All_crop_Model_Feature_Dataset.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Model and Data Analysis for All Crops Data"
      ],
      "metadata": {
        "id": "PePXrpUH2jZ3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "q-IKWBcfLXTl"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aNGyQlnvhTJX",
        "outputId": "4b027e78-4b06-4b69-ad3f-a520c3eaf623"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Read Dataset"
      ],
      "metadata": {
        "id": "HDYwKjBBCspK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Read the Processed data"
      ],
      "metadata": {
        "id": "OLiyqz0-AKAy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/drive/MyDrive/TDS_HW6/Data_ft_1961_2020.csv',index_col=0)\n",
        "df.head()"
      ],
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      "execution_count": 102,
      "outputs": [
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              "   Item Code (CPC)  Year  Area harvested  Production  Yield  Precipitation  \\\n",
              "0            111.0  1961      12927000.0  10997000.0 8507.0        1286.98   \n",
              "1            111.0  1962      13570000.0  12072000.0 8896.0        1169.53   \n",
              "2            111.0  1963      13590000.0  10776000.0 7929.0        1149.64   \n",
              "3            111.0  1964      13499000.0   9853000.0 7299.0        1189.87   \n",
              "4            111.0  1965      13422000.0  12257000.0 9132.0         953.51   \n",
              "\n",
              "   Mean Temperature  Minimum Temperature  Maximum Temperature  \n",
              "0             24.05                18.14                30.01  \n",
              "1              24.1                18.03                30.22  \n",
              "2             24.25                18.16                30.38  \n",
              "3             24.25                18.14                 30.4  \n",
              "4             24.34                18.23                 30.5  "
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              "      <td>12257000.0</td>\n",
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              "      <td>24.34</td>\n",
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              "   Item Code (CPC)  Year  Area harvested  Production  Yield  Precipitation  \\\n",
              "0            111.0  1961      12927000.0  10997000.0 8507.0        1286.98   \n",
              "1            111.0  1962      13570000.0  12072000.0 8896.0        1169.53   \n",
              "2            111.0  1963      13590000.0  10776000.0 7929.0        1149.64   \n",
              "3            111.0  1964      13499000.0   9853000.0 7299.0        1189.87   \n",
              "4            111.0  1965      13422000.0  12257000.0 9132.0         953.51   \n",
              "\n",
              "   Mean Temperature  Minimum Temperature  Maximum Temperature  \n",
              "0             24.05                18.14                30.01  \n",
              "1              24.1                18.03                30.22  \n",
              "2             24.25                18.16                30.38  \n",
              "3             24.25                18.14                 30.4  \n",
              "4             24.34                18.23                 30.5  "
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              "      <td>1961</td>\n",
              "      <td>12927000.0</td>\n",
              "      <td>10997000.0</td>\n",
              "      <td>8507.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>111.0</td>\n",
              "      <td>1962</td>\n",
              "      <td>13570000.0</td>\n",
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              "      <td>111.0</td>\n",
              "      <td>1963</td>\n",
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              "      <td>30.38</td>\n",
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              "      <th>3</th>\n",
              "      <td>111.0</td>\n",
              "      <td>1964</td>\n",
              "      <td>13499000.0</td>\n",
              "      <td>9853000.0</td>\n",
              "      <td>7299.0</td>\n",
              "      <td>1189.87</td>\n",
              "      <td>24.25</td>\n",
              "      <td>18.14</td>\n",
              "      <td>30.4</td>\n",
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              "      <th>4</th>\n",
              "      <td>111.0</td>\n",
              "      <td>1965</td>\n",
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              "      <td>12257000.0</td>\n",
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              "      <td>953.51</td>\n",
              "      <td>24.34</td>\n",
              "      <td>18.23</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Label Encoding the Item Code(Differs for different crops)"
      ],
      "metadata": {
        "id": "V3uEXy_R_aXD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "  \n",
        "# label_encoder object knows how to understand word labels.\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "  \n",
        "# Encode labels in column 'Item Code (CPC)'.\n",
        "df['Item Code (Encoded)']= label_encoder.fit_transform(df['Item Code (CPC)'])\n",
        "  \n",
        "df['Item Code (Encoded)'].unique()"
      ],
      "metadata": {
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        },
        "id": "GqGd8fmy_Z_J",
        "outputId": "e284fa57-e866-4452-f3f3-1be65cfeabed"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
              "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
              "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,  6,  7, 48, 49, 50,\n",
              "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
              "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79])"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "py862Cub_yFW",
        "outputId": "d6cda9da-cc92-4dce-f68a-b3186d02a06d"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Item Code (CPC)  Year  Area harvested  Production  Yield  Precipitation  \\\n",
              "0            111.0  1961      12927000.0  10997000.0 8507.0        1286.98   \n",
              "1            111.0  1962      13570000.0  12072000.0 8896.0        1169.53   \n",
              "2            111.0  1963      13590000.0  10776000.0 7929.0        1149.64   \n",
              "3            111.0  1964      13499000.0   9853000.0 7299.0        1189.87   \n",
              "4            111.0  1965      13422000.0  12257000.0 9132.0         953.51   \n",
              "\n",
              "   Mean Temperature  Minimum Temperature  Maximum Temperature  \\\n",
              "0             24.05                18.14                30.01   \n",
              "1              24.1                18.03                30.22   \n",
              "2             24.25                18.16                30.38   \n",
              "3             24.25                18.14                 30.4   \n",
              "4             24.34                18.23                 30.5   \n",
              "\n",
              "   Item Code (Encoded)  \n",
              "0                    0  \n",
              "1                    0  \n",
              "2                    0  \n",
              "3                    0  \n",
              "4                    0  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item Code (CPC)</th>\n",
              "      <th>Year</th>\n",
              "      <th>Area harvested</th>\n",
              "      <th>Production</th>\n",
              "      <th>Yield</th>\n",
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              "      <th>Minimum Temperature</th>\n",
              "      <th>Maximum Temperature</th>\n",
              "      <th>Item Code (Encoded)</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111.0</td>\n",
              "      <td>1961</td>\n",
              "      <td>12927000.0</td>\n",
              "      <td>10997000.0</td>\n",
              "      <td>8507.0</td>\n",
              "      <td>1286.98</td>\n",
              "      <td>24.05</td>\n",
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              "      <td>30.01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>111.0</td>\n",
              "      <td>1962</td>\n",
              "      <td>13570000.0</td>\n",
              "      <td>12072000.0</td>\n",
              "      <td>8896.0</td>\n",
              "      <td>1169.53</td>\n",
              "      <td>24.1</td>\n",
              "      <td>18.03</td>\n",
              "      <td>30.22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>111.0</td>\n",
              "      <td>1963</td>\n",
              "      <td>13590000.0</td>\n",
              "      <td>10776000.0</td>\n",
              "      <td>7929.0</td>\n",
              "      <td>1149.64</td>\n",
              "      <td>24.25</td>\n",
              "      <td>18.16</td>\n",
              "      <td>30.38</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>111.0</td>\n",
              "      <td>1964</td>\n",
              "      <td>13499000.0</td>\n",
              "      <td>9853000.0</td>\n",
              "      <td>7299.0</td>\n",
              "      <td>1189.87</td>\n",
              "      <td>24.25</td>\n",
              "      <td>18.14</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>111.0</td>\n",
              "      <td>1965</td>\n",
              "      <td>13422000.0</td>\n",
              "      <td>12257000.0</td>\n",
              "      <td>9132.0</td>\n",
              "      <td>953.51</td>\n",
              "      <td>24.34</td>\n",
              "      <td>18.23</td>\n",
              "      <td>30.5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          const element = document.querySelector('#df-09a6a00f-3033-44db-97cf-629d41aedb66');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Analysis"
      ],
      "metadata": {
        "id": "qkN_Ap1RJy1e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z90ZdfAyJ1-z",
        "outputId": "6dc702ca-a76e-4da2-950a-2a5eca64764a"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 4718 entries, 0 to 4717\n",
            "Data columns (total 10 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Item Code (CPC)      4718 non-null   float64\n",
            " 1   Year                 4718 non-null   int64  \n",
            " 2   Area harvested       4718 non-null   float64\n",
            " 3   Production           4718 non-null   float64\n",
            " 4   Yield                4718 non-null   float64\n",
            " 5   Precipitation        4718 non-null   float64\n",
            " 6   Mean Temperature     4718 non-null   float64\n",
            " 7   Minimum Temperature  4718 non-null   float64\n",
            " 8   Maximum Temperature  4718 non-null   float64\n",
            " 9   Item Code (Encoded)  4718 non-null   int64  \n",
            "dtypes: float64(8), int64(2)\n",
            "memory usage: 405.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Null Values in Columns"
      ],
      "metadata": {
        "id": "xS0A4sLiKcBv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "osQcOYfaJ86j",
        "outputId": "c3763c14-467b-4bda-ab33-7c5299e7f71b"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Item Code (CPC)        0\n",
            "Year                   0\n",
            "Area harvested         0\n",
            "Production             0\n",
            "Yield                  0\n",
            "Precipitation          0\n",
            "Mean Temperature       0\n",
            "Minimum Temperature    0\n",
            "Maximum Temperature    0\n",
            "Item Code (Encoded)    0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "sGW3CtlcKMfL",
        "outputId": "c8134c8d-478d-47af-b821-c9a5192ede5c"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Item Code (CPC)               Year    Area harvested  \\\n",
              "count             4718.0             4718.0            4718.0   \n",
              "mean  1316.3563798219586  1990.765154726579 2231011.480712166   \n",
              "std   449.23210351379134 17.229497507539712  5894296.12563013   \n",
              "min                111.0             1961.0             200.0   \n",
              "25%               1241.9             1976.0          60641.25   \n",
              "50%              1344.02             1991.0          308000.0   \n",
              "75%               1620.0             2006.0         1103350.0   \n",
              "max               1970.0             2020.0        45537400.0   \n",
              "\n",
              "              Production             Yield     Precipitation  \\\n",
              "count             4718.0            4718.0            4718.0   \n",
              "mean   7608293.338914795 65867.33976261127 1112.558969902501   \n",
              "std   30615487.820160855 92492.85094674396 97.88110556196311   \n",
              "min                300.0             382.0            874.32   \n",
              "25%            165080.75           7793.25           1034.04   \n",
              "50%             794850.0           28231.0           1114.26   \n",
              "75%            3755400.0          94921.25           1187.06   \n",
              "max          405416180.0          801984.0           1320.91   \n",
              "\n",
              "        Mean Temperature  Minimum Temperature  Maximum Temperature  \\\n",
              "count             4718.0               4718.0               4718.0   \n",
              "mean  24.602513777024164    18.62940652818991   30.623291649003814   \n",
              "std   0.3556392026242085   0.3854497848345317   0.3428157175743565   \n",
              "min                23.82                 17.8                 29.9   \n",
              "25%                24.32                 18.3                30.38   \n",
              "50%                24.62                18.66                30.64   \n",
              "75%                24.83                18.89                30.88   \n",
              "max                25.48                19.49                31.52   \n",
              "\n",
              "       Item Code (Encoded)  \n",
              "count               4718.0  \n",
              "mean     39.37049597286986  \n",
              "std       23.2474607066372  \n",
              "min                    0.0  \n",
              "25%                   19.0  \n",
              "50%                   39.0  \n",
              "75%                   60.0  \n",
              "max                   79.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c6b5ea62-e602-4458-b7f1-e20235cf7db3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Item Code (CPC)</th>\n",
              "      <th>Year</th>\n",
              "      <th>Area harvested</th>\n",
              "      <th>Production</th>\n",
              "      <th>Yield</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>Mean Temperature</th>\n",
              "      <th>Minimum Temperature</th>\n",
              "      <th>Maximum Temperature</th>\n",
              "      <th>Item Code (Encoded)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4718.0</td>\n",
              "      <td>4718.0</td>\n",
              "      <td>4718.0</td>\n",
              "      <td>4718.0</td>\n",
              "      <td>4718.0</td>\n",
              "      <td>4718.0</td>\n",
              "      <td>4718.0</td>\n",
              "      <td>4718.0</td>\n",
              "      <td>4718.0</td>\n",
              "      <td>4718.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1316.3563798219586</td>\n",
              "      <td>1990.765154726579</td>\n",
              "      <td>2231011.480712166</td>\n",
              "      <td>7608293.338914795</td>\n",
              "      <td>65867.33976261127</td>\n",
              "      <td>1112.558969902501</td>\n",
              "      <td>24.602513777024164</td>\n",
              "      <td>18.62940652818991</td>\n",
              "      <td>30.623291649003814</td>\n",
              "      <td>39.37049597286986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>449.23210351379134</td>\n",
              "      <td>17.229497507539712</td>\n",
              "      <td>5894296.12563013</td>\n",
              "      <td>30615487.820160855</td>\n",
              "      <td>92492.85094674396</td>\n",
              "      <td>97.88110556196311</td>\n",
              "      <td>0.3556392026242085</td>\n",
              "      <td>0.3854497848345317</td>\n",
              "      <td>0.3428157175743565</td>\n",
              "      <td>23.2474607066372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>111.0</td>\n",
              "      <td>1961.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>300.0</td>\n",
              "      <td>382.0</td>\n",
              "      <td>874.32</td>\n",
              "      <td>23.82</td>\n",
              "      <td>17.8</td>\n",
              "      <td>29.9</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1241.9</td>\n",
              "      <td>1976.0</td>\n",
              "      <td>60641.25</td>\n",
              "      <td>165080.75</td>\n",
              "      <td>7793.25</td>\n",
              "      <td>1034.04</td>\n",
              "      <td>24.32</td>\n",
              "      <td>18.3</td>\n",
              "      <td>30.38</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1344.02</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>308000.0</td>\n",
              "      <td>794850.0</td>\n",
              "      <td>28231.0</td>\n",
              "      <td>1114.26</td>\n",
              "      <td>24.62</td>\n",
              "      <td>18.66</td>\n",
              "      <td>30.64</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1620.0</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>1103350.0</td>\n",
              "      <td>3755400.0</td>\n",
              "      <td>94921.25</td>\n",
              "      <td>1187.06</td>\n",
              "      <td>24.83</td>\n",
              "      <td>18.89</td>\n",
              "      <td>30.88</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1970.0</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>45537400.0</td>\n",
              "      <td>405416180.0</td>\n",
              "      <td>801984.0</td>\n",
              "      <td>1320.91</td>\n",
              "      <td>25.48</td>\n",
              "      <td>19.49</td>\n",
              "      <td>31.52</td>\n",
              "      <td>79.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6b5ea62-e602-4458-b7f1-e20235cf7db3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c6b5ea62-e602-4458-b7f1-e20235cf7db3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c6b5ea62-e602-4458-b7f1-e20235cf7db3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature and Label"
      ],
      "metadata": {
        "id": "d4d9IRFSVL0x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=df.loc[:,['Item Code (Encoded)','Area harvested','Production','Precipitation','Mean Temperature',\t'Minimum Temperature','Maximum Temperature']]\n",
        "Y=df.loc[:,['Yield']]"
      ],
      "metadata": {
        "id": "ij2SVrJDT_QF"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Importance"
      ],
      "metadata": {
        "id": "XushWUHATvgf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import SelectKBest,chi2\n",
        "bestfeatures = SelectKBest(score_func=chi2, k=7)\n",
        "fit = bestfeatures.fit(X,Y)"
      ],
      "metadata": {
        "id": "d36SHKv_Kgig"
      },
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dfscores = pd.DataFrame(fit.scores_)\n",
        "dfcolumns = pd.DataFrame(X.columns)"
      ],
      "metadata": {
        "id": "vKxhimmjT1XY"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
        "featureScores.columns = ['Specs','Score']  \n",
        "print(featureScores.nlargest(5,'Score'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUy1-R2hURwu",
        "outputId": "8959d4fb-13dc-4459-a180-cae59fdd4d06"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Specs              Score\n",
            "2           Production  580207624225.1035\n",
            "1       Area harvested   71943403314.0281\n",
            "0  Item Code (Encoded)  62427.58775452292\n",
            "3        Precipitation  35317.03818532293\n",
            "5  Minimum Temperature 34.873492713652126\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Normalize/Standardize the Dataset"
      ],
      "metadata": {
        "id": "IzrJcWpwJNEt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#norm = MinMaxScaler()\n",
        "norm =StandardScaler()\n",
        "\n",
        "# transform data\n",
        "df.iloc[:,1:] = norm.fit_transform(df.iloc[:,1:])"
      ],
      "metadata": {
        "id": "i5GMaj7qJM3P"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "ET-bf3yPK4eY",
        "outputId": "cbda38fe-c4d4-4114-c0a8-0e39e2cc88dd"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Item Code (CPC)                Year     Area harvested          Production  \\\n",
              "0            111.0  -1.727752631214661  1.814826062596067 0.11069775740392183   \n",
              "1            111.0 -1.6697064813765132  1.923926138399717  0.1458144261246978   \n",
              "2            111.0 -1.6116603315383655 1.9273196088757247 0.10347842364830186   \n",
              "3            111.0 -1.5536141817002178 1.9118793182098894 0.07332708855130073   \n",
              "4            111.0   -1.49556803186207 1.8988144568772596 0.15185775981152902   \n",
              "\n",
              "                Yield       Precipitation    Mean Temperature  \\\n",
              "0 -0.6202254447224523  1.7821572066584537 -1.5537441768771438   \n",
              "1 -0.6160192684864758  0.5821048746372999 -1.4131373502704279   \n",
              "2 -0.6264752387183249  0.3788776214750981 -0.9913168704502899   \n",
              "3 -0.6332872979179783  0.7899300294547554 -0.9913168704502899   \n",
              "4 -0.6134674494847009 -1.6250922710690392 -0.7382245825582051   \n",
              "\n",
              "   Minimum Temperature  Maximum Temperature  Item Code (Encoded)  \n",
              "0  -1.2698370101188048    -1.78917308270866  -1.6937191336493012  \n",
              "1  -1.5552481419026156  -1.1765341400797868  -1.6937191336493012  \n",
              "2  -1.2179440770672036  -0.7097616123625439  -1.6937191336493012  \n",
              "3  -1.2698370101188048  -0.6514150463978898  -1.6937191336493012  \n",
              "4   -1.036318811386595  -0.3596822165746091  -1.6937191336493012  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-be626e53-197f-42a6-82f5-b84a977df455\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Item Code (CPC)</th>\n",
              "      <th>Year</th>\n",
              "      <th>Area harvested</th>\n",
              "      <th>Production</th>\n",
              "      <th>Yield</th>\n",
              "      <th>Precipitation</th>\n",
              "      <th>Mean Temperature</th>\n",
              "      <th>Minimum Temperature</th>\n",
              "      <th>Maximum Temperature</th>\n",
              "      <th>Item Code (Encoded)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>111.0</td>\n",
              "      <td>-1.727752631214661</td>\n",
              "      <td>1.814826062596067</td>\n",
              "      <td>0.11069775740392183</td>\n",
              "      <td>-0.6202254447224523</td>\n",
              "      <td>1.7821572066584537</td>\n",
              "      <td>-1.5537441768771438</td>\n",
              "      <td>-1.2698370101188048</td>\n",
              "      <td>-1.78917308270866</td>\n",
              "      <td>-1.6937191336493012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>111.0</td>\n",
              "      <td>-1.6697064813765132</td>\n",
              "      <td>1.923926138399717</td>\n",
              "      <td>0.1458144261246978</td>\n",
              "      <td>-0.6160192684864758</td>\n",
              "      <td>0.5821048746372999</td>\n",
              "      <td>-1.4131373502704279</td>\n",
              "      <td>-1.5552481419026156</td>\n",
              "      <td>-1.1765341400797868</td>\n",
              "      <td>-1.6937191336493012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>111.0</td>\n",
              "      <td>-1.6116603315383655</td>\n",
              "      <td>1.9273196088757247</td>\n",
              "      <td>0.10347842364830186</td>\n",
              "      <td>-0.6264752387183249</td>\n",
              "      <td>0.3788776214750981</td>\n",
              "      <td>-0.9913168704502899</td>\n",
              "      <td>-1.2179440770672036</td>\n",
              "      <td>-0.7097616123625439</td>\n",
              "      <td>-1.6937191336493012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>111.0</td>\n",
              "      <td>-1.5536141817002178</td>\n",
              "      <td>1.9118793182098894</td>\n",
              "      <td>0.07332708855130073</td>\n",
              "      <td>-0.6332872979179783</td>\n",
              "      <td>0.7899300294547554</td>\n",
              "      <td>-0.9913168704502899</td>\n",
              "      <td>-1.2698370101188048</td>\n",
              "      <td>-0.6514150463978898</td>\n",
              "      <td>-1.6937191336493012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>111.0</td>\n",
              "      <td>-1.49556803186207</td>\n",
              "      <td>1.8988144568772596</td>\n",
              "      <td>0.15185775981152902</td>\n",
              "      <td>-0.6134674494847009</td>\n",
              "      <td>-1.6250922710690392</td>\n",
              "      <td>-0.7382245825582051</td>\n",
              "      <td>-1.036318811386595</td>\n",
              "      <td>-0.3596822165746091</td>\n",
              "      <td>-1.6937191336493012</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be626e53-197f-42a6-82f5-b84a977df455')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-be626e53-197f-42a6-82f5-b84a977df455 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-be626e53-197f-42a6-82f5-b84a977df455');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Correlation Matrix (Heatmap)"
      ],
      "metadata": {
        "id": "fk_u4YxiEwA4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sb\n",
        "plt.figure(figsize = (16,5))\n",
        "\n",
        "#ax = sb.heatmap(df1.iloc[:, 1:6:], annot=True, linewidths=.5)\n",
        "corr = df.corr()\n",
        "ax= sb.heatmap(corr, cmap=\"BrBG\",annot=True, linewidths=.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "2og6suuqEsHn",
        "outputId": "91f208ec-9b21-4c40-d2a4-a741b223ba29"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using only the Features with High Importance and less Redundance\n",
        "\n",
        "X=X[['Area harvested','Production','Precipitation','Minimum Temperature']]"
      ],
      "metadata": {
        "id": "wXcAUV4SvnsO"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train test split"
      ],
      "metadata": {
        "id": "xVQdO40VB00q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=42)"
      ],
      "metadata": {
        "id": "16yCpbG784fo"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We use Mean Squared Error and R2 score to compare and evaluate our models.\n",
        "\n",
        "**R2 Score:**\n",
        "The r2 score varies between 0 and 100%. It is closely related to the MSE.\n",
        "\n",
        "It is  (total variance explained by model) / total variance. So if it is 100%, the two variables are perfectly correlated, i.e., with no variance at all. A low value would show a low level of correlation, meaning a regression model that is not valid, but not in all cases.\n",
        "\n",
        "**Mean Squared Error:**\n",
        "Mean square error (MSE) is the average of the square of the errors. The larger the number the larger the error. Error in this case means the difference between the observed values y1, y2, y3, … and the predicted ones pred(y1), pred(y2), pred(y3), … We square each difference (pred(yn) – yn)) ** 2."
      ],
      "metadata": {
        "id": "bEfMIneYHdJX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Regression"
      ],
      "metadata": {
        "id": "9QNdgzy3B6Iw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "regr1 = LinearRegression()\n",
        "\n",
        "regr1.fit(X_train, y_train)\n",
        "\n",
        "print(regr1.score(X_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xkyqVmNG-Ckz",
        "outputId": "3480330b-135c-4b35-aaf9-72f2dae9f7f2"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5191615539423922\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred1=regr1.predict(X_test)"
      ],
      "metadata": {
        "id": "P9PMdpRLFMXw"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error,accuracy_score\n",
        "\n",
        "print('Coefficient of Determination (R2 Score) =',r2_score(y_test, y_pred1))\n",
        "\n",
        "print('Mean Squared Error = ',mean_squared_error(y_test, y_pred1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CIyQFv_5FBJw",
        "outputId": "8b7be101-a004-4f01-f46e-2e24e9c823ea"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficient of Determination (R2 Score) = 0.5191615539423922\n",
            "Mean Squared Error =  4408763690.649902\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bayesian Ridge regressor"
      ],
      "metadata": {
        "id": "UWmIO9TXCCma"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import BayesianRidge\n",
        "\n",
        "regr2 = BayesianRidge().fit(X_train, y_train)\n",
        "\n",
        "regr2.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RIohf-gWA1OB",
        "outputId": "adbb0966-e22c-4f39-b8db-33f5426e585a"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5154728882541151"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred2=regr2.predict(X_test)\n",
        "\n",
        "print('Coefficient of Determination (R2 Score)=',r2_score(y_test, y_pred2))\n",
        "\n",
        "print('Mean Squared Error= ',mean_squared_error(y_test, y_pred2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7GYwNkpFber",
        "outputId": "0bc1aff4-4681-45fc-b890-99a6a9623cb1"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficient of Determination (R2 Score)= 0.5154728882541151\n",
            "Mean Squared Error=  4442584728.644594\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Tree Regressor"
      ],
      "metadata": {
        "id": "hkUiND2LOM-D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "regr3 = DecisionTreeRegressor(max_depth=2).fit(X_train, y_train)\n",
        "\n",
        "regr3.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9wt8vHkMOPlY",
        "outputId": "b82abb81-7120-4ef5-e45e-33dcb4ad34b1"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.36654305662693265"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred3=regr3.predict(X_test)\n",
        "\n",
        "print('Coefficient of Determination (R2 Score)=',r2_score(y_test, y_pred3))\n",
        "\n",
        "print('Mean Squared Error= ',mean_squared_error(y_test, y_pred3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAGqu102OzDY",
        "outputId": "87c9362a-cac4-47c1-e3a2-5a8de3a02eb0"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficient of Determination (R2 Score)= 0.36654305662693265\n",
            "Mean Squared Error=  5808108720.155581\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Lasso Regression"
      ],
      "metadata": {
        "id": "b7B11iS8O4ga"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n",
        "\n",
        "regr4 = linear_model.Lasso(alpha=0.1).fit(X_train, y_train)\n",
        "\n",
        "regr4.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T8V-_n03O86v",
        "outputId": "09bc727f-19a0-49e4-acb5-da09de41e188"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5191612235135079"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred4=regr4.predict(X_test)\n",
        "\n",
        "print('Coefficient of Determination (R2 Score)=',r2_score(y_test, y_pred4))\n",
        "\n",
        "print('Mean Squared Error= ',mean_squared_error(y_test, y_pred4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4rDmczSPKUm",
        "outputId": "f4a65f4d-3f5c-46c2-b8d4-8a6d96208da4"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficient of Determination (R2 Score)= 0.5191612235135079\n",
            "Mean Squared Error=  4408766720.322092\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest Regressor"
      ],
      "metadata": {
        "id": "rAGqx8l4PT2C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "regr5 = RandomForestRegressor(max_depth=3).fit(X_train, y_train)\n",
        "\n",
        "regr5.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0i2qs2gkPQWj",
        "outputId": "9ec031fc-1b12-42b9-c6eb-2966808eff55"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5932759295585954"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred5=regr5.predict(X_test)\n",
        "\n",
        "print('Coefficient of Determination (R2 Score)=',r2_score(y_test, y_pred5))\n",
        "\n",
        "print('Mean Squared Error= ',mean_squared_error(y_test, y_pred5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dpEvIt-NPgoG",
        "outputId": "192e8321-e0a9-4883-a02b-15aaf7936dc7"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficient of Determination (R2 Score)= 0.5932759295585954\n",
            "Mean Squared Error=  3729215765.8719454\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Polynomial Regression"
      ],
      "metadata": {
        "id": "vchPNK4BTIHF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "poly_reg=PolynomialFeatures(degree=2)\n",
        "\n",
        "X_poly=poly_reg.fit_transform(X_train)\n",
        "\n",
        "regr6=LinearRegression().fit(X_poly,y_train)\n"
      ],
      "metadata": {
        "id": "FNdAHJXtTLiV"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_poly=poly_reg.fit_transform(X_test)\n",
        "\n",
        "regr6.score(X_test_poly, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EbKhrS1KVpPv",
        "outputId": "0ac014ef-29d5-472d-ffc5-bb10c25db1cd"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6024162546543175"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred6=regr6.predict(X_test_poly)\n",
        "\n",
        "print('Coefficient of Determination (R2 Score)=',r2_score(y_test, y_pred6))\n",
        "\n",
        "print('Mean Squared Error= ',mean_squared_error(y_test, y_pred6))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3tMFCK8TZxm",
        "outputId": "92607863-5d28-43d9-c693-ef479c33cdee"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficient of Determination (R2 Score)= 0.6024162546543175\n",
            "Mean Squared Error=  3645408961.8753953\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# K nearest neighbors"
      ],
      "metadata": {
        "id": "llyNTqalFubj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import neighbors\n",
        "from math import sqrt\n",
        "\n",
        "mse_val = [] \n",
        "r2_val=[]\n",
        "\n",
        "# To find the Optimum K value for KNN Algorithm we use different K values on the model and obsereve the errors to find the best\n",
        "for K in range(20):\n",
        "    K = K+1\n",
        "    regr7 = neighbors.KNeighborsRegressor(n_neighbors = K)\n",
        "\n",
        "    regr7.fit(X_train, y_train)  #fit the model\n",
        "    y_pred7=regr7.predict(X_test) #make prediction on test set\n",
        "    #error = mean_squared_error(y_test,y_pred9) #calculate rmse\n",
        "    mse_val.append(mean_squared_error(y_test,y_pred7)) #store mse values\n",
        "    r2_val.append(r2_score(y_test, y_pred7))\n",
        "    print('MSE value for k= ' , K , 'is:', mean_squared_error(y_test,y_pred7))\n",
        "    print('R2 score value for k= ' , K , 'is:', r2_score(y_test, y_pred7))\n",
        "    print('\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQy---QxFrs-",
        "outputId": "13fde84c-37de-45e0-dcae-ec85d4caad5c"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE value for k=  1 is: 318997309.1779062\n",
            "R2 score value for k=  1 is: 0.9652088020124635\n",
            "\n",
            "\n",
            "MSE value for k=  2 is: 374814557.8142261\n",
            "R2 score value for k=  2 is: 0.9591211364035266\n",
            "\n",
            "\n",
            "MSE value for k=  3 is: 405621224.9627489\n",
            "R2 score value for k=  3 is: 0.9557612307702705\n",
            "\n",
            "\n",
            "MSE value for k=  4 is: 455244870.51589596\n",
            "R2 score value for k=  4 is: 0.9503490657531042\n",
            "\n",
            "\n",
            "MSE value for k=  5 is: 518099197.0847784\n",
            "R2 score value for k=  5 is: 0.9434939066118975\n",
            "\n",
            "\n",
            "MSE value for k=  6 is: 570174658.1305217\n",
            "R2 score value for k=  6 is: 0.9378143362098654\n",
            "\n",
            "\n",
            "MSE value for k=  7 is: 626710997.8142031\n",
            "R2 score value for k=  7 is: 0.9316482434848369\n",
            "\n",
            "\n",
            "MSE value for k=  8 is: 678946389.7455543\n",
            "R2 score value for k=  8 is: 0.925951230342865\n",
            "\n",
            "\n",
            "MSE value for k=  9 is: 726066828.6236907\n",
            "R2 score value for k=  9 is: 0.9208120756506397\n",
            "\n",
            "\n",
            "MSE value for k=  10 is: 755169291.9505907\n",
            "R2 score value for k=  10 is: 0.9176380377061174\n",
            "\n",
            "\n",
            "MSE value for k=  11 is: 796898505.5969203\n",
            "R2 score value for k=  11 is: 0.9130868728778775\n",
            "\n",
            "\n",
            "MSE value for k=  12 is: 838824205.5731285\n",
            "R2 score value for k=  12 is: 0.9085142784180765\n",
            "\n",
            "\n",
            "MSE value for k=  13 is: 891462965.7394208\n",
            "R2 score value for k=  13 is: 0.9027732722275116\n",
            "\n",
            "\n",
            "MSE value for k=  14 is: 942571746.9911131\n",
            "R2 score value for k=  14 is: 0.8971991320192076\n",
            "\n",
            "\n",
            "MSE value for k=  15 is: 996114474.9270592\n",
            "R2 score value for k=  15 is: 0.891359535273978\n",
            "\n",
            "\n",
            "MSE value for k=  16 is: 1056950894.4142231\n",
            "R2 score value for k=  16 is: 0.8847244576280713\n",
            "\n",
            "\n",
            "MSE value for k=  17 is: 1122685983.159583\n",
            "R2 score value for k=  17 is: 0.8775551103594\n",
            "\n",
            "\n",
            "MSE value for k=  18 is: 1185559460.4170218\n",
            "R2 score value for k=  18 is: 0.8706978625629666\n",
            "\n",
            "\n",
            "MSE value for k=  19 is: 1242472968.2908676\n",
            "R2 score value for k=  19 is: 0.864490634277226\n",
            "\n",
            "\n",
            "MSE value for k=  20 is: 1298009098.9678051\n",
            "R2 score value for k=  20 is: 0.8584336285839101\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max(r2_val),min(mse_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TuvTg113GX_R",
        "outputId": "f433a459-7489-4cc3-abd3-1225d3d88417"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9652088020124635, 318997309.1779062)"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANN"
      ],
      "metadata": {
        "id": "VzmRP-LUefC-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "\n",
        "from keras.losses import MeanSquaredError\n",
        "from keras.optimizers import Adam\n",
        "\n",
        "learning_rate = 0.01\n",
        "\n",
        "ann = tf.keras.models.Sequential()\n",
        "\n",
        "ann.add(tf.keras.layers.Dense(units=16,activation=\"relu\"))\n",
        "\n",
        "#Adding Second Hidden Layer\n",
        "ann.add(tf.keras.layers.Dense(units=8,activation=\"relu\"))\n",
        "\n",
        "#Adding third Hidden Layer\n",
        "ann.add(tf.keras.layers.Dense(units=2,activation=\"relu\"))  #kernel_initializer='normal', activation='linear'\n",
        "\n",
        "#Adding Output Layer\n",
        "ann.add(tf.keras.layers.Dense(units=1,kernel_initializer='normal', activation='linear'))\n",
        "\n",
        "msle =  MeanSquaredError()\n",
        "\n",
        "ann.compile(\n",
        "    loss=msle, \n",
        "    optimizer=Adam(learning_rate=learning_rate), \n",
        "    metrics=[msle]\n",
        ")"
      ],
      "metadata": {
        "id": "E_kLz29fjvEz"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = ann.fit(\n",
        "    X_train, y_train, \n",
        "    epochs=100, \n",
        "    batch_size=64,\n",
        "    validation_split=0.2\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U7Wy3vENepQK",
        "outputId": "1bc5228b-2b99-4d2c-8c59-511e54bb53ef"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "40/40 [==============================] - 1s 9ms/step - loss: 10789874688.0000 - mean_squared_error: 10890635264.0000 - val_loss: 7844660224.0000 - val_mean_squared_error: 7841008640.0000\n",
            "Epoch 2/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 8251267072.0000 - mean_squared_error: 8217254912.0000 - val_loss: 9917744128.0000 - val_mean_squared_error: 9866037248.0000\n",
            "Epoch 3/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 10377972736.0000 - mean_squared_error: 10357252096.0000 - val_loss: 7768430080.0000 - val_mean_squared_error: 7740983808.0000\n",
            "Epoch 4/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7740884992.0000 - mean_squared_error: 7697009152.0000 - val_loss: 6624812544.0000 - val_mean_squared_error: 6611902464.0000\n",
            "Epoch 5/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7575554048.0000 - mean_squared_error: 7649223680.0000 - val_loss: 6290376192.0000 - val_mean_squared_error: 6281854464.0000\n",
            "Epoch 6/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7621381120.0000 - mean_squared_error: 7575179264.0000 - val_loss: 7000207360.0000 - val_mean_squared_error: 6981701632.0000\n",
            "Epoch 7/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7222326784.0000 - mean_squared_error: 7208515072.0000 - val_loss: 6265615360.0000 - val_mean_squared_error: 6257183744.0000\n",
            "Epoch 8/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 7189046272.0000 - mean_squared_error: 7187959808.0000 - val_loss: 7623409152.0000 - val_mean_squared_error: 7597289472.0000\n",
            "Epoch 9/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 8212143616.0000 - mean_squared_error: 8228011008.0000 - val_loss: 6491109376.0000 - val_mean_squared_error: 6478651904.0000\n",
            "Epoch 10/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7495252992.0000 - mean_squared_error: 7537688576.0000 - val_loss: 6701418496.0000 - val_mean_squared_error: 6689830400.0000\n",
            "Epoch 11/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7460474880.0000 - mean_squared_error: 7420589568.0000 - val_loss: 6456997888.0000 - val_mean_squared_error: 6444754944.0000\n",
            "Epoch 12/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 7228233216.0000 - mean_squared_error: 7186187264.0000 - val_loss: 6194520064.0000 - val_mean_squared_error: 6186803200.0000\n",
            "Epoch 13/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 8208214016.0000 - mean_squared_error: 8190127104.0000 - val_loss: 7279718400.0000 - val_mean_squared_error: 7262643200.0000\n",
            "Epoch 14/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7849367040.0000 - mean_squared_error: 7850723840.0000 - val_loss: 7257617920.0000 - val_mean_squared_error: 7235111424.0000\n",
            "Epoch 15/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 7704754176.0000 - mean_squared_error: 7638357504.0000 - val_loss: 6266232320.0000 - val_mean_squared_error: 6257975296.0000\n",
            "Epoch 16/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7210520576.0000 - mean_squared_error: 7212085248.0000 - val_loss: 7183868928.0000 - val_mean_squared_error: 7162023936.0000\n",
            "Epoch 17/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7356686336.0000 - mean_squared_error: 7295576576.0000 - val_loss: 6114910208.0000 - val_mean_squared_error: 6107364352.0000\n",
            "Epoch 18/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7555207680.0000 - mean_squared_error: 7573600768.0000 - val_loss: 6618159616.0000 - val_mean_squared_error: 6606885376.0000\n",
            "Epoch 19/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7923101696.0000 - mean_squared_error: 7948114944.0000 - val_loss: 6101729792.0000 - val_mean_squared_error: 6093672448.0000\n",
            "Epoch 20/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 7083682304.0000 - mean_squared_error: 7177244672.0000 - val_loss: 6339542016.0000 - val_mean_squared_error: 6330511872.0000\n",
            "Epoch 21/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7180168704.0000 - mean_squared_error: 7220571136.0000 - val_loss: 6420528128.0000 - val_mean_squared_error: 6410698240.0000\n",
            "Epoch 22/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7104582656.0000 - mean_squared_error: 7094560256.0000 - val_loss: 6084621312.0000 - val_mean_squared_error: 6076178432.0000\n",
            "Epoch 23/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6921540608.0000 - mean_squared_error: 6904580608.0000 - val_loss: 6182758400.0000 - val_mean_squared_error: 6171909632.0000\n",
            "Epoch 24/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 7023911424.0000 - mean_squared_error: 7020916736.0000 - val_loss: 6205285376.0000 - val_mean_squared_error: 6196618752.0000\n",
            "Epoch 25/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7073061376.0000 - mean_squared_error: 7021272064.0000 - val_loss: 7411362304.0000 - val_mean_squared_error: 7385392640.0000\n",
            "Epoch 26/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 8099899392.0000 - mean_squared_error: 8049364992.0000 - val_loss: 5949494784.0000 - val_mean_squared_error: 5941257216.0000\n",
            "Epoch 27/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6807307264.0000 - mean_squared_error: 6809787392.0000 - val_loss: 5806901760.0000 - val_mean_squared_error: 5799510016.0000\n",
            "Epoch 28/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6803648000.0000 - mean_squared_error: 6807318528.0000 - val_loss: 5716349952.0000 - val_mean_squared_error: 5708461056.0000\n",
            "Epoch 29/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6942338560.0000 - mean_squared_error: 6950962176.0000 - val_loss: 6246453760.0000 - val_mean_squared_error: 6239407104.0000\n",
            "Epoch 30/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7749486080.0000 - mean_squared_error: 7859774464.0000 - val_loss: 6667757056.0000 - val_mean_squared_error: 6649705984.0000\n",
            "Epoch 31/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7308255232.0000 - mean_squared_error: 7261572096.0000 - val_loss: 7202734080.0000 - val_mean_squared_error: 7184608256.0000\n",
            "Epoch 32/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7304354816.0000 - mean_squared_error: 7285703680.0000 - val_loss: 5821757952.0000 - val_mean_squared_error: 5815301120.0000\n",
            "Epoch 33/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6671389184.0000 - mean_squared_error: 6753376768.0000 - val_loss: 5728872960.0000 - val_mean_squared_error: 5722739712.0000\n",
            "Epoch 34/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6846960128.0000 - mean_squared_error: 6893641728.0000 - val_loss: 6153278464.0000 - val_mean_squared_error: 6143708672.0000\n",
            "Epoch 35/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6792826368.0000 - mean_squared_error: 6818103296.0000 - val_loss: 5765587456.0000 - val_mean_squared_error: 5758901760.0000\n",
            "Epoch 36/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6570506240.0000 - mean_squared_error: 6585300480.0000 - val_loss: 5802841088.0000 - val_mean_squared_error: 5792457728.0000\n",
            "Epoch 37/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 6670315520.0000 - mean_squared_error: 6672642048.0000 - val_loss: 5676843008.0000 - val_mean_squared_error: 5667314688.0000\n",
            "Epoch 38/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 6664962048.0000 - mean_squared_error: 6710534656.0000 - val_loss: 5399157248.0000 - val_mean_squared_error: 5393421312.0000\n",
            "Epoch 39/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 7546820608.0000 - mean_squared_error: 7591400448.0000 - val_loss: 6069680128.0000 - val_mean_squared_error: 6060819456.0000\n",
            "Epoch 40/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 8374750208.0000 - mean_squared_error: 8391135232.0000 - val_loss: 6742246912.0000 - val_mean_squared_error: 6720842752.0000\n",
            "Epoch 41/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 7587850240.0000 - mean_squared_error: 7564604416.0000 - val_loss: 5749531648.0000 - val_mean_squared_error: 5739299328.0000\n",
            "Epoch 42/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6623231488.0000 - mean_squared_error: 6657202688.0000 - val_loss: 5476425728.0000 - val_mean_squared_error: 5470770688.0000\n",
            "Epoch 43/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6323493888.0000 - mean_squared_error: 6258678272.0000 - val_loss: 5988924416.0000 - val_mean_squared_error: 5977945600.0000\n",
            "Epoch 44/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 6268471808.0000 - mean_squared_error: 6270160384.0000 - val_loss: 5412741632.0000 - val_mean_squared_error: 5406365184.0000\n",
            "Epoch 45/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 6230936064.0000 - mean_squared_error: 6248102912.0000 - val_loss: 5176987136.0000 - val_mean_squared_error: 5168719872.0000\n",
            "Epoch 46/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 6398516224.0000 - mean_squared_error: 6368911360.0000 - val_loss: 5516115456.0000 - val_mean_squared_error: 5506504192.0000\n",
            "Epoch 47/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 9301522432.0000 - mean_squared_error: 9227438080.0000 - val_loss: 11742798848.0000 - val_mean_squared_error: 11676476416.0000\n",
            "Epoch 48/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 18530998272.0000 - mean_squared_error: 18920828928.0000 - val_loss: 16855024640.0000 - val_mean_squared_error: 16752392192.0000\n",
            "Epoch 49/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 13678338048.0000 - mean_squared_error: 13654882304.0000 - val_loss: 12836331520.0000 - val_mean_squared_error: 12769158144.0000\n",
            "Epoch 50/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 13020316672.0000 - mean_squared_error: 12913981440.0000 - val_loss: 12582284288.0000 - val_mean_squared_error: 12515039232.0000\n",
            "Epoch 51/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12833739776.0000 - mean_squared_error: 12778891264.0000 - val_loss: 12480680960.0000 - val_mean_squared_error: 12413294592.0000\n",
            "Epoch 52/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12731855872.0000 - mean_squared_error: 12672812032.0000 - val_loss: 12419178496.0000 - val_mean_squared_error: 12351715328.0000\n",
            "Epoch 53/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12662317056.0000 - mean_squared_error: 12653303808.0000 - val_loss: 12380611584.0000 - val_mean_squared_error: 12313139200.0000\n",
            "Epoch 54/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12612086784.0000 - mean_squared_error: 12618278912.0000 - val_loss: 12352897024.0000 - val_mean_squared_error: 12285463552.0000\n",
            "Epoch 55/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12573102080.0000 - mean_squared_error: 12544751616.0000 - val_loss: 12332484608.0000 - val_mean_squared_error: 12265115648.0000\n",
            "Epoch 56/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12543242240.0000 - mean_squared_error: 12479281152.0000 - val_loss: 12315849728.0000 - val_mean_squared_error: 12248530944.0000\n",
            "Epoch 57/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12519675904.0000 - mean_squared_error: 12468987904.0000 - val_loss: 12302497792.0000 - val_mean_squared_error: 12235212800.0000\n",
            "Epoch 58/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12500672512.0000 - mean_squared_error: 12377435136.0000 - val_loss: 12292359168.0000 - val_mean_squared_error: 12225098752.0000\n",
            "Epoch 59/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12486643712.0000 - mean_squared_error: 12596727808.0000 - val_loss: 12284948480.0000 - val_mean_squared_error: 12217704448.0000\n",
            "Epoch 60/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12476126208.0000 - mean_squared_error: 12415469568.0000 - val_loss: 12278601728.0000 - val_mean_squared_error: 12211371008.0000\n",
            "Epoch 61/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12469029888.0000 - mean_squared_error: 12377568256.0000 - val_loss: 12276032512.0000 - val_mean_squared_error: 12208795648.0000\n",
            "Epoch 62/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12466595840.0000 - mean_squared_error: 12355262464.0000 - val_loss: 12275201024.0000 - val_mean_squared_error: 12207956992.0000\n",
            "Epoch 63/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12465530880.0000 - mean_squared_error: 12414031872.0000 - val_loss: 12274862080.0000 - val_mean_squared_error: 12207613952.0000\n",
            "Epoch 64/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12465358848.0000 - mean_squared_error: 12379658240.0000 - val_loss: 12274805760.0000 - val_mean_squared_error: 12207556608.0000\n",
            "Epoch 65/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12465304576.0000 - mean_squared_error: 12400052224.0000 - val_loss: 12274749440.0000 - val_mean_squared_error: 12207500288.0000\n",
            "Epoch 66/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12465246208.0000 - mean_squared_error: 12386935808.0000 - val_loss: 12274695168.0000 - val_mean_squared_error: 12207446016.0000\n",
            "Epoch 67/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12465190912.0000 - mean_squared_error: 12627943424.0000 - val_loss: 12274638848.0000 - val_mean_squared_error: 12207389696.0000\n",
            "Epoch 68/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12465136640.0000 - mean_squared_error: 12608008192.0000 - val_loss: 12274582528.0000 - val_mean_squared_error: 12207333376.0000\n",
            "Epoch 69/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12465080320.0000 - mean_squared_error: 12368121856.0000 - val_loss: 12274527232.0000 - val_mean_squared_error: 12207278080.0000\n",
            "Epoch 70/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12465022976.0000 - mean_squared_error: 12578189312.0000 - val_loss: 12274472960.0000 - val_mean_squared_error: 12207224832.0000\n",
            "Epoch 71/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12464970752.0000 - mean_squared_error: 12442671104.0000 - val_loss: 12274417664.0000 - val_mean_squared_error: 12207169536.0000\n",
            "Epoch 72/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12464914432.0000 - mean_squared_error: 12400949248.0000 - val_loss: 12274364416.0000 - val_mean_squared_error: 12207115264.0000\n",
            "Epoch 73/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12464859136.0000 - mean_squared_error: 12530124800.0000 - val_loss: 12274311168.0000 - val_mean_squared_error: 12207062016.0000\n",
            "Epoch 74/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12464804864.0000 - mean_squared_error: 12431526912.0000 - val_loss: 12274255872.0000 - val_mean_squared_error: 12207007744.0000\n",
            "Epoch 75/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12464752640.0000 - mean_squared_error: 12342638592.0000 - val_loss: 12274199552.0000 - val_mean_squared_error: 12206952448.0000\n",
            "Epoch 76/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12464698368.0000 - mean_squared_error: 12417718272.0000 - val_loss: 12274145280.0000 - val_mean_squared_error: 12206897152.0000\n",
            "Epoch 77/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12464643072.0000 - mean_squared_error: 12564521984.0000 - val_loss: 12274091008.0000 - val_mean_squared_error: 12206843904.0000\n",
            "Epoch 78/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12464589824.0000 - mean_squared_error: 12367813632.0000 - val_loss: 12274037760.0000 - val_mean_squared_error: 12206790656.0000\n",
            "Epoch 79/100\n",
            "40/40 [==============================] - 0s 3ms/step - loss: 12464534528.0000 - mean_squared_error: 12379559936.0000 - val_loss: 12273985536.0000 - val_mean_squared_error: 12206737408.0000\n",
            "Epoch 80/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12464484352.0000 - mean_squared_error: 12362357760.0000 - val_loss: 12273931264.0000 - val_mean_squared_error: 12206684160.0000\n",
            "Epoch 81/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12464428032.0000 - mean_squared_error: 12363511808.0000 - val_loss: 12273876992.0000 - val_mean_squared_error: 12206629888.0000\n",
            "Epoch 82/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12464377856.0000 - mean_squared_error: 12389294080.0000 - val_loss: 12273825792.0000 - val_mean_squared_error: 12206577664.0000\n",
            "Epoch 83/100\n",
            "40/40 [==============================] - 0s 6ms/step - loss: 12464323584.0000 - mean_squared_error: 12414214144.0000 - val_loss: 12273770496.0000 - val_mean_squared_error: 12206524416.0000\n",
            "Epoch 84/100\n",
            "40/40 [==============================] - 0s 6ms/step - loss: 12464268288.0000 - mean_squared_error: 12521240576.0000 - val_loss: 12273718272.0000 - val_mean_squared_error: 12206471168.0000\n",
            "Epoch 85/100\n",
            "40/40 [==============================] - 0s 6ms/step - loss: 12464218112.0000 - mean_squared_error: 12464672768.0000 - val_loss: 12273667072.0000 - val_mean_squared_error: 12206418944.0000\n",
            "Epoch 86/100\n",
            "40/40 [==============================] - 0s 6ms/step - loss: 12464162816.0000 - mean_squared_error: 12445530112.0000 - val_loss: 12273611776.0000 - val_mean_squared_error: 12206364672.0000\n",
            "Epoch 87/100\n",
            "40/40 [==============================] - 0s 6ms/step - loss: 12464110592.0000 - mean_squared_error: 12411953152.0000 - val_loss: 12273558528.0000 - val_mean_squared_error: 12206312448.0000\n",
            "Epoch 88/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12464058368.0000 - mean_squared_error: 12496447488.0000 - val_loss: 12273506304.0000 - val_mean_squared_error: 12206258176.0000\n",
            "Epoch 89/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12464005120.0000 - mean_squared_error: 12507326464.0000 - val_loss: 12273454080.0000 - val_mean_squared_error: 12206206976.0000\n",
            "Epoch 90/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12463952896.0000 - mean_squared_error: 12398821376.0000 - val_loss: 12273400832.0000 - val_mean_squared_error: 12206154752.0000\n",
            "Epoch 91/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12463900672.0000 - mean_squared_error: 12555081728.0000 - val_loss: 12273349632.0000 - val_mean_squared_error: 12206102528.0000\n",
            "Epoch 92/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12463847424.0000 - mean_squared_error: 12394507264.0000 - val_loss: 12273294336.0000 - val_mean_squared_error: 12206049280.0000\n",
            "Epoch 93/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12463796224.0000 - mean_squared_error: 12480053248.0000 - val_loss: 12273244160.0000 - val_mean_squared_error: 12205998080.0000\n",
            "Epoch 94/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12463744000.0000 - mean_squared_error: 12566861824.0000 - val_loss: 12273192960.0000 - val_mean_squared_error: 12205946880.0000\n",
            "Epoch 95/100\n",
            "40/40 [==============================] - 0s 6ms/step - loss: 12463690752.0000 - mean_squared_error: 12416260096.0000 - val_loss: 12273139712.0000 - val_mean_squared_error: 12205893632.0000\n",
            "Epoch 96/100\n",
            "40/40 [==============================] - 0s 6ms/step - loss: 12463638528.0000 - mean_squared_error: 12569085952.0000 - val_loss: 12273086464.0000 - val_mean_squared_error: 12205840384.0000\n",
            "Epoch 97/100\n",
            "40/40 [==============================] - 0s 7ms/step - loss: 12463588352.0000 - mean_squared_error: 12571515904.0000 - val_loss: 12273035264.0000 - val_mean_squared_error: 12205790208.0000\n",
            "Epoch 98/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12463536128.0000 - mean_squared_error: 12394079232.0000 - val_loss: 12272982016.0000 - val_mean_squared_error: 12205736960.0000\n",
            "Epoch 99/100\n",
            "40/40 [==============================] - 0s 5ms/step - loss: 12463481856.0000 - mean_squared_error: 12367886336.0000 - val_loss: 12272929792.0000 - val_mean_squared_error: 12205684736.0000\n",
            "Epoch 100/100\n",
            "40/40 [==============================] - 0s 4ms/step - loss: 12463428608.0000 - mean_squared_error: 12579170304.0000 - val_loss: 12272878592.0000 - val_mean_squared_error: 12205633536.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred8=ann.predict(X_test)\n",
        "\n",
        "print('Coefficient of Determination (R2 Score)=',r2_score(y_test, y_pred8))\n",
        "\n",
        "print('Mean Squared Error= ',mean_squared_error(y_test, y_pred8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWFts9P1euCB",
        "outputId": "5601cd02-fb17-46bd-bffe-cbca2a40a070"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49/49 [==============================] - 0s 1ms/step\n",
            "Coefficient of Determination (R2 Score)= -0.507512352180933\n",
            "Mean Squared Error=  13822242742.846851\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Comparison"
      ],
      "metadata": {
        "id": "9jrNmNl2G5m_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "comp=pd.DataFrame({'Models':['Linear Regression','Bayesian Ridge regressor', \n",
        "                             'Decision Tree Regressor','Lasso Regression',\n",
        "                             'Random Forest Regressor','Polynomial Regression', \n",
        "                             'K Nearest Neighbor', 'Artificial Neural Network'],\n",
        "                   \n",
        "                   'Coefficient of Determination (R2 Score) ':[r2_score(y_test, y_pred1)*100,\n",
        "                                                               r2_score(y_test, y_pred2)*100,\n",
        "                                                               r2_score(y_test, y_pred3)*100,\n",
        "                                                               r2_score(y_test, y_pred4)*100,\n",
        "                                                               r2_score(y_test, y_pred5)*100,\n",
        "                                                               r2_score(y_test, y_pred6)*100,\n",
        "                                                               #r2_score(y_test, y_pred7)*100,\n",
        "                                                               max(r2_val)*100,\n",
        "                                                               r2_score(y_test, y_pred8)*100\n",
        "                                                               ],\n",
        "                   'Mean Squared Error':[mean_squared_error(y_test, y_pred1),\n",
        "                                         mean_squared_error(y_test, y_pred2),\n",
        "                                         mean_squared_error(y_test, y_pred3),\n",
        "                                         mean_squared_error(y_test, y_pred4),\n",
        "                                         mean_squared_error(y_test, y_pred5),\n",
        "                                         mean_squared_error(y_test, y_pred6),\n",
        "                                         #mean_squared_error(y_test, y_pred7),\n",
        "                                         min(mse_val),\n",
        "                                         mean_squared_error(y_test, y_pred8)\n",
        "                                          ] } )"
      ],
      "metadata": {
        "id": "QExVhLjWG5S1"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "comp\n",
        "comp.style.format(\"{:.1f}\")\n",
        "pd.set_option('display.float_format', str)"
      ],
      "metadata": {
        "id": "-24IX3QtHj6B"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "comp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mTlDladBlR_N",
        "outputId": "516e4bd8-f382-4d49-fc8c-74f3cc1d2f1c"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Models  Coefficient of Determination (R2 Score)   \\\n",
              "0          Linear Regression                         51.91615539423922   \n",
              "1   Bayesian Ridge regressor                        51.547288825411506   \n",
              "2    Decision Tree Regressor                         36.65430566269327   \n",
              "3           Lasso Regression                         51.91612235135079   \n",
              "4    Random Forest Regressor                        59.327592955859544   \n",
              "5      Polynomial Regression                        60.241625465431746   \n",
              "6         K Nearest Neighbor                         96.52088020124636   \n",
              "7  Artificial Neural Network                       -50.751235218093306   \n",
              "\n",
              "   Mean Squared Error  \n",
              "0   4408763690.649902  \n",
              "1   4442584728.644594  \n",
              "2   5808108720.155581  \n",
              "3   4408766720.322092  \n",
              "4  3729215765.8719454  \n",
              "5  3645408961.8753953  \n",
              "6   318997309.1779062  \n",
              "7  13822242742.846851  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f6e510d-341b-46f7-bc38-da23a6455cc2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Models</th>\n",
              "      <th>Coefficient of Determination (R2 Score)</th>\n",
              "      <th>Mean Squared Error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>51.91615539423922</td>\n",
              "      <td>4408763690.649902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bayesian Ridge regressor</td>\n",
              "      <td>51.547288825411506</td>\n",
              "      <td>4442584728.644594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>36.65430566269327</td>\n",
              "      <td>5808108720.155581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lasso Regression</td>\n",
              "      <td>51.91612235135079</td>\n",
              "      <td>4408766720.322092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>59.327592955859544</td>\n",
              "      <td>3729215765.8719454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Polynomial Regression</td>\n",
              "      <td>60.241625465431746</td>\n",
              "      <td>3645408961.8753953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>K Nearest Neighbor</td>\n",
              "      <td>96.52088020124636</td>\n",
              "      <td>318997309.1779062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Artificial Neural Network</td>\n",
              "      <td>-50.751235218093306</td>\n",
              "      <td>13822242742.846851</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f6e510d-341b-46f7-bc38-da23a6455cc2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Best Model "
      ],
      "metadata": {
        "id": "TAO12cCVjLB9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "r2_df =comp.loc[comp.iloc[:,1] == comp.iloc[:,1].max()]"
      ],
      "metadata": {
        "id": "4NwnhTdhgv3x"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "r2_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "cnAcpk5ahapX",
        "outputId": "655f569c-8925-419f-c5d6-722217e1f08a"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Models  Coefficient of Determination (R2 Score)   \\\n",
              "6  K Nearest Neighbor                         96.52088020124636   \n",
              "\n",
              "   Mean Squared Error  \n",
              "6   318997309.1779062  "
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              "      <th>6</th>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse_df=comp.loc[comp.iloc[:,2] == comp.iloc[:,2].min()]"
      ],
      "metadata": {
        "id": "F67F3qgiis8V"
      },
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mse_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "iEd6iLvpjB6U",
        "outputId": "e4e38143-7519-493f-b9a3-5872ebc82a89"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Models  Coefficient of Determination (R2 Score)   \\\n",
              "6  K Nearest Neighbor                         96.52088020124636   \n",
              "\n",
              "   Mean Squared Error  \n",
              "6   318997309.1779062  "
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              "      <th>6</th>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-abc4629c-0eef-4804-8960-2a4e30ab6009 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-abc4629c-0eef-4804-8960-2a4e30ab6009');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**KNN is the best model with least Mean Squared Error and Maximum R2 score**"
      ],
      "metadata": {
        "id": "DZQxa9hSjvaY"
      }
    }
  ]
}