MLR1f15.ipynb

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    "colab": {
      "name": "MLR1f15.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNQNS3GBB5b4DTXJLiJpcp3",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VishnuBhaarath/Rainfallpred/blob/master/MLR1f15.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5YGfJ9tYkM94",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from numpy.random import seed\n",
        "seed(1)\n",
        "from tensorflow import set_random_seed\n",
        "set_random_seed(2)\n",
        "from sklearn.metrics import r2_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cou_fBaEkP6G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from pandas import DataFrame\n",
        "import numpy as np\n",
        "from numpy import hstack\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn import preprocessing"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACH71at4kV9t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.read_excel('trend_anna_imd_21.xlsx')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hp4FJfQkk9o",
        "colab_type": "code",
        "outputId": "60eaf35c-ffeb-4646-aa40-31e3ffacc94d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1965-01-04 00:00:00</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": 12
        }
      ]
    },
    {
      "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": "1aebc167-3e19-48b8-e1b5-6017ca84a121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "Pennagaram.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <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",
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              "</table>\n",
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            ],
            "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": 14
        }
      ]
    },
    {
      "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 = 1\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": "8zmd12H9K3iu",
        "colab_type": "code",
        "outputId": "e2f133bb-a791-4e90-b668-9582b30fc69c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14999, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "feOCg2VVLLfe",
        "colab_type": "code",
        "outputId": "9076859c-4caa-45cd-b5b4-835739acf120",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14999,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sV15YyHcQ2vj",
        "colab_type": "code",
        "outputId": "4fcd2840-d4e7-4853-c22d-b3885bd7aa23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "reg = linear_model.Ridge(alpha=.5)\n",
        "reg.fit(X,y)"
      ],
      "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": "a7dc4e77-a0fe-4d7f-b6aa-ae023f3d264b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "reg.coef_"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.3314235])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jX4gtpjmSnDz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Station=reg.predict(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLJ42q148TA_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "1adaf577-2205-4f0b-9a86-abc08239ce01"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(Station)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd1bc33b240>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FnhZMGD0MzMDWvS348pjTbc/z1KqdeGoVcMoJg3HJp884tl61V5cVA3z0uvt1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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYq4f0uWAl-h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "5478fe9c-3944-4632-a5f6-bd21c2eafe25"
      },
      "source": [
        "plt.plot(X)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd1be4cdf60>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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g913pGhy0cLM17D2E/gHlGEr5+VU78OsvnG2x3+KOgwqCzpa90zXTfT3T0ihS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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-R6yP0vdAC9s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "6326ab7a-8daf-4661-83c9-84a8ff34dd8f"
      },
      "source": [
        "plt.plot(X)\n",
        "plt.legend(\"A\")\n",
        "plt.plot(Station)\n",
        "plt.legend(\"P\")"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd1be74ccf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UVmMnltGiogqc/885+K74UKZFAdD6BMK8GEgLg0azx5dV2oRCN/cLbVv0GT4b54+Yk2kx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\nAit0hmGYHIEVOsMwTI7w/6J5bC+usAUOAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DF81OVdzRPEq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Stationtest=reg.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ci7ExtrgSNlB",
        "colab_type": "code",
        "outputId": "1cb17b4a-8943-4ad0-f606-56de422103b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "mean_squared_error(y_test,Stationtest )"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.004071717806997995"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cYajWK-81UM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "daa94f12-e2e5-4c88-e595-59fb1c708433"
      },
      "source": [
        "plt.plot(y_test,label=\"Actual\")\n",
        "plt.legend()\n",
        "plt.plot(Stationtest)\n",
        "plt.legend(\"predicted\")\n"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd1bf76aba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Glm1yni1qpUi86okxTT5d/5pTx3ce/MB9mbZcTwvOyWtQNN8Y2vpdHXTY9iwt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\nURSlQvj/hYxewegC4YUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bR5Bg6PoSuRZ",
        "colab_type": "code",
        "outputId": "5d688620-075a-4675-b760-218d5b1efaac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y, Station)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0013114483853529392"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30jp2FItCFmm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cda4a524-e10c-4a18-8c25-77b46a48ffa0"
      },
      "source": [
        "mean_squared_error(y_test, Stationtest)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.004071717806997995"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVso5i0YSciR",
        "colab_type": "code",
        "outputId": "01da2f34-227a-4769-e336-f47d8e7e155f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        " from sklearn.metrics import r2_score\n",
        " r2_score(y_test8,Station8test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05316081593373512"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N31rAn-kS3LG",
        "colab_type": "code",
        "outputId": "a7f63aa8-970c-4d77-b363-a2785bfb4b21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        }
      },
      "source": [
        " r2_score(y_test,Stationtest)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-78-5f5c3563473b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStationtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'r2_score' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0oWS2a2HSV2",
        "colab_type": "code",
        "outputId": "5f56dca0-7c93-493f-b756-29b0e5224a49",
        "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": 0,
      "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": 229
        }
      ]
    },
    {
      "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": "36b96d4b-a6f2-4a11-b137-23c97ed64097",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test9, Station9test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0003731055036529381"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epygHk_tIthF",
        "colab_type": "code",
        "outputId": "2805096f-862e-413b-8964-e43f26acdd36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        " r2_score(y_test9,Station9test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05083869158488008"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61KmudDqH_8S",
        "colab_type": "code",
        "outputId": "d127652a-1ade-467e-f2da-e79e410962ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y9, Station9)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0003731055036529381"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mLzi688I2Gt",
        "colab_type": "code",
        "outputId": "0e7b8543-fc94-4ebc-9f99-4fc46ecffb9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y9,Station9)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05083869158488008"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 234
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOrXpPJsIGpW",
        "colab_type": "code",
        "outputId": "e8d11595-41a3-408a-a285-0ab97beb7c0f",
        "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": 0,
      "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": 235
        }
      ]
    },
    {
      "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": "2688f8aa-26c3-44e1-8f93-69ef0302c9df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test10, Station10test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0027153769779543788"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "etTZW_1nIfyf",
        "colab_type": "code",
        "outputId": "fa528bba-12c2-4b61-b22d-0002f72b4b66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y10, Station10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0027153769779543788"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoVxsrebIpT-",
        "colab_type": "code",
        "outputId": "17ba073a-ce7f-4d75-c188-40b97b9debd1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y10,Station10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07341015671785667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfN_X25HI_-U",
        "colab_type": "code",
        "outputId": "f97ad071-5835-4a4e-bdc5-268867e9dd7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test10,Station10test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07341015671785667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CrgaGMyfJGWX",
        "colab_type": "code",
        "outputId": "1bfaf4b2-7b7d-4c15-9048-2313586674f0",
        "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": 0,
      "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": 241
        }
      ]
    },
    {
      "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": "ade36289-860e-4026-fbce-2e350545ee6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y11,Station11)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06414297205432207"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHxiubcEJrCU",
        "colab_type": "code",
        "outputId": "915c3f9f-5bb4-484b-bd72-8f9ad6694b88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test11,Station11test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06414297205432207"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0PXVnPiJw78",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VWpgScqJu29",
        "colab_type": "code",
        "outputId": "6abd8585-01b8-4b19-9fa8-8e52a7838822",
        "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": 0,
      "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": 245
        }
      ]
    },
    {
      "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": "df78dfde-85e0-4a52-cdda-3824d3c5a06c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y12,Station12)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0914261741666137"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTL8MpRqJ6El",
        "colab_type": "code",
        "outputId": "52c1492e-8022-4598-b8a6-9b3f321a99ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test12,Station12test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0914261741666137"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vy7__MV8J_LD",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RrowjLFxJ9Wj",
        "colab_type": "code",
        "outputId": "5362b988-6035-4db7-f2e5-921b68108ea1",
        "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": 0,
      "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": 249
        }
      ]
    },
    {
      "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": "d099f801-578a-4476-826d-53738d700673",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y13,Station13)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002752659355036118"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJJTc9s5KLLp",
        "colab_type": "code",
        "outputId": "ea033ecc-fb21-433d-9bd3-4d1ebe08178c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test13,Station13test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002752659355036118"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10PxmkIRKPpR",
        "colab_type": "code",
        "outputId": "ed033423-fee7-4712-c548-9aee7224ddd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y13,Station13)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0637360457677294"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyYAI4YGKVBR",
        "colab_type": "code",
        "outputId": "3c4ea7de-5cda-487a-977d-9edba2c01b9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test13,Station13test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0637360457677294"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vhQFmtJDKfFO",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZfdDR2RKY3g",
        "colab_type": "code",
        "outputId": "eb943d84-9a8f-4505-c792-f6038eb01438",
        "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": 0,
      "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": 255
        }
      ]
    },
    {
      "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": "cb8b024a-b42a-4c33-e7a9-2b68a86c0059",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test14,Station14test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0025257311743248486"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3urI3tUJKpEU",
        "colab_type": "code",
        "outputId": "8a9c4fb1-ef89-47c7-a6ec-bf0592cbb5b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y14,Station14)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0025257311743248486"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r299o65EKt1M",
        "colab_type": "code",
        "outputId": "0454427b-8497-4a55-e551-08b2872b1031",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test14,Station14test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05783074170702773"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLuUwwwxKxo6",
        "colab_type": "code",
        "outputId": "54d623e6-268d-451e-9564-fcf85eaef9c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y14,Station14)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05783074170702773"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hoxLwahK2Bw",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FARhlbtwK1P-",
        "colab_type": "code",
        "outputId": "5ce6f0e6-5197-46e0-86c1-33b3d1ad5fea",
        "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": 0,
      "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": 261
        }
      ]
    },
    {
      "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": "fce08680-37c4-4c8b-a6ae-d46b9ff47121",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y15,Station15)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003124769121008758"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jOLt7v7LELm",
        "colab_type": "code",
        "outputId": "b8487dd5-accf-4f79-b5fc-036f691718c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test15,Station15test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.003124769121008758"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYC0vqMRLJNo",
        "colab_type": "code",
        "outputId": "5d53140b-f950-4f18-8aed-9a586571ff45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test15,Station15test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.040951669611194785"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4bRvqpMePAD",
        "colab_type": "code",
        "outputId": "5c8da6e9-8344-4091-8f6f-8793ebc14332",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y15,Station15)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.040951669611194785"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Epe0LAwneWVq",
        "colab_type": "code",
        "outputId": "6c018767-0b58-4c70-bb60-7061ae83bc16",
        "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": 0,
      "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": 267
        }
      ]
    },
    {
      "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": "28275c0c-7e4b-481e-aa62-edb5aeae2cae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y16,Station16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002542597928429796"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gXIp4w6ehzY",
        "colab_type": "code",
        "outputId": "9ee2dd6f-ef57-4dfc-b7c5-27fc70020a59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test16,Station16test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.002542597928429796"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqF2CzEaely8",
        "colab_type": "code",
        "outputId": "5cb70f67-f44f-4a56-8d55-4e08f6fb5458",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y16,Station16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04163538022796531"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oQJNM6Hpeoo-",
        "colab_type": "code",
        "outputId": "3bacd5a1-c70f-48f5-9c74-149034cb20f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test16,Station16test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04163538022796531"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTsNsb0cewNj",
        "colab_type": "code",
        "outputId": "900765bb-620c-4ad1-fbbf-ac6b7f7d2219",
        "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": 0,
      "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": 273
        }
      ]
    },
    {
      "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": "9c3d9ba5-3f96-4a17-d5f6-6d503128b72a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y17,Station17)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0025413589453472623"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXSNWquPe8tR",
        "colab_type": "code",
        "outputId": "9e1ec501-9775-4b34-ba9e-672eaf5d0db0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test17,Station17test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0025413589453472623"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPlbPfb6fBWW",
        "colab_type": "code",
        "outputId": "cd8086fc-7bd4-43a6-f3d3-7203749bc263",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y17,Station17)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.052769203275740084"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SsNIB251fFBX",
        "colab_type": "code",
        "outputId": "08134256-9062-4bde-f192-8cae99e02db7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test17,Station17test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.052769203275740084"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3L_-aVDIfIG4",
        "colab_type": "code",
        "outputId": "10f947ea-ec6f-4f86-8c0f-f3ff58142518",
        "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": 0,
      "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": 279
        }
      ]
    },
    {
      "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": "123dc945-c827-403e-9ff3-91af36ff03b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y18,Station18)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00015727305957166573"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 281
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8NNvtbLfS4z",
        "colab_type": "code",
        "outputId": "19bee9b6-b108-489d-92ce-8951e14e7c9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_test18,Station18test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00015727305957166573"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHbhn-rFfWnw",
        "colab_type": "code",
        "outputId": "21d05a52-0b5c-44cb-a345-ff0177c923ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y18,Station18)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.008151568338115367"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 283
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I68KysRefZ0F",
        "colab_type": "code",
        "outputId": "7713d3c6-7e6d-4744-b81d-f10c20a6a276",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "r2_score(y_test18,Station18test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.008151568338115367"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 284
        }
      ]
    },
    {
      "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": "a9539bde-3f2c-4e40-ea1c-c3bac9f8e1db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y8,Station8)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.026323400032348444"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 286
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3H5Orw_gIho",
        "colab_type": "code",
        "outputId": "4631b0b2-49b2-4db6-e71a-bd4e229265f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y_test8,Station8test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.026323400032348444"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 287
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0JVHHLwNgM7E",
        "colab_type": "code",
        "outputId": "f25b018a-e18d-4e00-adef-0904293708d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y9,Station9)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.006786927554307694"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 288
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O-65BCvgP_W",
        "colab_type": "code",
        "outputId": "950410b3-d117-4546-e8fa-d155e197d146",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y_test9,Station9test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.006786927554307694"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 289
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gCWS7E63gT8r",
        "colab_type": "code",
        "outputId": "801060b3-3e77-42ae-823a-c2e4d36f1af3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y10,Station10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02230302166962082"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZKI8PjZgXdB",
        "colab_type": "code",
        "outputId": "716ced82-73a3-4d2b-9b2f-0d6a9bd109e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y_test10,Station10test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02230302166962082"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 291
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8_K6XGqgbBA",
        "colab_type": "code",
        "outputId": "cd5fc4d5-8e0e-49f5-d80f-0e021639712b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y11,Station11)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.021651486233572936"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 292
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5hbgqOogdiH",
        "colab_type": "code",
        "outputId": "e6f8afcf-a440-43dc-9c6f-ddd7cbdfbb21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y11,Station11test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.021651486233572936"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 293
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KHhriJ9Oggy7",
        "colab_type": "code",
        "outputId": "1df30875-6a7d-48d0-da66-78d4c2d067c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y11,Station11)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.021651486233572936"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bo8eZtb4m4bh",
        "colab_type": "code",
        "outputId": "b5f3303a-825e-4bc4-c7f8-ed48a00df161",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y_test11,Station11test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.021651486233572936"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 295
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KiriDospm7vw",
        "colab_type": "code",
        "outputId": "da224201-3e9e-4e33-8b57-07584bf4cc3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y12,Station12)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.017438594911746173"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 296
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teGy_xFmm-Si",
        "colab_type": "code",
        "outputId": "2bfd82ad-6a1b-481f-d2ed-4ceaf48fba9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y_test12,Station12test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.017438594911746173"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 297
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zi0tA59DnB9C",
        "colab_type": "code",
        "outputId": "9f9955a0-3380-437c-d37e-9856e8bd4775",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y13,Station13)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02175621681079423"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ukLy8UTnGzw",
        "colab_type": "code",
        "outputId": "c8c9c492-eb50-4c10-ac8f-04b580996e43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y_test13,Station13test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02175621681079423"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vz-x1-DLnKoy",
        "colab_type": "code",
        "outputId": "88f268ef-6d7b-472f-c779-9d0dcb422230",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_absolute_error(y14,Station14)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.020939251366835224"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 300
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmJDiCbqnNEp",
        "colab_type": "code",
        "outputId": "b13334f0-00b1-4efa-b609-ae92765a12de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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