Weather_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Weather-Prediction.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4NiLyAQTSbA"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tempfile import TemporaryFile\n",
        "import tensorflow as tf"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDM2aabQTVCj"
      },
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/csv_files/weatherHistory.csv')\n",
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51kAjWKsTiyE"
      },
      "source": [
        "data['Formatted Date'] = pd.to_datetime(data['Formatted Date'], utc=True)\n",
        "data['Hour'] = data['Formatted Date'].dt.hour\n",
        "data['Day'] = data['Formatted Date'].dt.day\n",
        "data['Month'] = data['Formatted Date'].dt.month\n",
        "data['Year'] = data['Formatted Date'].dt.year\n",
        "data['DayOfYear'] = data['Formatted Date'].dt.dayofyear\n",
        "\n",
        "data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Xu1Fc_1nTnU_",
        "outputId": "107fbedd-ac1f-4a1e-e256-ca96e6e0e837"
      },
      "source": [
        "temperature_average = data.groupby(['DayOfYear', 'Year'])['Temperature (C)'].mean().reset_index()\n",
        "temperature_average"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DayOfYear</th>\n",
              "      <th>Year</th>\n",
              "      <th>Temperature (C)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2006</td>\n",
              "      <td>4.075000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2007</td>\n",
              "      <td>3.806713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2008</td>\n",
              "      <td>-5.663194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>2009</td>\n",
              "      <td>-4.850926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>2010</td>\n",
              "      <td>7.807407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4014</th>\n",
              "      <td>365</td>\n",
              "      <td>2015</td>\n",
              "      <td>-4.304861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4015</th>\n",
              "      <td>365</td>\n",
              "      <td>2016</td>\n",
              "      <td>0.117593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4016</th>\n",
              "      <td>366</td>\n",
              "      <td>2008</td>\n",
              "      <td>-6.218056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4017</th>\n",
              "      <td>366</td>\n",
              "      <td>2012</td>\n",
              "      <td>-3.249306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4018</th>\n",
              "      <td>366</td>\n",
              "      <td>2016</td>\n",
              "      <td>0.113527</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4019 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      DayOfYear  Year  Temperature (C)\n",
              "0             1  2006         4.075000\n",
              "1             1  2007         3.806713\n",
              "2             1  2008        -5.663194\n",
              "3             1  2009        -4.850926\n",
              "4             1  2010         7.807407\n",
              "...         ...   ...              ...\n",
              "4014        365  2015        -4.304861\n",
              "4015        365  2016         0.117593\n",
              "4016        366  2008        -6.218056\n",
              "4017        366  2012        -3.249306\n",
              "4018        366  2016         0.113527\n",
              "\n",
              "[4019 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "wIrpmxiGTpzM",
        "outputId": "8c03a601-3ed0-4c03-b8a3-b760d64a622a"
      },
      "source": [
        "fig = plt.figure(figsize=(18, 10))\n",
        "plt.scatter(temperature_average['DayOfYear'], temperature_average['Temperature (C)'])\n",
        "plt.xlabel('DayOfYear')\n",
        "plt.ylabel('Temperature (C)')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Temperature (C)')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ReRh3pmExIh"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "                tf.keras.layers.Dense(16, input_dim = 1, activation='relu'), \n",
        "                tf.keras.layers.Dense(64, activation='relu'),\n",
        "                tf.keras.layers.Dense(128, activation='relu'),\n",
        "                tf.keras.layers.Dense(256, activation='relu'),\n",
        "                tf.keras.layers.Dense(128, activation='relu'),\n",
        "                tf.keras.layers.Dense(64, activation='relu'),\n",
        "                tf.keras.layers.Dense(16, activation='relu'),\n",
        "                tf.keras.layers.Dense(1, activation='linear'),\n",
        "])"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A3PFjUrHT1LF"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(temperature_average['DayOfYear'],\n",
        "                                                    temperature_average['Temperature (C)'], test_size = 0.1)"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NC9-slitT3KN"
      },
      "source": [
        "X_train = np.array(X_train)\n",
        "Y_train = np.array(Y_train)\n",
        "X_test = np.array(X_test)\n",
        "Y_test = np.array(Y_test)\n",
        "\n",
        "X_train = X_train.reshape(-1, 1)\n",
        "Y_train = Y_train.reshape(-1 ,1)\n",
        "X_test = X_test.reshape(-1, 1)\n",
        "Y_test = Y_test.reshape(-1 ,1)"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgTOMF4oT5_y"
      },
      "source": [
        "model.compile(optimizer = tf.keras.optimizers.SGD(learning_rate = 0.0001),\n",
        "              loss = tf.keras.losses.mean_absolute_error)"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18n4deTeKulO",
        "outputId": "17a43cd1-6100-41c6-ef7e-d8fdab86f787"
      },
      "source": [
        "model.fit(X_train, Y_train, epochs = 100)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "114/114 [==============================] - 1s 3ms/step - loss: 7.4631\n",
            "Epoch 2/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4619\n",
            "Epoch 3/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4607\n",
            "Epoch 4/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4597\n",
            "Epoch 5/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4582\n",
            "Epoch 6/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4568\n",
            "Epoch 7/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4556\n",
            "Epoch 8/100\n",
            "114/114 [==============================] - 0s 4ms/step - loss: 7.4544\n",
            "Epoch 9/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4537\n",
            "Epoch 10/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4526\n",
            "Epoch 11/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4516\n",
            "Epoch 12/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4510\n",
            "Epoch 13/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4500\n",
            "Epoch 14/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4495\n",
            "Epoch 15/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4491\n",
            "Epoch 16/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4486\n",
            "Epoch 17/100\n",
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            "Epoch 82/100\n",
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            "Epoch 83/100\n",
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            "Epoch 85/100\n",
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            "Epoch 86/100\n",
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            "Epoch 87/100\n",
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            "Epoch 88/100\n",
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            "Epoch 89/100\n",
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            "Epoch 90/100\n",
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            "Epoch 91/100\n",
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            "Epoch 92/100\n",
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            "Epoch 93/100\n",
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            "Epoch 94/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4413\n",
            "Epoch 95/100\n",
            "114/114 [==============================] - 0s 4ms/step - loss: 7.4413\n",
            "Epoch 96/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4413\n",
            "Epoch 97/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4413\n",
            "Epoch 98/100\n",
            "114/114 [==============================] - 0s 4ms/step - loss: 7.4413\n",
            "Epoch 99/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4413\n",
            "Epoch 100/100\n",
            "114/114 [==============================] - 0s 3ms/step - loss: 7.4413\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f99206c9550>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ls_t51P4VxKm",
        "outputId": "4735cf4f-b1b3-4f54-c300-e7b3d11f3ce1"
      },
      "source": [
        "model.evaluate(X_test, Y_test)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13/13 [==============================] - 0s 2ms/step - loss: 7.5488\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.5488481521606445"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    }
  ]
}