410e-04 - val_acc: 0.8766\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 3.5560e-04 - acc: 0.8766 - val_loss: 3.5344e-04 - val_acc: 0.8766\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 3.5625e-04 - acc: 0.8766 - val_loss: 3.5430e-04 - val_acc: 0.8766\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 3.5746e-04 - acc: 0.8766 - val_loss: 3.5388e-04 - val_acc: 0.8766\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 3.5591e-04 - acc: 0.8766 - val_loss: 3.5603e-04 - val_acc: 0.8766\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 3.5635e-04 - acc: 0.8766 - val_loss: 3.5590e-04 - val_acc: 0.8766\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 3.5672e-04 - acc: 0.8766 - val_loss: 3.5369e-04 - val_acc: 0.8766\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 3.5691e-04 - acc: 0.8766 - val_loss: 3.5310e-04 - val_acc: 0.8766\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 3.5593e-04 - acc: 0.8766 - val_loss: 3.5490e-04 - val_acc: 0.8766\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 3.5599e-04 - acc: 0.8766 - val_loss: 3.5277e-04 - val_acc: 0.8766\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 3.5646e-04 - acc: 0.8766 - val_loss: 3.5401e-04 - val_acc: 0.8766\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 3.5757e-04 - acc: 0.8766 - val_loss: 3.6308e-04 - val_acc: 0.8766\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 3.5766e-04 - acc: 0.8766 - val_loss: 3.5332e-04 - val_acc: 0.8766\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 3.5606e-04 - acc: 0.8766 - val_loss: 3.5525e-04 - val_acc: 0.8766\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 3.5538e-04 - acc: 0.8766 - val_loss: 3.5554e-04 - val_acc: 0.8766\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 3.5554e-04 - acc: 0.8766 - val_loss: 3.5329e-04 - val_acc: 0.8766\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 3.5681e-04 - acc: 0.8766 - val_loss: 3.8289e-04 - val_acc: 0.8766\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 3.5544e-04 - acc: 0.8766 - val_loss: 3.6253e-04 - val_acc: 0.8766\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 3.5602e-04 - acc: 0.8766 - val_loss: 3.5374e-04 - val_acc: 0.8766\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 3.5861e-04 - acc: 0.8766 - val_loss: 3.5527e-04 - val_acc: 0.8766\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 3.5552e-04 - acc: 0.8766 - val_loss: 3.5330e-04 - val_acc: 0.8766\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 3.5610e-04 - acc: 0.8766 - val_loss: 3.5347e-04 - val_acc: 0.8766\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 3.5561e-04 - acc: 0.8766 - val_loss: 3.6361e-04 - val_acc: 0.8766\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 3.5555e-04 - acc: 0.8766 - val_loss: 3.5676e-04 - val_acc: 0.8766\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 3.5641e-04 - acc: 0.8766 - val_loss: 3.5597e-04 - val_acc: 0.8766\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 3.5630e-04 - acc: 0.8766 - val_loss: 3.5283e-04 - val_acc: 0.8766\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 3.5479e-04 - acc: 0.8766 - val_loss: 3.5615e-04 - val_acc: 0.8766\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 3.5613e-04 - acc: 0.8766 - val_loss: 3.5225e-04 - val_acc: 0.8766\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e1351d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 546
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQ5v6HwLH_Wh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "002b470f-22aa-4372-cf58-8138ce020291"
      },
      "source": [
        "ypred = model.predict(X9)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y9)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 547,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.323307\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 547
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Up6_NVQoLi3n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "449d2f5d-1f33-4fee-abb3-4a70fa79f339"
      },
      "source": [
        "r2_score(y9,ypred)"
      ],
      "execution_count": 548,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10404726066784542"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 548
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3IOE5BCJRSK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "6b017826-8622-4b7f-e1be-875130534c81"
      },
      "source": [
        "ypred1=model.predict(X_test9)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test9)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 549,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.323307\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 549
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FDKRSVwLkoD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5328fb64-409e-4bbd-c79d-57f8d325375d"
      },
      "source": [
        "r2_score(y_test9,ypred1)"
      ],
      "execution_count": 550,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10404726066784542"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 550
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4HYV_dDbxDs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b460f649-3fc4-43d9-978f-64ddb2c8d1e4"
      },
      "source": [
        "mean_absolute_error(y9,ypred)"
      ],
      "execution_count": 551,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.006445981466698695"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 551
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WPfFs1Pbx7w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e43f458d-fce5-4ca2-b266-0f2e61d8bb7c"
      },
      "source": [
        "mean_absolute_error(y_test9,ypred1)"
      ],
      "execution_count": 552,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.006445981466698695"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 552
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4tac5LntOTz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1cfee21b-7c56-4447-f925-f9f208ccf69d"
      },
      "source": [
        "model.fit(X10, y10, validation_data=(X_test10,y_test10),epochs=50)"
      ],
      "execution_count": 553,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0027 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0027 - val_acc: 0.8375\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0026 - acc: 0.8375 - val_loss: 0.0026 - val_acc: 0.8375\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e135710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 553
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7iVtOzuIAff",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f5899170-ab74-40fe-f414-a577fdeafa6a"
      },
      "source": [
        "ypred = model.predict(X10)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y10)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 554,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.321255\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 554
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZzIyxgSKLcls",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2ef78eb8-cfd1-4035-e322-b779b8c3e24d"
      },
      "source": [
        "r2_score(y10,ypred)"
      ],
      "execution_count": 555,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09841319139574423"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 555
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vz0yUtDVJSqy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "e43d52fb-c0db-482f-e3e1-3a20de781715"
      },
      "source": [
        "ypred1=model.predict(X_test10)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test10)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 556,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.321255\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 556
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OvG7PKCLeB2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e021d391-f091-4561-ce3f-ad6fe4f2735e"
      },
      "source": [
        "r2_score(y_test10,ypred1)"
      ],
      "execution_count": 557,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09841319139574423"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 557
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9e2RJz20b4JB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9d6305d9-8fed-4db6-c10b-39245992443b"
      },
      "source": [
        "mean_absolute_error(y_test9,ypred1)"
      ],
      "execution_count": 558,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.011776980525917823"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 558
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSHbLk8Lb5Bo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "53594c8c-3e3a-4244-e408-59f01d55a8d0"
      },
      "source": [
        "mean_absolute_error(y9,ypred)"
      ],
      "execution_count": 559,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.011776980525917823"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 559
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBFxiG7GtOdT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "38ec3ea1-e3a1-42d3-d8eb-498c10c67a02"
      },
      "source": [
        "model.fit(X11, y11, validation_data=(X_test11,y_test11),epochs=50)"
      ],
      "execution_count": 560,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8450 - val_loss: 0.0027 - val_acc: 0.8450\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e135908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 560
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOytGCJYIByT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "74273550-47b0-4ddb-f621-2ea3f6242da9"
      },
      "source": [
        "ypred = model.predict(X11)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y11)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 561,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.313267\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 561
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iINdBIwoLTXh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f89339fb-6c2d-4390-8011-a943a8836232"
      },
      "source": [
        "r2_score(y11,ypred)"
      ],
      "execution_count": 562,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0945778583820549"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 562
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4WAXVRGRJUGH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f2521769-3ffe-410f-c742-b2a4524c78c1"
      },
      "source": [
        "ypred1=model.predict(X_test11)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test11)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 563,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.313267\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 563
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IteaW7lyLXic",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "211589dd-525f-4a6f-cc21-4f2f96d8d2d5"
      },
      "source": [
        "r2_score(y_test11,ypred1)"
      ],
      "execution_count": 564,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0945778583820549"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 564
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dR8eWYdab9x0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "990bbb0b-93df-4cf9-f05b-7d856ff05693"
      },
      "source": [
        "mean_absolute_error(y_test11,ypred1)"
      ],
      "execution_count": 565,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.018920249043578256"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 565
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQmn_nV5b_wQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7afbf352-ca39-4478-e36b-8431977fa052"
      },
      "source": [
        "mean_absolute_error(y11,ypred)"
      ],
      "execution_count": 566,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.018920249043578256"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 566
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uin8xQNXB74p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "61eec2da-9627-4898-b076-a930d9b43511"
      },
      "source": [
        "model.fit(X12, y12, validation_data=(X_test12,y_test12),epochs=50)"
      ],
      "execution_count": 567,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0017 - acc: 0.8385 - val_loss: 0.0017 - val_acc: 0.8385\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e135a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 567
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdlcVaWfIDEI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f1cbbd8b-feee-4815-b623-bb6a31df55d5"
      },
      "source": [
        "ypred = model.predict(X12)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y12)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 568,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.366368\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 568
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcIK1vV7LH0l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5db4279b-74b0-46bf-97e6-7ce9d9869dfb"
      },
      "source": [
        "r2_score(y12,ypred)"
      ],
      "execution_count": 569,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13409576795703992"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 569
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1dAhjw5JWAB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "e5590e25-520d-4522-b536-c00ea07fe319"
      },
      "source": [
        "ypred1=model.predict(X_test12)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test12)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 570,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.366368\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 570
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUu9HNNALKXT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "88e12f49-d67e-4dfa-912b-eb2c81989e86"
      },
      "source": [
        "r2_score(y_test12,ypred1)"
      ],
      "execution_count": 571,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13409576795703992"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 571
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2tbAJ9kcHAn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d30e42a7-eeb3-4e70-a27d-08e0a7e90594"
      },
      "source": [
        "mean_absolute_error(y_test12,ypred1)"
      ],
      "execution_count": 572,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.016133867823599986"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 572
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mH7-ROQ-cJPL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "695e9966-0b3c-4da4-e16a-da9ce802704f"
      },
      "source": [
        "\n",
        "\n",
        "mean_absolute_error(y12,ypred)"
      ],
      "execution_count": 573,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.016133867823599986"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 573
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q24UogQuB8LN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b6043dbc-aece-4220-e569-ab6d49b4e554"
      },
      "source": [
        "model.fit(X13, y13, validation_data=(X_test13,y_test13),epochs=50)"
      ],
      "execution_count": 574,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 229us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0026 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0026 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0026 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0026 - acc: 0.8430 - val_loss: 0.0027 - val_acc: 0.8430\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0027 - acc: 0.8430 - val_loss: 0.0026 - val_acc: 0.8430\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e039630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 574
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhNbhU3fIE3L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "1decbe1a-07ee-443b-8a6a-7f9b339e77ce"
      },
      "source": [
        "ypred = model.predict(X13)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y13)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 575,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.322495\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 575
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDtK58AIK8cY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "52a34193-d87d-448a-9159-6db8780b1e2a"
      },
      "source": [
        "r2_score(y13,ypred)"
      ],
      "execution_count": 576,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10080423346816669"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 576
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aRT60eIMJXc9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "b97954db-6147-4414-ed2f-55c80e11779f"
      },
      "source": [
        "ypred1=model.predict(X_test13)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test13)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 577,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.322495\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 577
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xh4_heUK-xf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eb15f7df-8cae-4145-b491-080210185a0a"
      },
      "source": [
        "r2_score(y_test13,ypred1)"
      ],
      "execution_count": 578,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10080423346816669"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 578
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFzCJDbpcMx8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "064cb87d-d711-40ec-a391-ddbb579c0a74"
      },
      "source": [
        "mean_absolute_error(y_test13,ypred1)"
      ],
      "execution_count": 579,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02290264249860427"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 579
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O41sYTw2cO27",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c40e670c-b722-4d69-fcbd-97c49959a5b0"
      },
      "source": [
        "mean_absolute_error(y13,ypred)"
      ],
      "execution_count": 580,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02290264249860427"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 580
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOiZXqKJB8jC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f9a1073b-cb18-48f8-b6c5-1752da686f2b"
      },
      "source": [
        "model.fit(X14, y14, validation_data=(X_test14,y_test14),epochs=50)"
      ],
      "execution_count": 581,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 228us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 228us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 228us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 228us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0024 - val_acc: 0.8338\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0024 - val_acc: 0.8338\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0024 - val_acc: 0.8338\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0024 - val_acc: 0.8338\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0024 - val_acc: 0.8338\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0024 - val_acc: 0.8338\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0024 - val_acc: 0.8338\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0024 - val_acc: 0.8338\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0025 - acc: 0.8338 - val_loss: 0.0025 - val_acc: 0.8338\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e039860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 581
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Ky-EMUEIGq6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "56932da9-c8d8-4ef2-8ef1-2dd3d241005d"
      },
      "source": [
        "ypred = model.predict(X14)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y14)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 582,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.2985\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 582
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kow20CjpKwRA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "73b5174e-bcb7-4dec-db46-435f4332fbb4"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y14,ypred)"
      ],
      "execution_count": 583,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08395486665665652"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 583
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YoTtOaYJY6P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "ff707f7f-33cc-4d58-d042-8d43a880102d"
      },
      "source": [
        "ypred1=model.predict(X_test14)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test14)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 584,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.2985\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 584
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esTMyuJ3Kzmj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "318156d2-3ad5-4dcb-e97c-e96c944a1e3a"
      },
      "source": [
        "r2_score(y_test14,ypred1)"
      ],
      "execution_count": 585,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08395486665665652"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 585
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qVcX82RcWAs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "aaadbd1e-f62f-4cab-c326-0798b6e31b19"
      },
      "source": [
        "mean_absolute_error(y_test14,ypred1)"
      ],
      "execution_count": 586,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.017544800950225283"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 586
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFHNwtqAcX1O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3f57e8fb-9f70-41cb-eb7d-f0f65fd8b1e8"
      },
      "source": [
        "mean_absolute_error(y14,ypred)"
      ],
      "execution_count": 587,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.017544800950225283"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 587
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31sa5rlXB9BA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2ce0b057-3e1a-46e9-c565-46afdbeb3a3a"
      },
      "source": [
        "model.fit(X15, y15, validation_data=(X_test15,y_test15),epochs=50)"
      ],
      "execution_count": 588,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0031 - val_acc: 0.8192\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0031 - val_acc: 0.8192\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0031 - val_acc: 0.8192\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 229us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0031 - val_acc: 0.8192\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0031 - val_acc: 0.8192\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0031 - val_acc: 0.8192\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0031 - val_acc: 0.8192\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 229us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0031 - val_acc: 0.8192\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0031 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 230us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0031 - val_acc: 0.8192\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0030 - acc: 0.8192 - val_loss: 0.0030 - val_acc: 0.8192\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e0edd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 588
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x783mmAYIIMe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c3050fb2-0189-4b93-b25a-a96d84d4f847"
      },
      "source": [
        "ypred = model.predict(X15)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y15)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 589,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.267316\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 589
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHuQ0617KncF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fc4e196e-dcc0-4455-e90d-24976eb52347"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y15,ypred)"
      ],
      "execution_count": 590,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07099232636588804"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 590
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7dQRGRnJb-H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "ff9a1b27-03d8-46c3-d946-42a13e76224b"
      },
      "source": [
        "ypred1=model.predict(X_test15)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test15)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 591,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.267316\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 591
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MsXJBffKsBU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6a447a48-fab9-4763-f78a-b906579fd213"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test15,ypred1)"
      ],
      "execution_count": 592,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07099232636588804"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 592
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqpc6rGJccsh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ee58d810-edbf-4325-d6ec-78b4e12d8d74"
      },
      "source": [
        "mean_absolute_error(y_test15,ypred1)"
      ],
      "execution_count": 593,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.022643189333733486"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 593
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBuDDwkTce4X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "efb7a482-509f-4caa-c051-c29984a76a6a"
      },
      "source": [
        "mean_absolute_error(y15,ypred)"
      ],
      "execution_count": 594,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.022643189333733486"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 594
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddVXEmYRB9TB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4ca5232d-a06b-4e3e-c024-5233959a838d"
      },
      "source": [
        "model.fit(X16, y16, validation_data=(X_test16,y_test16),epochs=100)"
      ],
      "execution_count": 595,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/100\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 2/100\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 3/100\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 4/100\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 5/100\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 6/100\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 7/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 8/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 9/100\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 10/100\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 11/100\n",
            "11997/11997 [==============================] - 3s 231us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 12/100\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 13/100\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 14/100\n",
            "11997/11997 [==============================] - 3s 254us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 15/100\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 16/100\n",
            "11997/11997 [==============================] - 3s 262us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 17/100\n",
            "11997/11997 [==============================] - 3s 254us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 18/100\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 19/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 20/100\n",
            "11997/11997 [==============================] - 3s 232us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 21/100\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 22/100\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 23/100\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 24/100\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 25/100\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 26/100\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 27/100\n",
            "11997/11997 [==============================] - 3s 233us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 28/100\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 29/100\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 30/100\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 31/100\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 32/100\n",
            "11997/11997 [==============================] - 3s 236us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 33/100\n",
            "11997/11997 [==============================] - 3s 234us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 34/100\n",
            "11997/11997 [==============================] - 3s 235us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 35/100\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 36/100\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 37/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 38/100\n",
            "11997/11997 [==============================] - 3s 238us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 39/100\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0025 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 40/100\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 41/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 42/100\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 43/100\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 44/100\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 45/100\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 46/100\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 47/100\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 48/100\n",
            "11997/11997 [==============================] - 3s 237us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 49/100\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 50/100\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 51/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 52/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 53/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 54/100\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 55/100\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 56/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 57/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 58/100\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 59/100\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 60/100\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 61/100\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 62/100\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 63/100\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 64/100\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 65/100\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 66/100\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 67/100\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 68/100\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 69/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 70/100\n",
            "11997/11997 [==============================] - 3s 239us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 71/100\n",
            "11997/11997 [==============================] - 3s 240us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 72/100\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 73/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 74/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 75/100\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 76/100\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 77/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 78/100\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 79/100\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 80/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 81/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 82/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 83/100\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 84/100\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 85/100\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 86/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 87/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 88/100\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 89/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 90/100\n",
            "11997/11997 [==============================] - 3s 242us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 91/100\n",
            "11997/11997 [==============================] - 3s 241us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 92/100\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 93/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 94/100\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 95/100\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0025 - val_acc: 0.8464\n",
            "Epoch 96/100\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 97/100\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 98/100\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 99/100\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n",
            "Epoch 100/100\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 0.0024 - acc: 0.8464 - val_loss: 0.0024 - val_acc: 0.8464\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3de85320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 595
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBzu8k-rIJfj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "e354ab0d-a5db-4237-e6fa-c69f3a88ca0f"
      },
      "source": [
        "ypred = model.predict(X16)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y16)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 596,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.294606\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 596
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oa8m9LhKKdqO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "81e06e2a-f3f6-434c-a965-7d43c985e1b6"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y16,ypred)"
      ],
      "execution_count": 597,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.086262418311012"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 597
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCjU5wnqJdhv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "faabc6c2-fcd5-4999-a135-0bc49d89b85a"
      },
      "source": [
        "ypred1=model.predict(X_test16)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test16)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 598,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.294606\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 598
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UY6rtmdyKjSa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ca9558bc-9305-4de6-8f73-ac4967cdd2fa"
      },
      "source": [
        "r2_score(y_test16,ypred1)"
      ],
      "execution_count": 599,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.086262418311012"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 599
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U9Ef0FuxciMB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cfe612fd-a8ca-4d1f-957c-a0ddb8dbea15"
      },
      "source": [
        "mean_absolute_error(y_test16,ypred1)"
      ],
      "execution_count": 600,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.020578645048889434"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 600
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82kqJKkTcj7i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "94dc978d-c827-4c13-84c9-72d427a57480"
      },
      "source": [
        "mean_absolute_error(y16,ypred)"
      ],
      "execution_count": 601,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.020578645048889434"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 601
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIErxCqcB9uC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8fa762b1-778e-4bcc-b224-79e4f2174442"
      },
      "source": [
        "model.fit(X17, y17, validation_data=(X_test17,y_test17),epochs=50)"
      ],
      "execution_count": 602,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 2/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 3/50\n",
            "11997/11997 [==============================] - 3s 246us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 4/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 5/50\n",
            "11997/11997 [==============================] - 3s 243us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 6/50\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 7/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 8/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 9/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 10/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 11/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 12/50\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 13/50\n",
            "11997/11997 [==============================] - 3s 262us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 14/50\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 15/50\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 16/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 17/50\n",
            "11997/11997 [==============================] - 3s 244us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 18/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 19/50\n",
            "11997/11997 [==============================] - 3s 247us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 20/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 21/50\n",
            "11997/11997 [==============================] - 3s 245us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 22/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 23/50\n",
            "11997/11997 [==============================] - 3s 248us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 24/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 25/50\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 26/50\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 27/50\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 28/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 29/50\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 30/50\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 31/50\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 32/50\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 33/50\n",
            "11997/11997 [==============================] - 3s 263us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 34/50\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 35/50\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 36/50\n",
            "11997/11997 [==============================] - 3s 261us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 37/50\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 38/50\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 39/50\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 40/50\n",
            "11997/11997 [==============================] - 3s 261us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 41/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 42/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0025 - val_acc: 0.8949\n",
            "Epoch 43/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 44/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 45/50\n",
            "11997/11997 [==============================] - 3s 254us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 46/50\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 47/50\n",
            "11997/11997 [==============================] - 3s 250us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 48/50\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 49/50\n",
            "11997/11997 [==============================] - 3s 262us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n",
            "Epoch 50/50\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 0.0025 - acc: 0.8949 - val_loss: 0.0024 - val_acc: 0.8949\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3de85358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 602
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_cqFEZyIK_5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "e52b7a04-0ffa-4321-a09d-c9608ebe1171"
      },
      "source": [
        "ypred = model.predict(X17)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y17)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 603,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.302593\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 603
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNUMrec5KInu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "91c6801e-83d6-4871-cf09-433b1ee80e1c"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y17,ypred)"
      ],
      "execution_count": 604,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08791346035194758"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 604
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJ7Wc3CIJerO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "47834132-8f63-44a3-ecea-ada3011d6505"
      },
      "source": [
        "ypred1=model.predict(X_test17)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test17)\n",
        "s1.corrwith(s,axis=0)"
      ],
      "execution_count": 605,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.302593\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 605
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKYoL8cDKKmZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e8658ec1-4fde-492e-96f8-eb827c474950"
      },
      "source": [
        "r2_score(y_test17,ypred1)"
      ],
      "execution_count": 606,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08791346035194758"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 606
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqMbanyDcoVD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "df1117d7-bfa0-4c25-dea3-4ba578323a87"
      },
      "source": [
        "mean_absolute_error(y_test17,ypred1)"
      ],
      "execution_count": 607,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.019650643640597563"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 607
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4z--E3OPcqLB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f5c6a47c-5f11-42c4-b1ac-05f8d6e25bd7"
      },
      "source": [
        "mean_absolute_error(y17,ypred)"
      ],
      "execution_count": 608,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.019650643640597563"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 608
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnBS9J1vB-JJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "98b47213-9c2c-4908-9dea-711107c8f941"
      },
      "source": [
        "model.fit(X18, y18, validation_data=(X_test18,y_test18),epochs=100)"
      ],
      "execution_count": 609,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 11997 samples, validate on 11997 samples\n",
            "Epoch 1/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.7000e-04 - acc: 0.8540 - val_loss: 1.6249e-04 - val_acc: 0.8540\n",
            "Epoch 2/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.6134e-04 - acc: 0.8540 - val_loss: 1.5857e-04 - val_acc: 0.8540\n",
            "Epoch 3/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5808e-04 - acc: 0.8540 - val_loss: 1.5639e-04 - val_acc: 0.8540\n",
            "Epoch 4/100\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 1.5663e-04 - acc: 0.8540 - val_loss: 1.5549e-04 - val_acc: 0.8540\n",
            "Epoch 5/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5613e-04 - acc: 0.8540 - val_loss: 1.5527e-04 - val_acc: 0.8540\n",
            "Epoch 6/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5563e-04 - acc: 0.8540 - val_loss: 1.5454e-04 - val_acc: 0.8540\n",
            "Epoch 7/100\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 1.5547e-04 - acc: 0.8540 - val_loss: 1.5430e-04 - val_acc: 0.8540\n",
            "Epoch 8/100\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 1.5512e-04 - acc: 0.8540 - val_loss: 1.5420e-04 - val_acc: 0.8540\n",
            "Epoch 9/100\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 1.5532e-04 - acc: 0.8540 - val_loss: 1.5432e-04 - val_acc: 0.8540\n",
            "Epoch 10/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5533e-04 - acc: 0.8540 - val_loss: 1.5478e-04 - val_acc: 0.8540\n",
            "Epoch 11/100\n",
            "11997/11997 [==============================] - 3s 249us/sample - loss: 1.5562e-04 - acc: 0.8540 - val_loss: 1.5675e-04 - val_acc: 0.8540\n",
            "Epoch 12/100\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 1.5507e-04 - acc: 0.8540 - val_loss: 1.5474e-04 - val_acc: 0.8540\n",
            "Epoch 13/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.5544e-04 - acc: 0.8540 - val_loss: 1.5407e-04 - val_acc: 0.8540\n",
            "Epoch 14/100\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 1.5517e-04 - acc: 0.8540 - val_loss: 1.5463e-04 - val_acc: 0.8540\n",
            "Epoch 15/100\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 1.5510e-04 - acc: 0.8540 - val_loss: 1.5401e-04 - val_acc: 0.8540\n",
            "Epoch 16/100\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 1.5527e-04 - acc: 0.8540 - val_loss: 1.6044e-04 - val_acc: 0.8540\n",
            "Epoch 17/100\n",
            "11997/11997 [==============================] - 3s 254us/sample - loss: 1.5604e-04 - acc: 0.8540 - val_loss: 1.5419e-04 - val_acc: 0.8540\n",
            "Epoch 18/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.5523e-04 - acc: 0.8540 - val_loss: 1.5472e-04 - val_acc: 0.8540\n",
            "Epoch 19/100\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 1.5518e-04 - acc: 0.8540 - val_loss: 1.5409e-04 - val_acc: 0.8540\n",
            "Epoch 20/100\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 1.5510e-04 - acc: 0.8540 - val_loss: 1.5474e-04 - val_acc: 0.8540\n",
            "Epoch 21/100\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 1.5546e-04 - acc: 0.8540 - val_loss: 1.5439e-04 - val_acc: 0.8540\n",
            "Epoch 22/100\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 1.5509e-04 - acc: 0.8540 - val_loss: 1.5466e-04 - val_acc: 0.8540\n",
            "Epoch 23/100\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 1.5552e-04 - acc: 0.8540 - val_loss: 1.5613e-04 - val_acc: 0.8540\n",
            "Epoch 24/100\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 1.5524e-04 - acc: 0.8540 - val_loss: 1.5490e-04 - val_acc: 0.8540\n",
            "Epoch 25/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.5520e-04 - acc: 0.8540 - val_loss: 1.5485e-04 - val_acc: 0.8540\n",
            "Epoch 26/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5530e-04 - acc: 0.8540 - val_loss: 1.5369e-04 - val_acc: 0.8540\n",
            "Epoch 27/100\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 1.5506e-04 - acc: 0.8540 - val_loss: 1.5376e-04 - val_acc: 0.8540\n",
            "Epoch 28/100\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 1.5518e-04 - acc: 0.8540 - val_loss: 1.5367e-04 - val_acc: 0.8540\n",
            "Epoch 29/100\n",
            "11997/11997 [==============================] - 3s 251us/sample - loss: 1.5547e-04 - acc: 0.8540 - val_loss: 1.5436e-04 - val_acc: 0.8540\n",
            "Epoch 30/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5459e-04 - acc: 0.8540 - val_loss: 1.6006e-04 - val_acc: 0.8540\n",
            "Epoch 31/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5597e-04 - acc: 0.8540 - val_loss: 1.5423e-04 - val_acc: 0.8540\n",
            "Epoch 32/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5461e-04 - acc: 0.8540 - val_loss: 1.5467e-04 - val_acc: 0.8540\n",
            "Epoch 33/100\n",
            "11997/11997 [==============================] - 3s 253us/sample - loss: 1.5593e-04 - acc: 0.8540 - val_loss: 1.5424e-04 - val_acc: 0.8540\n",
            "Epoch 34/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.5463e-04 - acc: 0.8540 - val_loss: 1.5378e-04 - val_acc: 0.8540\n",
            "Epoch 35/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5484e-04 - acc: 0.8540 - val_loss: 1.5355e-04 - val_acc: 0.8540\n",
            "Epoch 36/100\n",
            "11997/11997 [==============================] - 3s 254us/sample - loss: 1.5475e-04 - acc: 0.8540 - val_loss: 1.5375e-04 - val_acc: 0.8540\n",
            "Epoch 37/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5495e-04 - acc: 0.8540 - val_loss: 1.5445e-04 - val_acc: 0.8540\n",
            "Epoch 38/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5527e-04 - acc: 0.8540 - val_loss: 1.5459e-04 - val_acc: 0.8540\n",
            "Epoch 39/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.5510e-04 - acc: 0.8540 - val_loss: 1.5669e-04 - val_acc: 0.8540\n",
            "Epoch 40/100\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 1.5523e-04 - acc: 0.8540 - val_loss: 1.5546e-04 - val_acc: 0.8540\n",
            "Epoch 41/100\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 1.5490e-04 - acc: 0.8540 - val_loss: 1.5479e-04 - val_acc: 0.8540\n",
            "Epoch 42/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.5472e-04 - acc: 0.8540 - val_loss: 1.5373e-04 - val_acc: 0.8540\n",
            "Epoch 43/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.5481e-04 - acc: 0.8540 - val_loss: 1.5407e-04 - val_acc: 0.8540\n",
            "Epoch 44/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5462e-04 - acc: 0.8540 - val_loss: 1.5447e-04 - val_acc: 0.8540\n",
            "Epoch 45/100\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 1.5506e-04 - acc: 0.8540 - val_loss: 1.5414e-04 - val_acc: 0.8540\n",
            "Epoch 46/100\n",
            "11997/11997 [==============================] - 3s 262us/sample - loss: 1.5552e-04 - acc: 0.8540 - val_loss: 1.5428e-04 - val_acc: 0.8540\n",
            "Epoch 47/100\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 1.5500e-04 - acc: 0.8540 - val_loss: 1.5409e-04 - val_acc: 0.8540\n",
            "Epoch 48/100\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 1.5516e-04 - acc: 0.8540 - val_loss: 1.5377e-04 - val_acc: 0.8540\n",
            "Epoch 49/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5504e-04 - acc: 0.8540 - val_loss: 1.5339e-04 - val_acc: 0.8540\n",
            "Epoch 50/100\n",
            "11997/11997 [==============================] - 3s 262us/sample - loss: 1.5496e-04 - acc: 0.8540 - val_loss: 1.5411e-04 - val_acc: 0.8540\n",
            "Epoch 51/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5577e-04 - acc: 0.8540 - val_loss: 1.5381e-04 - val_acc: 0.8540\n",
            "Epoch 52/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5491e-04 - acc: 0.8540 - val_loss: 1.5344e-04 - val_acc: 0.8540\n",
            "Epoch 53/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5551e-04 - acc: 0.8540 - val_loss: 1.5381e-04 - val_acc: 0.8540\n",
            "Epoch 54/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5533e-04 - acc: 0.8540 - val_loss: 1.5372e-04 - val_acc: 0.8540\n",
            "Epoch 55/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5479e-04 - acc: 0.8540 - val_loss: 1.5338e-04 - val_acc: 0.8540\n",
            "Epoch 56/100\n",
            "11997/11997 [==============================] - 3s 252us/sample - loss: 1.5584e-04 - acc: 0.8540 - val_loss: 1.5344e-04 - val_acc: 0.8540\n",
            "Epoch 57/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5431e-04 - acc: 0.8540 - val_loss: 1.5380e-04 - val_acc: 0.8540\n",
            "Epoch 58/100\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 1.5545e-04 - acc: 0.8540 - val_loss: 1.5376e-04 - val_acc: 0.8540\n",
            "Epoch 59/100\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 1.5489e-04 - acc: 0.8540 - val_loss: 1.5371e-04 - val_acc: 0.8540\n",
            "Epoch 60/100\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 1.5452e-04 - acc: 0.8540 - val_loss: 1.7564e-04 - val_acc: 0.8540\n",
            "Epoch 61/100\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 1.5513e-04 - acc: 0.8540 - val_loss: 1.5418e-04 - val_acc: 0.8540\n",
            "Epoch 62/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5502e-04 - acc: 0.8540 - val_loss: 1.5398e-04 - val_acc: 0.8540\n",
            "Epoch 63/100\n",
            "11997/11997 [==============================] - 3s 265us/sample - loss: 1.5504e-04 - acc: 0.8540 - val_loss: 1.5527e-04 - val_acc: 0.8540\n",
            "Epoch 64/100\n",
            "11997/11997 [==============================] - 3s 273us/sample - loss: 1.5546e-04 - acc: 0.8540 - val_loss: 1.5785e-04 - val_acc: 0.8540\n",
            "Epoch 65/100\n",
            "11997/11997 [==============================] - 3s 271us/sample - loss: 1.5536e-04 - acc: 0.8540 - val_loss: 1.5343e-04 - val_acc: 0.8540\n",
            "Epoch 66/100\n",
            "11997/11997 [==============================] - 3s 265us/sample - loss: 1.5477e-04 - acc: 0.8540 - val_loss: 1.5459e-04 - val_acc: 0.8540\n",
            "Epoch 67/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5509e-04 - acc: 0.8540 - val_loss: 1.5400e-04 - val_acc: 0.8540\n",
            "Epoch 68/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5497e-04 - acc: 0.8540 - val_loss: 1.5365e-04 - val_acc: 0.8540\n",
            "Epoch 69/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5461e-04 - acc: 0.8540 - val_loss: 1.5357e-04 - val_acc: 0.8540\n",
            "Epoch 70/100\n",
            "11997/11997 [==============================] - 3s 254us/sample - loss: 1.5484e-04 - acc: 0.8540 - val_loss: 1.5386e-04 - val_acc: 0.8540\n",
            "Epoch 71/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5490e-04 - acc: 0.8540 - val_loss: 1.5327e-04 - val_acc: 0.8540\n",
            "Epoch 72/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.5480e-04 - acc: 0.8540 - val_loss: 1.5378e-04 - val_acc: 0.8540\n",
            "Epoch 73/100\n",
            "11997/11997 [==============================] - 3s 255us/sample - loss: 1.5447e-04 - acc: 0.8540 - val_loss: 1.5403e-04 - val_acc: 0.8540\n",
            "Epoch 74/100\n",
            "11997/11997 [==============================] - 3s 262us/sample - loss: 1.5466e-04 - acc: 0.8540 - val_loss: 1.5398e-04 - val_acc: 0.8540\n",
            "Epoch 75/100\n",
            "11997/11997 [==============================] - 3s 262us/sample - loss: 1.5521e-04 - acc: 0.8540 - val_loss: 1.5345e-04 - val_acc: 0.8540\n",
            "Epoch 76/100\n",
            "11997/11997 [==============================] - 3s 264us/sample - loss: 1.5438e-04 - acc: 0.8540 - val_loss: 1.5337e-04 - val_acc: 0.8540\n",
            "Epoch 77/100\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 1.5506e-04 - acc: 0.8540 - val_loss: 1.5482e-04 - val_acc: 0.8540\n",
            "Epoch 78/100\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 1.5438e-04 - acc: 0.8540 - val_loss: 1.5344e-04 - val_acc: 0.8540\n",
            "Epoch 79/100\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 1.5456e-04 - acc: 0.8540 - val_loss: 1.5324e-04 - val_acc: 0.8540\n",
            "Epoch 80/100\n",
            "11997/11997 [==============================] - 3s 266us/sample - loss: 1.5484e-04 - acc: 0.8540 - val_loss: 1.5469e-04 - val_acc: 0.8540\n",
            "Epoch 81/100\n",
            "11997/11997 [==============================] - 3s 261us/sample - loss: 1.5491e-04 - acc: 0.8540 - val_loss: 1.5356e-04 - val_acc: 0.8540\n",
            "Epoch 82/100\n",
            "11997/11997 [==============================] - 3s 257us/sample - loss: 1.5478e-04 - acc: 0.8540 - val_loss: 1.5317e-04 - val_acc: 0.8540\n",
            "Epoch 83/100\n",
            "11997/11997 [==============================] - 3s 263us/sample - loss: 1.5463e-04 - acc: 0.8540 - val_loss: 1.5393e-04 - val_acc: 0.8540\n",
            "Epoch 84/100\n",
            "11997/11997 [==============================] - 3s 264us/sample - loss: 1.5455e-04 - acc: 0.8540 - val_loss: 1.5419e-04 - val_acc: 0.8540\n",
            "Epoch 85/100\n",
            "11997/11997 [==============================] - 3s 266us/sample - loss: 1.5451e-04 - acc: 0.8540 - val_loss: 1.5804e-04 - val_acc: 0.8540\n",
            "Epoch 86/100\n",
            "11997/11997 [==============================] - 3s 263us/sample - loss: 1.5472e-04 - acc: 0.8540 - val_loss: 1.5345e-04 - val_acc: 0.8540\n",
            "Epoch 87/100\n",
            "11997/11997 [==============================] - 3s 261us/sample - loss: 1.5441e-04 - acc: 0.8540 - val_loss: 1.5329e-04 - val_acc: 0.8540\n",
            "Epoch 88/100\n",
            "11997/11997 [==============================] - 3s 262us/sample - loss: 1.5475e-04 - acc: 0.8540 - val_loss: 1.5353e-04 - val_acc: 0.8540\n",
            "Epoch 89/100\n",
            "11997/11997 [==============================] - 3s 261us/sample - loss: 1.5485e-04 - acc: 0.8540 - val_loss: 1.5317e-04 - val_acc: 0.8540\n",
            "Epoch 90/100\n",
            "11997/11997 [==============================] - 3s 262us/sample - loss: 1.5479e-04 - acc: 0.8540 - val_loss: 1.5500e-04 - val_acc: 0.8540\n",
            "Epoch 91/100\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 1.5473e-04 - acc: 0.8540 - val_loss: 1.5744e-04 - val_acc: 0.8540\n",
            "Epoch 92/100\n",
            "11997/11997 [==============================] - 3s 256us/sample - loss: 1.5491e-04 - acc: 0.8540 - val_loss: 1.5383e-04 - val_acc: 0.8540\n",
            "Epoch 93/100\n",
            "11997/11997 [==============================] - 3s 261us/sample - loss: 1.5454e-04 - acc: 0.8540 - val_loss: 1.5534e-04 - val_acc: 0.8540\n",
            "Epoch 94/100\n",
            "11997/11997 [==============================] - 3s 258us/sample - loss: 1.5487e-04 - acc: 0.8540 - val_loss: 1.5589e-04 - val_acc: 0.8540\n",
            "Epoch 95/100\n",
            "11997/11997 [==============================] - 3s 259us/sample - loss: 1.5460e-04 - acc: 0.8540 - val_loss: 1.5374e-04 - val_acc: 0.8540\n",
            "Epoch 96/100\n",
            "11997/11997 [==============================] - 3s 260us/sample - loss: 1.5442e-04 - acc: 0.8540 - val_loss: 1.5336e-04 - val_acc: 0.8540\n",
            "Epoch 97/100\n",
            "11997/11997 [==============================] - 3s 274us/sample - loss: 1.5471e-04 - acc: 0.8540 - val_loss: 1.5350e-04 - val_acc: 0.8540\n",
            "Epoch 98/100\n",
            "11997/11997 [==============================] - 3s 268us/sample - loss: 1.5486e-04 - acc: 0.8540 - val_loss: 1.5412e-04 - val_acc: 0.8540\n",
            "Epoch 99/100\n",
            "11997/11997 [==============================] - 3s 263us/sample - loss: 1.5450e-04 - acc: 0.8540 - val_loss: 1.5344e-04 - val_acc: 0.8540\n",
            "Epoch 100/100\n",
            "11997/11997 [==============================] - 3s 265us/sample - loss: 1.5423e-04 - acc: 0.8540 - val_loss: 1.5367e-04 - val_acc: 0.8540\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f9a3e039e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 609
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4dm66DTdIMJ8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "31a219ba-4fca-4f00-d5b7-7808e1d9919d"
      },
      "source": [
        "ypred = model.predict(X18)\n",
        "s=pd.DataFrame(ypred)\n",
        "s1=pd.DataFrame(y18)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 610,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.1813\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 610
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVgkRVW42TsQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b5918e04-6fa8-45de-cf80-513bd33a1813"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y18,ypred)"
      ],
      "execution_count": 611,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.031059897715727813"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 611
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtcP5oF9J_Ae",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "697ebadf-a654-425f-b715-e056b2a229c2"
      },
      "source": [
        "ypred1 = model.predict(X_test18)\n",
        "s=pd.DataFrame(ypred1)\n",
        "s1=pd.DataFrame(y_test18)\n",
        "s1.corrwith(s, axis = 0) "
      ],
      "execution_count": 612,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.1813\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 612
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUlUlZnL2tki",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3a30f354-c985-40c8-c7e5-fb88e6a42381"
      },
      "source": [
        "r2_score(y_test18,ypred1)"
      ],
      "execution_count": 613,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.031059897715727813"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 613
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClsczOxcct6d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ede98025-b80e-45e3-8218-dcae19c9a218"
      },
      "source": [
        "mean_absolute_error(y_test18,ypred1)"
      ],
      "execution_count": 614,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003054207639441114"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 614
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHM1dOQ3cx-y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9abf4b50-4df1-4123-f69c-3eb193318e46"
      },
      "source": [
        "mean_absolute_error(y18,ypred)"
      ],
      "execution_count": 615,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003054207639441114"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 615
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFJr0ZVqgowL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}