MLR2f15.ipynb

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    "colab": {
      "name": "MLR2f15.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNLJ/ZQ72S1jTuB/BCGWHEU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "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/MLR2f15.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",
        "outputId": "05a324d7-d6cb-4df1-c55d-4a10d1faef63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 62
        }
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          "metadata": {
            "tags": []
          }
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    {
      "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 matplotlib import pyplot as plt\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": "13aa9b4f-4286-46d4-f3ad-b092ce878fd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "  </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",
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              "    <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",
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            ],
            "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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RB9D0KOIkl-g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cuddalore=df[3:19360]\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": "z7bqZet_0j0n",
        "colab_type": "code",
        "outputId": "04fa1acc-1593-44de-ff3f-c938d92545b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "Pennagaram.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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>100460</th>\n",
              "      <td>1975-01-03 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100461</th>\n",
              "      <td>1975-01-04 00:00:00</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>100462</th>\n",
              "      <td>1975-01-05 00:00:00</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>100463</th>\n",
              "      <td>1975-01-06 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100464</th>\n",
              "      <td>1975-01-07 00:00:00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Unnamed: 0  Unnamed: 1\n",
              "100460  1975-01-03 00:00:00         0.0\n",
              "100461  1975-01-04 00:00:00         0.0\n",
              "100462  1975-01-05 00:00:00         0.0\n",
              "100463  1975-01-06 00:00:00         0.0\n",
              "100464  1975-01-07 00:00:00         0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "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[0:15000]\n",
        "test=Cuddalore[15000:19004]\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((15000, 1))\n",
        "in_seqtest1 = in_seqtest1.reshape((4004, 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 = 2\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": "wChOw-PbTin3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test,y_test = split_sequence(cuddataset1, n_steps)\n",
        "X_test1,y_test1 = split_sequence(pandataset1, n_steps)\n",
        "X_test2,y_test2= split_sequence(dhadataset1, n_steps)\n",
        "X_test3,y_test3 = split_sequence(hardataset1, n_steps)\n",
        "X_test4,y_test4 = split_sequence(paldataset1, n_steps)\n",
        "X_test5,y_test5 = split_sequence(pendataset1, n_steps)\n",
        "X_test6,y_test6 = split_sequence(chedataset1, n_steps)\n",
        "X_test7,y_test7 = split_sequence(satdataset1, n_steps)\n",
        "X_test8,y_test8 = split_sequence(Tirdataset1, n_steps)\n",
        "X_test9,y_test9 = split_sequence(Sandataset1, n_steps)\n",
        "X_test10,y_test10 = split_sequence(Tirudataset1, n_steps)\n",
        "X_test11,y_test11= split_sequence(uludataset1, n_steps)\n",
        "X_test12,y_test12 = split_sequence(vildataset1, n_steps)\n",
        "X_test13,y_test13 = split_sequence(bardataset1, n_steps)\n",
        "X_test14,y_test14 = split_sequence(Dendataset1, n_steps)\n",
        "X_test15,y_test15 = split_sequence(kridataset1, n_steps)\n",
        "X_test16,y_test16 = split_sequence(mardataset1, n_steps)\n",
        "X_test17,y_test17= split_sequence(raydataset1, n_steps)\n",
        "X_test18,y_test18= split_sequence(uthdataset1, n_steps)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e32xKtsRkTLS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_test=np.nan_to_num(y_test)\n",
        "y_test1=np.nan_to_num(y_test1)\n",
        "y_test2=np.nan_to_num(y_test2)\n",
        "y_test3=np.nan_to_num(y_test3)\n",
        "\n",
        "y_test4=np.nan_to_num(y_test4)\n",
        "y_test5=np.nan_to_num(y_test5)\n",
        "y_test6=np.nan_to_num(y_test6)\n",
        "y_test7=np.nan_to_num(y_test7)\n",
        "y_test8=np.nan_to_num(y_test8)\n",
        "y_test9=np.nan_to_num(y_test9)\n",
        "y_test10=np.nan_to_num(y_test10)\n",
        "y_test11=np.nan_to_num(y_test11)\n",
        "y_test12=np.nan_to_num(y_test12)\n",
        "y_test13=np.nan_to_num(y_test13)\n",
        "y_test14=np.nan_to_num(y_test14)\n",
        "y_test15=np.nan_to_num(y_test15)\n",
        "y_test16=np.nan_to_num(y_test16)\n",
        "y_test17=np.nan_to_num(y_test17)\n",
        "y_test18=np.nan_to_num(y_test18)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zmd12H9K3iu",
        "colab_type": "code",
        "outputId": "c0160a42-05fe-45e3-90d7-8fa64ff8227d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14998, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feOCg2VVLLfe",
        "colab_type": "code",
        "outputId": "7c1a3c83-f127-45f5-ceac-cbeb21ed9d09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14998,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sV15YyHcQ2vj",
        "colab_type": "code",
        "outputId": "9aa9f43d-e11c-43ee-bdcb-12dd4517fc13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X8,y8)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvagXvUsREvQ",
        "colab_type": "code",
        "outputId": "309b6f42-5e74-4651-efd2-2e77c70a44e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "reg.coef_"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.13482978, 0.19740671])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jX4gtpjmSnDz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station8=reg.predict(X8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DF81OVdzRPEq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station8test=reg.predict(X_test8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ci7ExtrgSNlB",
        "colab_type": "code",
        "outputId": "5de171fa-c898-4276-dc91-e55dcfbfa788",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(y_test8,Station8test )"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003933529373148926"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bR5Bg6PoSuRZ",
        "colab_type": "code",
        "outputId": "1dca17fe-2ad2-4139-98be-a705b44d6d04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y8, Station8)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0036768393761606832"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVso5i0YSciR",
        "colab_type": "code",
        "outputId": "cf2ca12e-38a2-4779-af30-6903e0966e33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        " from sklearn.metrics import r2_score\n",
        " r2_score(y_test8,Station8test)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03720583712662506"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N31rAn-kS3LG",
        "colab_type": "code",
        "outputId": "70c68dfb-1581-4b33-d688-3b47136a6dfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        " r2_score(y8,Station8)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07062651423391775"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23Ci8yxtkdns",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "e09d7c36-d875-4b19-9993-b7b6884824dc"
      },
      "source": [
        "plt.plot(y_test8)\n",
        "plt.plot(Station8test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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pWxSQgpZcZkya5ZVGkN6EzJEBtMd6iuZLPd2c51sQuwZPL9qWcXuAxVszZxHE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\nNYIKuqIoSo2ggq4oilIjqKAriqLUCP8fixrFiJ0KqUkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0oWS2a2HSV2",
        "colab_type": "code",
        "outputId": "254122f8-cbfb-4f97-a856-0dd190549096",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X9,y9)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e12wvbpnHlt4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station9=reg.predict(X9)\n",
        "Station9test=reg.predict(X_test9)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ghtc5L5H8eX",
        "colab_type": "code",
        "outputId": "17160787-5e30-4201-f6d5-95f9a38635b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test9, Station9test)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0002894972547599511"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epygHk_tIthF",
        "colab_type": "code",
        "outputId": "d383afc8-e07b-42d5-b3b1-f1be87f43615",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        " r2_score(y_test9,Station9test)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.07231160751819155"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61KmudDqH_8S",
        "colab_type": "code",
        "outputId": "1206519f-8edd-4da7-9696-3e1ab779ba16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y9, Station9)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00036872794787722545"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mLzi688I2Gt",
        "colab_type": "code",
        "outputId": "4fb10c2e-a454-4d12-dc26-7f007b3166a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y9,Station9)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.062049761637356315"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wG8ZEt4Tk5gF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "275c77c0-54ee-4111-ecf8-3bc320caa34a"
      },
      "source": [
        "plt.plot(y_test9)\n",
        "plt.plot(Station9test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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77yd32yevc43vBN6yRN893b/zo4Fd3b//lvV+LwCnAWd12ycBd3a1bJpjuUKNm+ZYdsfj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\naoSBLkmNMNAlqRH/D8Aagx8Wp9POAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOrXpPJsIGpW",
        "colab_type": "code",
        "outputId": "ccce7fd9-d1f3-4650-b19f-baaf15af7ee7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X10,y10)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7oUUVBgIY-U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station10=reg.predict(X10)\n",
        "Station10test=reg.predict(X_test10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6srjltieIcCZ",
        "colab_type": "code",
        "outputId": "3f37cc87-64e3-471c-ad99-3979f76b398b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test10, Station10test)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0003735086533540237"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "etTZW_1nIfyf",
        "colab_type": "code",
        "outputId": "4c6e2255-5051-4560-8026-98af709d6cfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y10, Station10)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0026906558796144496"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoVxsrebIpT-",
        "colab_type": "code",
        "outputId": "75a200c1-4e11-42a2-8c92-3765d035b1f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y10,Station10)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08191718450375773"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfN_X25HI_-U",
        "colab_type": "code",
        "outputId": "cc4c7cb7-9fc1-4ac0-a619-a841f26f38a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test10,Station10test)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.38349502030402616"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzZY0_jck_Jq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "b0327890-87a1-41f5-95c7-7985423af0eb"
      },
      "source": [
        "plt.plot(y_test10)\n",
        "plt.plot(Station10test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CrgaGMyfJGWX",
        "colab_type": "code",
        "outputId": "dec8b340-5db7-4e25-91f1-2016f1eb563d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X11,y11)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9fPoegjHJh71",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station11=reg.predict(X11)\n",
        "Station11test=reg.predict(X_test11)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1g3RApY4Jnzd",
        "colab_type": "code",
        "outputId": "3a45a047-7157-4ee1-8422-d63d09b8866b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y11,Station11)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07358492208359668"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHxiubcEJrCU",
        "colab_type": "code",
        "outputId": "02a8b932-e274-4cfc-cd0e-e9b4ca848e5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test11,Station11test)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.3269835625378994"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LoUzHwklIP3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "25691d70-5b06-463c-c478-25e5e359b559"
      },
      "source": [
        "plt.plot(y_test11)\n",
        "plt.plot(Station11test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mGbx2Vvz1U1UPVdV/dsvfAe5i8JnXq3Z/HyDz/qyK/d3ts+92N9d1XwX8DHBNt37hvp7/\nO7gGeGOSHOD5rGTm/ent9TFp5b/Yh8kf6EW50gr4lyQ3Z/Bh9QAnVtVD3fL/Aid2y6vtuRxsztWS\n/6Lu7e8V81MnrNLM3bTCqxgc3U3E/l6QGVb5/k6yJsktwMMMCvCrwLer6qlFMuzL121/FDhhpXMv\nzFxV8/v6D7p9/SdJjlyYeUG2g848aeW/2r2uqk4HzgLeleT1wxtr8P5s1V9bOyk5gb8AfgR4JfAQ\n8MfjjbN/SY4C/g74jar6v+Ftq3V/L5J51e/vqnq6ql7J4LPGzwB+bMyRlrQwc5KXA+9jkP0nGUzl\n/Hbfjztp5T/Kh8mPTVU92H1/GPgHBi++b8xP53TfH+6Gr7bncrA5x56/qr7R/cN5BvgYz741X1WZ\nk6xjUKJ/U1V/361e1ft7scyTsr+7rN8Grgd+isHUyPynFg5n2Jev234MsJcx5R7KvLWbequqehz4\nK5ZhX09a+Y/yYfJjkeSFSY6eXwbOBG7n+z/c/nzgn7rlncAvdWfvXwM8OjQNMA4Hm/Na4Mwkx3Vv\n/8/s1q2YBedIfo7B/p7PvL27mmMzsAX4EmN4/XRzyJ8A7qqqDw9tWrX7e3+ZV/v+TjKV5Nhu+QeA\nNzM4X3E9cG43bOG+nv87OBe4rnsXtr/ns1KZ/2vowCAMzlEM7+t+Xh+HepZ6XF8Mznbfw2Au7/3j\nzjOU61QGVwjcCtwxn43BHOIXgXuBfwWOr2fP8l/ePY+vANMrmPUzDN62P8lgbvCCQ8kJ/AqDk2Gz\nwDvGkPnTXabbun8UJw2Nf3+X+W7grHG9foDXMZjSuQ24pfs6ezXv7wNkXtX7G3gF8OUu3+3AJd36\nUxmU9yzwOeDIbv367vZst/3UpZ7PCma+rtvXtwN/zbNXBPX2+vDXO0hSgyZt2keS1APLX5IaZPlL\nUoMsf0lqkOUvSQ2y/CWpQZa/JDXo/wEBgbDTCYBidwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0PXVnPiJw78",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VWpgScqJu29",
        "colab_type": "code",
        "outputId": "98532f19-7e28-4de1-ce05-8c18891d867d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X12,y12)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gl2k1F-cJzxk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station12=reg.predict(X12)\n",
        "Station12test=reg.predict(X_test12)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQbVZwtjJ3EQ",
        "colab_type": "code",
        "outputId": "7bf88365-d583-4912-fab5-cb7a8a2409fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y12,Station12)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1046874923562452"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTL8MpRqJ6El",
        "colab_type": "code",
        "outputId": "ad75a6f7-1596-4548-a444-0008121d4a23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test12,Station12test)"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.25512979090836785"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUXQ-0D3lMzP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "8555fedb-dfe4-49de-bc08-4043ff975afb"
      },
      "source": [
        "plt.plot(y_test12)\n",
        "plt.plot(Station12test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qtumakryr6//5JO9a7blGXON7kzzR7cv7k1w7sO3dXY2PJHnnQPuWvRaSXJ7kniQnkjyU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\nIwx0SWqEgS5JjTDQJakR/x8fELs83e/IagAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vy7__MV8J_LD",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrowjLFxJ9Wj",
        "colab_type": "code",
        "outputId": "ed8db7ec-b2cf-4772-89e7-8463ccaf3156",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X13,y13)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZL-KUlaSKBd-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station13=reg.predict(X13)\n",
        "Station13test=reg.predict(X_test13)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ol96KuvdKEKL",
        "colab_type": "code",
        "outputId": "f0f4323a-e14a-450f-840c-41f4c0f57289",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y13,Station13)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0027253764383948506"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJJTc9s5KLLp",
        "colab_type": "code",
        "outputId": "fb3b1549-8954-4b82-d7d7-cadb09ab33b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test13,Station13test)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00036745969586937275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10PxmkIRKPpR",
        "colab_type": "code",
        "outputId": "7c9b655c-b716-4ce1-a807-077a1333e16a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y13,Station13)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07308819926642907"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyYAI4YGKVBR",
        "colab_type": "code",
        "outputId": "f9c2769d-5bac-45a5-d627-6b38001dff9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test13,Station13test)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.36108853769652405"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2_jkxK_lTiX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "d66f43c8-ec2a-424e-d79a-da48f0b8baa1"
      },
      "source": [
        "plt.plot(y_test13)\n",
        "plt.plot(Station13test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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O4KIx1vMgsGfVsl8DrummrwFe301fDrwbCPBS4L0jqullwMXA3WdaE/Ac4IHu97O76WeP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\npZHyNxubBAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhQFmtJDKfFO",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZfdDR2RKY3g",
        "colab_type": "code",
        "outputId": "2b4baa05-cbba-481f-e5a1-1cfeb94860d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X14,y14)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X50GiIsoKg3z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station14=reg.predict(X14)\n",
        "Station14test=reg.predict(X_test14)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oJ51x8ZKljD",
        "colab_type": "code",
        "outputId": "677b8e86-1f45-4c20-a03b-aac5655ea4f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test14,Station14test)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003976467614366887"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3urI3tUJKpEU",
        "colab_type": "code",
        "outputId": "64a91b60-8679-474d-ce81-c25f3affbe28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y14,Station14)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002505227039988342"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r299o65EKt1M",
        "colab_type": "code",
        "outputId": "df0b99f8-4734-4730-dd9f-b35ce9b4b8d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test14,Station14test)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.034712125417182715"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLuUwwwxKxo6",
        "colab_type": "code",
        "outputId": "e91a38cc-5d91-4ae0-da52-9be9d21ec2e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y14,Station14)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06555224896435285"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Nv_ug5Ol_Nh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "b087551d-141d-42de-da26-852411015ab5"
      },
      "source": [
        "plt.plot(y_test14)\n",
        "plt.plot(Station14test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z3GBwQpGVE6OEsFeWRmQWzQHFjj0PQzcOIQ4g+7c7bQB/1GDdTfntsM46CKGN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\nhgkIrNAZhmECAit0hmGYgPD/AZ+fN8NJBCZZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hoxLwahK2Bw",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FARhlbtwK1P-",
        "colab_type": "code",
        "outputId": "34babf86-2412-4555-c4ed-36b641aee89a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X15,y15)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AU24RhQvK5YG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station15=reg.predict(X15)\n",
        "Station15test=reg.predict(X_test15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mr9N0aIIK9y4",
        "colab_type": "code",
        "outputId": "2ef3fb56-0fa1-41f6-df28-abe85df50422",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y15,Station15)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0031042065117012794"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jOLt7v7LELm",
        "colab_type": "code",
        "outputId": "1cb4c1f4-fd8d-447c-9861-1ba39c55223b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test15,Station15test)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0042674491233922335"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYC0vqMRLJNo",
        "colab_type": "code",
        "outputId": "f96d6d6e-9640-4a16-de87-e87122f46c59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test15,Station15test)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.023802974889989392"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4bRvqpMePAD",
        "colab_type": "code",
        "outputId": "ac2689ce-bd8c-4280-e191-6c349f1ec453",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y15,Station15)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04733693322411525"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-tRKYwNmIRo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "af9a731c-80ab-43f9-ada1-7c0c896176ca"
      },
      "source": [
        "plt.plot(y_test15)\n",
        "plt.plot(Station15test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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14d1T2Nsd4dufGFrWcmvaQu/pylkpLeVSAtW+wFMuOrrCXPFw9exj89i7K9OOVbqK91bI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\nPwHCIlZVSnBNAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Epe0LAwneWVq",
        "colab_type": "code",
        "outputId": "2d563e70-6351-43ac-fae6-d6cbe68684d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X16,y16)"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kEtCzh0Zea1Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station16=reg.predict(X16)\n",
        "Station16test=reg.predict(X_test16)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d__nNjHBee0c",
        "colab_type": "code",
        "outputId": "81f269b8-5ac2-4a98-f959-2194561f5138",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y16,Station16)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0025264519605659634"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gXIp4w6ehzY",
        "colab_type": "code",
        "outputId": "196aefad-7fa1-48bb-b4b5-b274b04616c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test16,Station16test)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0026339434492434993"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqF2CzEaely8",
        "colab_type": "code",
        "outputId": "a772f2c6-9a47-46a1-a993-9074416a8819",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y16,Station16)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.047795473762775265"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQJNM6Hpeoo-",
        "colab_type": "code",
        "outputId": "764c5d98-d8b8-44f2-c704-2cc0cbbfee6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test16,Station16test)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.030489742089803373"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WlXRx3DCmNWT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "92239728-41cd-4bdd-9eaa-2064dab94e31"
      },
      "source": [
        "plt.plot(y_test16)\n",
        "plt.plot(Station16test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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81Tc1du5v7sq48wk5ecZzHlmEcX+ak/7AAqWoBX1WhbKQ0t7WrjxbUprYbf04JYzF7CEJ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\niSCCLgiCUCKIoAuCIJQI/x8mLRAV7vZl9wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTsNsb0cewNj",
        "colab_type": "code",
        "outputId": "dfb4e03e-d97b-4f91-9c88-b1bfa4d857a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X17,y17)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-z4yErUe1SJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station17=reg.predict(X17)\n",
        "Station17test=reg.predict(X_test17)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kr4ej6Pe5Xq",
        "colab_type": "code",
        "outputId": "210f4144-811d-436c-afc7-a6e70cf26000",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y17,Station17)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0025170336138352982"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXSNWquPe8tR",
        "colab_type": "code",
        "outputId": "9c324659-f8b0-401e-80ff-fd75ba9ca84e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test17,Station17test)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.008134285009407214"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPlbPfb6fBWW",
        "colab_type": "code",
        "outputId": "63501d82-f535-4542-8732-7860fe748a12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y17,Station17)"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.061910639746241225"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsNIB251fFBX",
        "colab_type": "code",
        "outputId": "4b12a9c2-69e2-4bb6-d6cd-1316224f59d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test17,Station17test)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.024656481594837687"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVYTU4ElmRhJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "1a9a5cfa-8d16-48fc-c2a4-52ef6042a921"
      },
      "source": [
        "plt.plot(y_test17)\n",
        "plt.plot(Station17test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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lxHBt4SglnZAY5Rr8eBPnRN7Dj+N/x//FHpXzJFnleO9DD4cBlX//dtqSG9ia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\nTJ3w/wELRgyow8nOpAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3L_-aVDIfIG4",
        "colab_type": "code",
        "outputId": "9d4c18ba-78b5-4f81-cb9b-d21e227cbb1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X18,y18)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AeSFxHo9fMe6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station18=reg.predict(X18)\n",
        "Station18test=reg.predict(X_test18)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0irp1w8sfP-W",
        "colab_type": "code",
        "outputId": "c7171fef-f0d7-4ea0-b64b-8bc22eb21ea0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y18,Station18)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0001570778656111503"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8NNvtbLfS4z",
        "colab_type": "code",
        "outputId": "48dae245-603f-409c-e413-608ad230e740",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test18,Station18test)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0023696690463456994"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHbhn-rFfWnw",
        "colab_type": "code",
        "outputId": "c540a3f1-5552-4da0-8c0f-c8a4bdc24db7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y18,Station18)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00946254522186607"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I68KysRefZ0F",
        "colab_type": "code",
        "outputId": "aa5d2d64-98ab-4aab-ba0e-23494309a682",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test18,Station18test)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.013287716872504518"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9P3FGgHmWjH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "ea68f8f9-e57b-4027-82ab-88472888f079"
      },
      "source": [
        "plt.plot(y_test18)\n",
        "plt.plot(Station18test)\n",
        "plt.legend(\"Actual\",\"predicted\")\n",
        "plt.show()"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'A' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'c' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 't' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'u' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'a' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: UserWarning: Legend does not support 'l' instances.\n",
            "A proxy artist may be used instead.\n",
            "See: http://matplotlib.org/users/legend_guide.html#creating-artists-specifically-for-adding-to-the-legend-aka-proxy-artists\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z6EgKYfm5RHivi9nuFlrQgu4Vt2c3OqDCq+B6jcH2+106aT0m9jIYxHWWOGer\nQxrX0uaxu1qQX++puwu6N4o6U8QcT+UEJOrlkkjo7f3b4xZO7nYJE9z0/MDmMElFWtU0yyGXSPQF\nm9fDc5KTjkP1/IpdMX9rt0UPpCojN/HqiYZP0shjoJ+yXT2RpEK6amewy6c1tvT17MjTdtAYnF3x\n0vtd/24u8SGZRGGrRF6uV92Lv4e3vty/e+TuJv+7jAKs3mXlvfdge2xIJEmdy36bqJVjSDJwjgag\nho2tnbR1xjommU/rnLEZgVOQgj5rxS5W1lpzLwup42o9boJuK5pXrzsSSc9D71sFqO83D79Rw/XP\nrU5sp0eVzbRR7WBMP/Hc1cohtHNO6C0PKdPvVeTlV4nmaDemr5EwYQw94bAld17buK/fPjevPVMm\nyW4+E1oGwOqd1tD79btjJx1LVq2y3cvF5MhDP/mmV/jCXYvibMnMhoFNcaCNov344ZPvcv5db/Af\nXYvYWnEJ5c1bk6Z3q9DRB3a2yxSqbjS3d2fUSBf/m8eXDHxFID88hGC8DG/q8MvSR7iv7E4+KMnv\nW+yUs8lxTnHgvLZb7QFETsu+9ciylP3J+9UZl5CLcw6gRCLtJpgtnf6NuJxT9j9ML7sjaRprtS33\nY9n20CMm8xwHWmXj57LP9Bn4+T8SO2Wp0JBLEs7ost64QxqSxyRdQy72E3vfq1s855fJREbBNIr6\n0GvHBzu8nnXOmthG1fdiea0jJfk8M42tnbR6jN9f73jInOWz3p4i1yloNQ2t1DS45x1Nl+heO/eu\n3dXnqSX6ugp6Lc8yscrH+aUan2WyQXHZ7rYY9dBDGdTAfAl1BD1IbCAUtKBH6UwRm3YTwJ7eRlHv\nNyc6L0k4lLrYUn2iDwQv9Sl12CH27yEEE9cFOHjoIP8bfpIKLI+2mxIASlKEy15avYfXMhiQ46WX\nx3qXudDjB1u9s/2A4/f90zjrVaKH3Lku9bTwo7xd/t1+aWaXXcOMsl+6/t4rZfSF0+I1Olkdz3bI\nJdIr6JmMkPb3Wcr0bL3tb/b/k2Q3y8qv4D0eeu5ot8UkROviPfOruf+1xJ6229dw9AFM523bHbFO\n5EHPA35QUtucuitnX4LJUsu6im9xQWhRkl94wf2iv17yCt8LP8/lJVZDXY9d7cJ4Dz2k8yx3dlsv\neOdkUqnuxxmhFZRGOohewx/mbuT/3fMm79qinmoaVbd2Goj1gL8VfpkxYr1Ifh1+gH8LWYsjnBja\nxsdD61JcVR+f+t1C7l24OWZfjKDH3Yfm9i7uWlANwK/CD3J6aFXCtEETLaWoh37Pwmrvv7V/XEYX\nR8vAu9Iu3eKt6+T5oTeYU9/v+VkAABJQSURBVPY/RK2PxGnHf5XMZZw08/mSJTG/K6dv6urDaOeT\nIX9Wp0pGQQu6k+eW70p4zNVDt/dtSrFGpJPoSLVkD8HWfS1U7z3kebX6TPDkoRvDJNkdU6lij/dt\nnyBW3+DPllQBcDjNhOmmJA3BjWcobZwiVh/cqDc2Wg7yjZI5HCaWp55I0IfRyktl/8uJUtNnbxq+\nzSd/t5DHl27n/de/3Pf7BD//csmr1FRczKNlt3Lxvjt7hXud3bAYXWRaHN9cfefs204UunDbHSLC\nxeEFPFl2s8crimXLvpZ+vWdKHYIeX1Yber9GDJeE5/F42W8cBmZkQlJK6OFYcX8ejS050UbR377s\nfdRo9Kp+U/oAr5X/hMMcX5UflK2Mo5HHFtdQOfVFFm9OLdbJpkRwckfpPbwvVNv70oxOlRHVkIh9\nTdF6fm/pHWwov5QNFZcxSaw2ul+WPsTDZbdR2hBsv/SiEHQD9EQSf8K5xUPdkn8+tJiaios5P/Qm\nh9PMJ0Kr+RexPP9jxw3lqqfe5Y7Su+nYujhhXp/83ULOur1vQh8vy9t9KvQOl5S8AsBIDlFBB0KE\nIzjAFFnPfaWxjV6eYug9HSwov5o7S+9mDE2cGecd1B+08hAidFAKWJ7PYbTzTsUVVFd8g80VX+fs\nUBXDaHXLoR9O8bq99F6eLb+RURyky54y6JvhOdxU+hc+FrLEKD7kEqabW8LT+WLJG3wgtIOp4Sd7\nj5V4+OQZyaHeWPILcf1/E3nQvyu9v3d7YufmXn2LZhf9ndvcPM7tSWOHup7fzephuC//liik8Kd5\nm/jmw/17BX0i1NduUEYXZXRxWcnLhEzsi9Kq/oYvhV53te+n4ae4omSWa97900diXvTzyq5mUfkP\nY9L8JPw088t/yrXhvwLWfe21xS6Rw+kf8kpFtN6fbl/3cLteltLNi+XXsaziv7neXhLuogeWUH+w\ng1Ec5BjpW4fgtY31/CT8NDUVF9Ntjzz95sNv9fvicdJpPx9DiJ1tMaoh0Wv6Xvh5aiou5tySZZSL\n1UX4Q3bD/1FihQ5D7cEOqCqSybncQyfWzayjx3wIsGJdozjEu2ay6wP+/fBzAPyx7C5WRiZxUsi6\nGXN7PsIicz6NHMl/lrzB2aG3WbTp65w+eWxi24yhlG4eXbwt5XU8VPY7AP7a82lWVFzOTjOGTZGJ\nnFmyojfNJZFXmNMzhUvC8zCR01KeU7otz/zs0NscWdbAyaEtvL/9Ye4rvZNJspv/eOhO5pX9jBHS\nytVdVwDWZ/s14SdizvNA2e3M7jkV+ErKPI0tHH8ovbvX2x8hrb0PRDxOD71SdjNJ9nBheCHd5jUA\nhko7H5QaXiy/lkWHZgNHJsx7JIdYUXE5r/Scwne6fkq4pE9KT5Qaqg9OABc7Okxp78NXavq+ZqJf\nWM56FSISI+LOr7sLpy9hkuxmuzki5vyddrtL9GsFEgv6pGtmU3PL5/rt/32C0YZPlP26d3uUtHBu\n6C1uKH2MmY3DeYG+OmKM4TOhKn5fdl+/c4jAlXa9h/v7HY/nubLrOVG2cXzH43xUNnBcyPJAQ0R6\nPdUPiyWOl4dfZH7kI8wo+xXP9Pw7NZHxfLVkIQBXlT7N/T2fp4OylHn2XQe8T7YzXqwuy+XSBSZx\n+8+rG+t5ofw6Jso+Ktutev2Nh95ic/nfASjpthrGF2yoZ8GGer535nGu5xkiVr34TEkVt5VO56Pt\n9wLWy/5Y2cVlJda0wqOl/9d+hXTyw5JnOTVkfYlIT7AriHny0EXkHBHZICLVIjLV5Xi5iDxlH18q\nIpV+G+pql+N/tzDE38um8Vz5NP5ZZXmEC8qv5u/lNzCCFnocCy9cF36cmoqL+UCor0thVMwBzip5\nlxsP/YJhYj2IIQxz1iRffWhI82Y2VXyDc0NL+RfZwtyynzKOA/3SHeWIBX4hZMXgJkhDjJgD/Kr0\nYZZVfJ8fhZ+lfG/sMVcilkiFJcLJIesrY6w0c2bJCo4J7UWIcFxoN+OkiffJDgDOKFnF18Nz+53q\nlNCm1PkBYHi89NdcUPJm756TZTOdCfyGCbKPO0vvYgQtLCy/mofLbgP64qtDaec8Oy75ntrkc3EP\nt+/N2SXv8F729YZAxtLE7PJruTn8IOeH3mRu2U8ZTTOjaaam4uJeMQdL0D9kqnm+7FqG2t5fVNCH\ntu1hS8UlfORAXxjnz69vYQxNDKGdsTSxoPxqpoUfjbGro7uHcRzgutK/9u6LemsAi8uvTHpdXrk2\n/FcqsK5l/x6rHn+3xPIY19fWUxEXers6PJNN5V/nnEPePHOAD0oNJ4W2EpYIY2nimfJf9B7bUnEJ\nXwwt4qWyqb0hNYAZZb8C4Eslr3N16dOUS5+3vqHisrSu8b5XNzOnvE9+Li6Zx1dKFjIl5P7C++nf\nVjBRouMA+p73VioACHel/kpwhv1uK50OwJTQRv715rn0RAxPld1EhSSeinko7VxV+nTv30ELekoP\nXURKgLuBs4FaYJmIzDLGOPsKfhs4YIw5XkQuBG4FvhaEwTTv4vHSm2lhCEdErJFx15c+xiOdPbC9\nojeZMYZJIUssr6/7MWx/uPfYyorvsGfro9Aynq+ULOQ74dmesr4p/AgAh0kHxzS9Bdv7v5EvL3me\nk0Obqaj7MGDF36I3fFnF93mh5+OwfUxv+tfLf9K7/ceyuzzZUb5vDWwvT5pm+N6afvsWlf+od/uW\n8J97t68rfaJfWifvkQOwfUnSNAAVDc2cXhK7Cvofy+5iv3FfLeqaUiuk8kXHCwD6RhG+P7SD94es\nl82IxrVJbXi67Mbe7TvL7ua1tks4K/waR4oVJ/1K+DW+guX5v1txhes5RnfVcRO3MzFUx1fbn+Zb\npTWM2PV1GHsCE/ZaYbTz6u6D7WcAMCWyivsqLFG7q/sCAC4NvxJj51fa/sb3Kh6Pyeep8r4eLVH7\nAKtR2lE3onwutIQR0gLbx2CM4efhx9htYtOdUbKKM0qsxs4rws9zRfj53mNuwvmD8D8AuKz5nr6d\nW1+HEvevKYAXy6/t3a6q+F6/43eW3dNvXyqmhp9wvWY3trw5F6dDf0X4hX5pzgst4Y7Se3nXHM8z\nPf/e99vyS9jLKKZ1Xdb78v9ow/OwfSiXlsyx2iBc7JjtuOYo95fdwc9aW5Dt5YyT5IOMbiyNfcGX\nN6yF7e+F0ZNg+Pikv80ESdUVSET+DbjRGPNZ++9rAIwxv3GkmWOnWSwiYWAPMM4kOfmUKVNMVVVV\n2gavmPELPrz+9rR/pyiKki+sPPkGTvriVRn9VkTeNsZMcTvmJYY+Adjh+LsW+FiiNMaYbhFpAsYA\nMeOeReRy4HKAo48+2pPx8TQd+zm+txpGDwkzctRodu3cQTvlVI4fzdAhFTFpN+7Yy5BIM+ER4zly\n9HDqm9vYt38/h9HBMZXHAtDa2cPOXTt7f9NJmHHSxAEznMOknS4T5gDDOaVyDKtr9jBKDlFOF6Mn\nvo/Skv4Rq9U76jkqspsRR5/E1m01tEoF5aaT4dJGoxnKEaNHcvjI4X027tqH6WxnpLTQSZhtkfFM\nkH10EqaJoYyviNDdfqg31lgqPZxwzERPZbWipo4SIoyUFg6ZIYyyY3x1ZjRjy7op6bIaLCvopJwu\nGodNYsihWropockMZZi0MUH2sav8WCaOPyJFbhYbtu+hPNJK+ajxhJqsatNkhlJCDxFCGIQeQgyh\noze2foQ0UkEHteYIDlHBSceMZ/m2vYygjQrpoNVUcELlUUnz3bm/hdKD22k15XRSyjETJ7C9tpYR\nY8bT1rATRhxJV3M9bZTzHtlPB6VEjHX/WqhAMLzv6PdysKOHhrpajj76GDZv38kJlRMRrA/2LTU1\nHHPU0YTt+94dMWzfvo2uisMpb99HPaM4rCzMB987oteutq4eDu7aSL0ZyQRpoNEMpY7RvE9qaeYw\nxtLEbnM4w6SdFlPBsZWV/a/tQAttTfs4vvIYAJbX7GXiiFJ2N3dwnOxivTmKE6SWltEn0rZ/J6XS\nQ5spo5sSRkoLB81hDJEOjqARg1DPSMrp4oAZzvFHT2D59v2E6ebDlck9xg27DjC+q5ZOwnQSps4c\nTg8h3ic7aDAjmDDxaDbV1jE01EWpscIu7aacIdJBuyllyGHD2NsaoZNSxtDEUOmgZ+h4JowdlbJe\nAdQ2NNF0sIUmhiEYxpZ1MaIsRPOhQxwjddSVH8OIUYfTUbeJMD3sNocTwlAWihCREsYMP4yhw0bQ\ntnMNbZQx7qgTCIeErfWHGCJdvGfs6H55btq1j2FdDXQRZggdhDAcNEPoIcRxlZWsq9nJMGmjzZQT\nIkKdOZwx0kStGcdIaWGi1FvPIC3UmrEcZ9/fjx3zcU/XnC5ZbRQ1xkwHpoPloWdyjjNOncIZp7q+\nnBRFUQY1XhpFdwJO92iivc81jR1yGQkFPeGxoihKweFF0JcBk0VkkoiUARcC8U3js4BL7e0vA/OT\nxc8VRVEU/0kZcrFj4lcCc4AS4CFjzBoRuQmoMsbMAh4EHhORamA/lugriqIoWcRTDN0YMxuYHbdv\nmmO7HS8jTxRFUZTAKIqh/4qiKIoKuqIoStGggq4oilIkqKAriqIUCSmH/geWsUg9kHoqQnfGEjcK\nNQ8pBBuhMOxUG/1BbfSHXNt4jDFmnNuBnAn6QBCRqkRzGeQLhWAjFIadaqM/qI3+kM82ashFURSl\nSFBBVxRFKRIKVdCn59oADxSCjVAYdqqN/qA2+kPe2liQMXRFURSlP4XqoSuKoihxqKAriqIUCQUn\n6KkWrM6yLTUiskpElotIlb3vcBF5RUQ22f+PtveLiPzRtnuliJwSkE0PicheEVnt2Je2TSJyqZ1+\nk4hc6paXzzbeKCI77bJcLiLnOY5dY9u4QUQ+69gfWF0QkaNEZIGIrBWRNSLyI3t/3pRlEhvzpixF\npEJE3hKRFbaNv7D3TxJrQflqsRaYL7P3J1xwPpHtAdr4iIhsdZTjyfb+nDw3njDGFMw/rOl7NwPH\nYi0XuwI4MYf21ABj4/b9Fphqb08FbrW3zwNeAgT4OLA0IJvOAE4BVmdqE3A4sMX+f7S9PTpgG28E\nfuqS9kT7PpcDk+z7XxJ0XQCOBE6xt4cDG21b8qYsk9iYN2Vpl8cwe7sUWGqXz0zgQnv/fcD37O3/\nBu6zty8Enkpme8A2PgJ82SV9Tp4bL/8KzUM/Fag2xmwxxnQCM4ALcmxTPBcAf7G3/wJ80bH/UWOx\nBBglIkf6nbkx5jWsOekHYtNngVeMMfuNMQeAV4BzArYxERcAM4wxHcaYrUA1Vj0ItC4YY3YbY96x\ntw8C67DWzs2bskxiYyKyXpZ2eRyy/yy1/xngU8DT9v74coyW79PAp0VEktgepI2JyMlz44VCE3S3\nBauTVeCgMcA/ReRtsRbABhhvjNltb+8Boivv5tL2dG3Kla1X2p+wD0VDGflgo/3Z/xEszy0vyzLO\nRsijshSREhFZDuzFErnNQKMxptslv5gF54HogvNZtdEYEy3Hm+1yvENEyuNtjLMl5/pUaIKeb5xu\njDkFOBf4voic4TxorO+wvOoXmo822dwLHAecDOwGfp9bcyxEZBjwDPBjY0yz81i+lKWLjXlVlsaY\nHmPMyVjrEZ8KvD+X9rgRb6OIfAi4BsvWf8UKo/xvDk30RKEJupcFq7OGMWan/f9e4O9YlbUuGkqx\n/99rJ8+l7enalHVbjTF19kMVAR6g73M6ZzaKSCmWUP7VGPOsvTuvytLNxnwsS9uuRmAB8G9YYYro\nimnO/BItOJ9tG8+xQ1rGGNMBPEyelGMyCk3QvSxYnRVEZKiIDI9uA58BVhO7YPalwHP29izgG3YL\n+ceBJsene9Cka9Mc4DMiMtr+XP+MvS8w4toT/hOrLKM2Xmj3fpgETAbeIuC6YMdtHwTWGWNudxzK\nm7JMZGM+laWIjBORUfb2EOBsrFj/AqwF5aF/ObotOJ/I9qBsXO94cQtWjN9Zjnnx3PQjmy2wfvzD\namHeiBWHuy6HdhyL1eq+AlgTtQUr3jcP2ATMBQ43fS3pd9t2rwKmBGTXk1if2V1YMbxvZ2IT8C2s\nhqdq4JtZsPEx24aVWA/MkY7019k2bgDOzUZdAE7HCqesBJbb/87Lp7JMYmPelCVwEvCubctqYJrj\n+XnLLpO/AeX2/gr772r7+LGpbA/Qxvl2Oa4GHqevJ0xOnhsv/3Tov6IoSpFQaCEXRVEUJQEq6Iqi\nKEWCCrqiKEqRoIKuKIpSJKigK4qiFAkq6IqiKEWCCrqiKEqR8H/bi76D3FtlKgAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiH6c58wfnm_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6GEwQRkgBn2",
        "colab_type": "code",
        "outputId": "3ead2a82-8cce-472f-ab5a-29a2d72b7f64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y8,Station8)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.025701632409521745"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3H5Orw_gIho",
        "colab_type": "code",
        "outputId": "dda55015-07f5-4685-d132-4e4a40684aa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y_test8,Station8test)"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.026665029045753342"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JVHHLwNgM7E",
        "colab_type": "code",
        "outputId": "b3dfcd54-135c-4fe2-dec1-91d55697b0d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y9,Station9)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.006638154203568645"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O-65BCvgP_W",
        "colab_type": "code",
        "outputId": "d9558e31-b61d-4f69-971c-ca6aca74fac9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y_test9,Station9test)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003286007793417076"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCWS7E63gT8r",
        "colab_type": "code",
        "outputId": "1e6de02c-5ffd-409b-c442-615f70770c0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y10,Station10)"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02201644012737965"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZKI8PjZgXdB",
        "colab_type": "code",
        "outputId": "159ec67f-6706-454e-d730-7b9e3672b6fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y_test10,Station10test)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00976864019342774"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8_K6XGqgbBA",
        "colab_type": "code",
        "outputId": "e4710030-ac80-46ce-9754-6f677303c1fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y11,Station11)"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0213424490705496"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5hbgqOogdiH",
        "colab_type": "code",
        "outputId": "fe703217-ea4d-40d6-c6ae-34185b96cf43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "mean_absolute_error(y11,Station11test)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-135-a4c79ebd3b37>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my11\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStation11test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m    176\u001b[0m     \"\"\"\n\u001b[1;32m    177\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 178\u001b[0;31m         y_true, y_pred, multioutput)\n\u001b[0m\u001b[1;32m    179\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     output_errors = np.average(np.abs(y_pred - y_true),\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \"\"\"\n\u001b[0;32m---> 84\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 212\u001b[0;31m                          \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [11998, 3343]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHhriJ9Oggy7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mean_absolute_error(y11,Station11)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "bo8eZtb4m4bh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mean_absolute_error(y_test11,Station11test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KiriDospm7vw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mean_absolute_error(y12,Station12)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teGy_xFmm-Si",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mean_absolute_error(y_test12,Station12test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zi0tA59DnB9C",
        "colab_type": "code",
        "outputId": "dad7f0eb-3f96-4ba8-c101-949f46e49892",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y13,Station13)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.021453877014134214"
            ]
          },
          "metadata": {
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          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "4ukLy8UTnGzw",
        "colab_type": "code",
        "outputId": "dd43bf6b-6a76-4140-9493-2a559506da57",
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        "mean_absolute_error(y_test13,Station13test)"
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      "outputs": [
        {
          "output_type": "execute_result",
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              "0.009546345501214006"
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          },
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      ]
    },
    {
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        "colab_type": "code",
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      },
      "source": [
        "mean_absolute_error(y14,Station14)"
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      "outputs": [
        {
          "output_type": "execute_result",
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              "0.020713033434568477"
            ]
          },
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          },
          "execution_count": 138
        }
      ]
    },
    {
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      "source": [
        "mean_absolute_error(y_test14,Station14test)"
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        {
          "output_type": "execute_result",
          "data": {
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              "0.02460183649072673"
            ]
          },
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          "execution_count": 139
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      ]
    },
    {
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        "mean_absolute_error(y15,Station15)"
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              "0.023028044927860657"
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          },
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        "mean_absolute_error(y_test15,Station15test)"
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              "0.026900367173826458"
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          },
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              "0.01790973788909145"
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              "0.01790973788909145"
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    {
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        "mean_absolute_error(y18,Station18)"
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              "0.00371714984505401"
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        "mean_absolute_error(y_test18,Station18test)"
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        {
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              "0.00371714984505401"
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          },
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  ]
}