BigMart_Sales.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "BigMart Sales.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nn7FT22Mlp3i",
        "colab_type": "text"
      },
      "source": [
        "#Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dyV0uG_BlwXo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJr3ARNNmWYk",
        "colab_type": "text"
      },
      "source": [
        "Import dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GrMo5OFJl5oP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset = pd.read_csv('Train_BigMart.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sv131yYgmTaZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "8b838a13-a0db-4f44-96ac-0d900f201937"
      },
      "source": [
        "dataset.isnull().sum().sort_values(ascending = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Outlet_Size                  2410\n",
              "Item_Weight                  1463\n",
              "Item_Outlet_Sales               0\n",
              "Outlet_Type                     0\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Identifier               0\n",
              "Item_MRP                        0\n",
              "Item_Type                       0\n",
              "Item_Visibility                 0\n",
              "Item_Fat_Content                0\n",
              "Item_Identifier                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SS2XcMLhnNWa",
        "colab_type": "text"
      },
      "source": [
        "Understanding the dataset and dependencies\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbfMYpkdpH9_",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "#Divide dataset into numerical, categorical and target data and combine them after encoding"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTlSIVeEpRn-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_data = []\n",
        "cat_data = []\n",
        "target = []\n",
        "\n",
        "for i,c in enumerate(dataset.dtypes):\n",
        "  if c == object:\n",
        "    cat_data.append(dataset.iloc[:, i])\n",
        "  else:\n",
        "    num_data.append(dataset.iloc[:, i])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZ9Sd3Twp7Cc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_data = pd.DataFrame(num_data).transpose()\n",
        "cat_data = pd.DataFrame(cat_data).transpose()\n",
        "target = num_data.iloc[:, -1].values\n",
        "num_data = pd.DataFrame(num_data).drop('Item_Outlet_Sales', axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZTb86ZAq8Rl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "f6b005f2-69a0-4f4c-9830-611344823ccc"
      },
      "source": [
        "num_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.30</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>1999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.92</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>2009.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.50</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>1999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>19.20</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>1998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.93</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>1987.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year\n",
              "0         9.30         0.016047  249.8092                     1999.0\n",
              "1         5.92         0.019278   48.2692                     2009.0\n",
              "2        17.50         0.016760  141.6180                     1999.0\n",
              "3        19.20         0.000000  182.0950                     1998.0\n",
              "4         8.93         0.000000   53.8614                     1987.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B704uvaArJIe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "03dc2f08-462d-4705-ca27-133be8262a04"
      },
      "source": [
        "plt.scatter(num_data.iloc[:, 1], target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbb073eb5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VaQbzzXCNFNyd6jsRolbq8Rk8QdER/BOKYaV9FE04nwc+a2b7KfoE7vZ2uRt4hzf+WWDYO85e4H6KiuQHwI3OuXhB/QkI6wtQabwSSfIOwjArCuabvASwKLNNPYlQlVYalRTMpHOl80aaVso0V2HSMfrSYaB9PoWhlQNcs2px6Ormgvcvarjg7lTfiRC1UleegXNuA7ChbPh5QqKBnHNvAp+IOM6XgC/VM5dGU6mMQ1yzQ3nGcLrPmAImw6rChXBSuvaF2/yIxjhQubUlzExcC96Dzdv3TfO7+Ny78wCDZyxsq0/h9qHlDJ6xMHTOjSgpHqRTfSdC1ErPlKNYEJHwtaB/ZtewamUcKiWP+RhwzarFPPbMoZJgOnZ8IlH/Yj/WH5KVVh4Zy3H0+ETod3G9JP41BzOWK/U98B21zaj7k6T3QBJHfz2CW/WNxGyjZ8pRbLhkWaite8MlM7uGVSvj8M9vTpDqqxyo6Sg+qfoJYo8Pr4lMOqtELaaHSpVIkxBWUC8q0Q6KwrVa7kNSGmGbryesOIpGX6cQ7aZnVgZJuoZVe2IsTLmqMfsDZdE1m7fvi/1UnnQ+9W6fhEnnIgvm+cL1pHRfSZkaMxVakg5jjTDxNKMwXSu60AnRSnpGGSShUrlon0qCPSy6ph6Hc9In2DhmrFrx8xXKS3EHHbXBaw2uqNZ95ykwSquWOPWhGmGbb5bgVn0jMZswV0esfTsZHBx0o6OjsbcPE8qZdCo0fHJkLMdNEXbmuCzoT7PhkmVs3r4vUZJXOemUcfLcObyRL0zri7Bx696Sg7jPimGyvqDe8uODoU7eRnDtqsXcPrQ81I5f67X69ZaC+MevdLyBBgh19UIWvYaZ7XLODc4Y7xVlsHrTjlDBEiaIAFbe9kjdT9eZdCrxiiCbSXPyvDm8Mp4n25/mn9+cmJ41nTImJx1TEftX6nnQCKIUKMDS4W01nduAFzZdXPqcZDVVaT7VSPKAIMRsIUoZ9IwDOam5YcMly+oq8gbUZBryn/bvvGoF/XPnzHjCL1RQBNAYRZAy49pVi0OdxZUc2rU6ZOP0cogiX5hk49a9DetTkcRhrwxkMZvoGWUwPzMzhLTSeFgRuFbhh3XWY16qFQO+fOU53D60nKmIVWOUAq2lSmqYIzepA3w8X6gp2ijqPHHuuzKQxWyjZ5TB8YnwJ82ocSgqBD80tLwsdbNpl/HumlWLSyaSpCGZQysH+Pi58cwrlTqMJe0fUU69fSoMqgp1ZSCL2UbPKINjhXDjStR4kJGxHPfuPNDoKdVEOmVN/aUNnrGw9D5JLP3IWI6Vtz3Ct2Lcp4FsppR7EWabDzuvcSKsNQ5x+1SEHc9PoKvl+MpAFt1KzyiDeti4dW/bntThRDXRgWyGzVecw59ctYJshHmrXm7aspt/+V++z9LhbWzevo+PnztQtVewbzKJ43CvFt/vR/cEE9yCTvFgH+iBbCY0gxzi96mo1jo0imYksgnRTnomzyAqyqbak+bIWK6mYnaNxO977Idv+mGQy059G3/73OGqimogm6F/bh/PvnY01vny3mopN55ny5MH2XzFOYkTw8oxqBq6WR7dE5Xg5t+Px4fXREYExU0oG6ixrEQzEtmEaCc9szKIEpjVBGmzbcBxzR6+gzLosHw8piJ4fHgNx45XN4eFUZh0fPb+3RVt6NWeoo1idFSUWcinUhmQcnwBXq3bWzVqLStR73mF6DR6ZmVQK822Acc1P6XMagpV9edfT2TSlKNipnC1jO243caS3OuU2YyEsTuvWpFYGNeTnawMZDGbkDIoo1zANLO0QxJq7co2P5Nm9aYddZ+/Uj2gMJNJOXEEfZwyID6Tzs2oLBumsOJkGEuoCyFlMI2w0tXdztHjEw3zebwynp8hXP2+yr7DN0ppxXGshimVShnVlUI7/VIWwf3j1EJqNip/ITqVnilHsWR4W+R3L3qlEKJKVogi6T6Yk6pcYiOdMnBMy5xOUuIhTNmUF8WrRrWSHAv60/TPndNygazyF6ITiCpHoZVBgE6JEU/1WexuaI2mkiAtTEFhqrLfojDp6hK2YSabOLkLQarduSPHCiXTX6maKs1fLTSj45oQjULKIEASm3WzSBm8/aQ5bfNTNEIFjR8rMPbFjzTgSEWiwj8bVZSvMOVY/9DTTTffKFFNdDI9E1oadaHB8VaXnAhj0tERDut6iJt4FbfQW9Tv5cPvXRi7REU18oWpptcZUqKa6GR6RhnMj8hUDY4/9syhVk1n1hI38SpJobeo38uLr+cji+kFCWYsx6UZdYaS5jSoKqpoJXWZicwsC/xP4AMUV+y/DewDtgBLgBeBK51zR8zMgK8CHwOOAb/lnPuJd5zrgFu8w97unLunnnmFEdV/ODiu5Xp9GPDxc+OFaUbZz299eG/JXHNSuo83C1MVE88qRTDBzAY4SfpU5MbzrLztEZxjWnOhWs1HSXIawiLb2h0JJWY39foMvgr8wDl3hZnNBfqBPwB+6JzbZGbDwDDweeCjwJne63zg68D5ZrYQ2AAMUlQou8xsq3PuSJ1zm0b/3BRHj890fvbPPfGk1gk+g27GAd/+8UEGz1hYc4JZ0Lmbr1JE0C9eF0a6DzZ/YmYS2oZLlrHugadKrTerEVQcQYEMzU1Uk7NZtJqazURmNh/4VeBuAOfccefcOHAZ4D/Z3wMMee8vA77piuwEsmb2bmAt8Khz7rCnAB4FLqp1XlGEKYLy8Vrq8beC/nRfS/sp1MPklOPWh6s3m2mEnbySOC9MwbrvPFU6t29yuXnLbk6eO4cF/elSGYlrVy2O/Xv3Vy/lJq6btuxmxa2PNMyUI2ezaDX1rAyWAoeA/2Vm5wC7gN8D3uWce9Xb5ufAu7z3A8DBwP4ve2NR4y3jveu/N+0Js5rpodXkC1P8dHgNt4zsSRxzH4c4+QNJKA/dDDNvxMlarpfClCvZ/YPnGs8XyKRT08pXDJ6xMHYP5ygz03i+0DBTTtQqVc5m0SzqcSDPAT4EfN05txI4StEkVMIVM9oaJrvM7AYzGzWz0UOHGufsLRf8naQIoFhSYmQsx4O7ck0ppV2Yqq1FZ1yinLEnpU/8+WUzaTLpxsczvDKej9WIxm9kVO8KrFGO51oL6AlRK/WsDF4GXnbOPeF9foCiMviFmb3bOfeqZwZ6zfs+B5we2P80bywH/Nuy8R+FndA5dxdwFxQzkOuYe1fxT29NsHHr3qYK7GaTG8+zetMOXhnPMz+T5ujxiWl2+6PHJxIn2sXJMzg1m6locgnLeK6U5JZJp5g3p69iiY9GmHLqKaAnRC3UrAyccz83s4NmdpZzbh9wIfBT73UdsMn7+V1vl63A75rZfRQdyG94CmM78EdmtsDb7iPA+lrnNRuZnHJt76kQh3lz+nhrItrp65s9wq4lrkPXxyi26HzsmUMVTTt+D4iwbbL96RkROw/uytGf7gvtgJcy447LlwPFJkBRNMqUowJ6opXUG030n4F7vUii54FPUzQ93W9m1wMvAVd6236PYljpfoqhpZ8GcM4dNrM/BJ70trvNOXe4znmJFpPuM/rnpioqg5qPnbJpysJXBLcPFQVzVE2pbCZdEqbl/ol0nzGeL1BuEcwXJslm0jisYg2hWx/eG+o7MGioKUeF7USrqEsZOOd2UwwJLefCkG0dcGPEcb4BfKOeuYj2UphyTcucDiqCBf1pNlyyDKBkdsr2p0n32YzieBsvLW43tHKA0ZcO8+0nDpa6p03BDEXg80a+wJ1XrQgVwiNjOTZu3Ru6uvGVVKOEtXINRCtRbSI6L3qoFwmrdhrGkWMFvvDXezg+MVXa9sixAumUkc2kQ5PDfOe7/zt2UNE/cWo2M8NEMzKWY8Wtj0Sa63wl1UghrVwD0UqkDIDn7vgY4D+JPV012UmEk82kY/s20n3GKSfNYfzYCeENlW3xPmE5I4VJx8nz5rB7w8wCeXF6NPuEReyElZ4up3/unIoCuhZzj3INRCuRMvA48Q8vRVAri942lzfyhVjhr/5TfTDWf2QsV1cl0tx4nqXD28j2p3mzMJn4dxl0EPsmqFOzGY4dn6iqTMoFdFD4l0dP5cbz3LxlN6MvHS75PcJQroFoJVIGqKlNo3j2taOJtj9yrMC6B070Eti8fV/deRSO2qq++g5ioKZud0EBPTKWm1byImy15IB7dx6oWLojLDFPuQaiWUgZMDvaW3YrhckTWcLt+j1kM2k2Xlq096/etCNxPkc6ZdME9K0P740VKuugov1fuQailUgZiLZTXgCu1Zw874S9P6k9PsxxnGRlUu18yjUQrULKQCRmTp8x0cC2nCmztmZXBwVylJ0+m0lz8rzG900ut/+X+xrMmOZkl2IQzULKQCSmkYoAWlMLKp0y5qb6IsuYx8lZiCuI40ZVpfuMY8cnWDq8rVQK48FduWkF9XyUYyCaTc90OhOdSTaTrrk4nHmvBRFd7HwW9KfZfMU5pFPhf+5Hj0+WylEfOVYAK87LL3EdzDyOw8ZLl5Hum96OM91nXLtq8bS5+ol6fhnse3ceqLhCakb3NSF8tDIQbWXR2+Zy4wVn1lTO2gEvbrqYFbc+UnG7sS8Wcw9ujpHDAJVzFsq5ZWRPKbM5ZcbV559eChctd/wCPLgrut9BnPWRcgxEs5AyEG3l2deOMvrSYe64fDmfu/+pxCajkbFcRZNMNnPiSTxJJ7s4QveWkT3TKpxOOlf6XK4QNm/fx9G3qucrVEM5BqJZyEwk2o4vQL985TmJO81VM5v49YlGxnIcfWtixvc2Y6RIHKH77ScORo77SYzBbmhx/AhR8wHlGIjmImUgOgLfOXrH5csT+RBeGc9H+gz6032l4nLrH9oTKoz756Zm2PfjCN2RsVzkKmbSuUQlMILnvWbV4tL1B2e1oD+d2HchRBJkJhIdge8cfXx4DUMrB1j2xR9E9q0O0j83xVshQjedMv7o8g8ClWsTHT0+WbHIXTm3jOzhr544QKWAKqO2fIWLP/juUn+G8rIcb6pMimgyUgaiY8iN57llpLhCiKMIKm133pIFsRPJ4jqMy30EUfT1GW+bNydRQyLnmBZWWq5rVK1UNBspA9FRxBG2cXj8ucMsGd7GQDbD/Bhx/74iqlQ4LspHUE4tnenibK9IItFM5DMQs5rceJ5fvhlPMH9r54HSyiSMdve8cBSLKo6MRYenClErUgZi1pMkYfpbOw9ECttKkT710hfz4H4mshSCaDRSBkKUUS5sR8ZyrN60o+7y2pWYcswIq43SD8pEFs1AykCIMoLCNpgv0EwM+Pi5AwxkM6UyGHdetSJSIch/IBqNHMhChJAbz5cqiCbJF6i1U5sDHnvmEI8Pr5k2vnn7PnU7Ey1BKwPRUaTMpv1sJ7WsCBxMe7q/1ksiM4qlMdKp6OsKPu37pik/5yCIMpFFM6h7ZWBmKWAUyDnnft3MlgL3Ae8AdgG/4Zw7bmbzgG8C5wKvA1c55170jrEeuB6YBD7jnNte77xEd/LlK88pZQ2v+85T00pJt5pa6ghlM+kZT/dBRsZykTWY/Kf9E/24T+Qc+CuOAfU1EE2iESuD3wN+Fvj8x8Cdzrn3AUcoCnm8n0e88Tu97TCzs4FPAsuAi4A/9xSM6DHmpqykCD57/+62KoJaeePNAkuHt4WGgPpmp0nnQn0BR9+aiDRN+YrAz9AWotHUtTIws9OAi4EvAZ81MwPWAJ/yNrkH2Ah8HbjMew/wAPBn3vaXAfc5594CXjCz/cB5wN/VMzfRfRyfdCwZ3tbuadSF/8Bf3srz1of3TmuHGabmxvOFiqW85TQWzaReM9FXgN8H3uZ9fgcw7pzzy0O+DPiPMQPAQQDn3ISZveFtPwDsDBwzuM80zOwG4AaAxYsX1zl1IZpLvjDJTTF7KAT3SZmFmpH6zBgZy2llIJpCzWYiM/t14DXn3K4Gzqcizrm7nHODzrnBRYsWteq0QrSUSedCS3lPOqeEM9E06vEZrAYuNbMXKTqM1wBfBbJm5q84TgP8v9wccDqA9/18io7k0njIPkL0HH6rzbCIKiWciWZRszJwzq13zp3mnFtC0QG8wzl3DfAYcIW32XXAd733W73PeN/vcM45b/yTZjbPi0Q6E/hxrfMSoolAQJgAAA+jSURBVJtJ9RlH35rg5i27I2shyXcgmkEzks4+D9xnZrcDY8Dd3vjdwP/2HMSHKSoQnHN7zex+4KfABHCjc66+3oBCdClxKp4q4Uw0g4YoA+fcj4Afee+fpxgNVL7Nm8AnIvb/EsWIJCFEBZRwJpqFylEI0QUYVO3CJkQ9SBkI0eEs6E8z9sXKXdiEqBfVJhKiwxnPFxROKpqOlIEQTabeknvOwc1bdlfswiZEvUgZCNFkGlFhyQH3VujCJkS9SBkI0SaSlul2wE1bdqsPsmgKUgZCtIGBbIarzz99hgkp3WdV+yGrD7JoBlIGQrQYoyjQv7XzwAwT0nlLF/AnV0a3u/RRWQrRaKQMhGgxlXwIf/vcYQCuWbW4qkJQWQrRSKQMhOggHMW+x7cPLefOq1YwUKH0hMpSiEYiZSBEh+E/8Q+tHODx4TV85aoVM0paqyyFaDTKQBaiw+gzY+nwthnlJzZv38cr43mVpRBNQcpAiA7DL10dbJ05tHJAwl80FZmJhOhgFDUkWoVWBkJ0OLnxPKs37ZCJSDQVKQMhOhw/LwFmmo6EaBQyEwnRwRgz8xJkOhLNQMpAiIRkM2nSTfzPMe81kM1EJqgp4Uw0GikDIRLyyzcLFKaae44XNl3MurVnRRazU8KZaDRSBkIkZKoRNakrcGo2w8hYjvUP7SmFmZZzwfsXNXcSoueQA1mIDiKdMi54/yI+d/9TkYoA4LFnDrVwVqIXqHllYGanm9ljZvZTM9trZr/njS80s0fN7Fnv5wJv3MzsT81sv5k9bWYfChzrOm/7Z83suvovS4juZHLS8VdPHKioCEA+A9F46jETTQCfc86dDawCbjSzs4Fh4IfOuTOBH3qfAT4KnOm9bgC+DkXlAWwAzgfOAzb4CkSIXmOKeGYo+QxEo6lZGTjnXnXO/cR7/0/Az4AB4DLgHm+ze4Ah7/1lwDddkZ1A1szeDawFHnXOHXbOHQEeBS6qdV5CzHZUpE40g4b4DMxsCbASeAJ4l3PuVe+rnwPv8t4PAAcDu73sjUWNh53nBoqrChYvXtyIqQvRVQwoA1k0ibqVgZmdAjwI3OSc+6UFQuGcc87MGhZ74Zy7C7gLYHBwsMkxHUJ0Dmbwwh0Xt3saYhZTV2ipmaUpKoJ7nXMPecO/8Mw/eD9f88ZzwOmB3U/zxqLGhRAeVfzJQtRNPdFEBtwN/Mw59yeBr7YCfkTQdcB3A+O/6UUVrQLe8MxJ24GPmNkCz3H8EW9MCOGRaWbKsxDUtzJYDfwGsMbMdnuvjwGbgH9nZs8Cv+Z9Bvge8DywH/gfwO8AOOcOA38IPOm9bvPGhBAe+cIUqzftYGRMi2bRHMx16fpzcHDQjY6Oxt5+yfC2Js5GiNZgwDWrFnP70PJ2T0V0KWa2yzk3WD6utacQXYQD7t15QCsE0XBUjkKILsNR7IfcrPDSkbGc+i33IFIGQnQhzSpH4RfIyxcmATXT6SVkJhKiC2lWOYrN2/eVFIGPmun0BloZCNFlGDStHEXUiqMbCuPJvFUfWhkI0WVcs2px04Rc1Iqj0wvj+eat3Hgexwnzlhzt8ZEyEKLLGDxjYdOOvW7tWWTSqWlj3VAYr9XmrZGxHKs37WDp8LZZk/8hM5EQbWbenD7eeco8cjFNMeseeApojkPXP2a3mVtaad6arU52KQMh2sxbE8kaKhcmXWhoaaNs5kMrB7pOqJ2azYQq02aYtyqtQsLuW7f4MqQMhOgA4q4KfMqfeBvxtNoModUqQbhu7VnTrh+aY94aGctF/q7CViHdtIqQMhCiCyl/4o16Wr314b2xhE4zhFYrFVQrzFv+9UTh/06Cc+4zm9HCtNIqIuq8rVCoUgZCdBnplM144o2yjR85VmBkLFdVeCQ1fcQh6pifu7+6z2NkLMetD+/lyLFCaayaMolr3qpVuIZdj4+/CilXgFG9rOP6Mlq5spAyEKKLSPfBKfPmcPOW3Wzevq8kyKJs5gC3Pry3qvBrhgM2at9J5yIFWpgSCBKloOIK+HqEa6V7ccflyxlaOcDK2x6JVBhB4voymqGko5AyEKKLKExREpS58Tw3b9nNTVt2k82kI/c5cqwwbZ+btuzm5vt349yJNppRymR+4LjlAveC9y/isWcORQrgSgoqGPbpH3N+Js3R4xMUJitXUi4/ZhIBX89qJdufDlVSA9kMQysHGBnLRSqxIEl8GVEKKDeej7XiS4JKWAvR4xjF4ndhpFPG5ivOAZjhoI06jq9g4uyTSadiPUmXn+fOq1aUBOHqTTtClY4/j6ACq+Soz6RTpSf8ckbGcqz7zlMUpqbfqT4rKszxY4VQ/0BwzkBim3/UtVWbbyWiSlhLGQghqpKqIOjC8PsuDJ6xkM/d/1TovkmPGWQgm+Hx4TUALB3eFqnMkiqb4HF9RsZykdcQF1+pJhXc5aueOPOtRpQykJlICFGVpILQ77sweMZCvnzlOaFhn0lXBEGC5pMo802fkfgcufE8qzftmGYK2/LkwboUAUTnhlTD3/6mLbtDv29kUp3KUQghmkKw78LHzx0gZUVjScqMDy2eXzKd1ELQARslp6dqlN/B+kbf2nmgqg8jyXFrYWjlAAMtqBmllYEQomnkxvOsuPURjh6fKD1dTzrH48/V3uY83Tc9tPaNfHWnbSdgEMvpGxYZ1YqkOq0MhBBNZTxfaNjTNcApJ80pCdSRsRx9Fr7GqBRh1Q4cRXPPkuFtrLztkdDidlHVV6EYvjqQzWAUfQW1OI8roZWBEKKr8P0DvuCMsuePd/CK4cixQmjBwUp5BY8Pr2lqCYuOWRmY2UVmts/M9pvZcCvPnU7VY70UQrSDShnB3YDvVA7SzuZCHaEMzCwFfA34KHA2cLWZnd2q8/tx1EKI7qEbuq9Vo/wa2tlcqCOUAXAesN8597xz7jhwH3BZq04+tHKgrsgGIUTr8KOSkghIA17cdDFfuWrFjOY97aT8GtrZXKhTlMEAcDDw+WVvbBpmdoOZjZrZ6KFDhxo6gU5v6ydEUrKZNNeuWtzuaSQm1Vf50ezq808HwgVnFP7/99DKgZIjthmkIpzZYYQVHAzOr1mO4ig6RRnEwjl3l3Nu0Dk3uGjRooYee93as0hX+SMUIopO+0fKpFNsvHQZtw8t5ytXrSgJlyTCqlnzWv3e8Lad2Uyar1y1gi9/4pzSfPvTffhTTplx7arF3D60HJgp2KOurPzJemjlAI8Pr6lZIVjZz+B5rj7/9NAn+2tXLZ4W3bSgPx2ZkezP74VNFzfdaRykU6KJcsDpgc+neWMN4+3zUvzyrZnOprfPK/7i/Bu+cevejo5CaCcGvO9XTmb/a0dD0//TfXDKScU6LfMzacyKURN9Fp0AFFUXJ5Pu462JqZoShyqdrxZOnpvi2PHJUkbqtqdfLUW0ZDNpNl66rFSorJ6/n0o1girRZ/Cp8xdHFo0LlnaOKm+Q7oOrzpt+jPJCdBe8fxEP7srNiHX/+LkDM8bDWNCfZsMlJ+5VpSqjcQVg+bVt3r6P3Hi+VOpioEItoLDY/XTKwDGtBlG6zzjlpDmMHytMm2vUNQyesTB03FdinUpH1CYysznAPwAXUlQCTwKfcs7tjdonaW0igA9u+ME0hfD2eSmevvWiRMcI+wMAWP/Q0+QLxfaF/j9n3F/+LSN7+PYTxZT3lBlXn3/6tH3DqkUGBVJ/uo+5c1K8kS9M+yfOjeenCcZMuo+T0qnSH3X/3D6efe1o6Txn/srJHDs+xSvjeTLpPvITUzjHjDnVWg8+6X7lwrU/3ce8wPz9ex92zJGxHH/w0NMc834n5l1/vjAVeg8BzODD71nIi6/nG9JIJEw4ZT0lWS5Y4t6n8nsSFLBJ51XLNUbtm7SiaacQ9f/cDW0qa6XjC9WZ2ceArwAp4BvOuS9V2r4WZSCEEL1Oxxeqc859D/heu+chhBC9SKf5vYQQQrQBKQMhhBBSBkIIIaQMhBBC0EHRREkxs0PASzXu/k7gHxs4nVbRrfOG7p275t16unXu3TLvM5xzM7J2u1YZ1IOZjYaFVnU63Tpv6N65a96tp1vn3q3z9pGZSAghhJSBEEKI3lUGd7V7AjXSrfOG7p275t16unXu3TpvoEd9BkIIIabTqysDIYQQAaQMhBBCzD5lYGYXmdk+M9tvZsMh388zsy3e90+Y2ZLAd+u98X1mtrYb5m1mS8wsb2a7vddfdNi8f9XMfmJmE2Z2Rdl315nZs97rutbNuu55Twbu99bWzbp0/mpz/6yZ/dTMnjazH5rZGYHvOvmeV5p3p9/z/2hme7z5/b9gD/d2ypVEOOdmzYti+evngPcAc4GngLPLtvkd4C+8958Etnjvz/a2nwcs9Y6T6oJ5LwH+voPv9xLgg8A3gSsC4wuB572fC7z3Czp93t53/9yO+51g7hcA/d77/xT4W+n0ex467y65528PvL8U+IH3vm1yJelrtq0MzgP2O+eed84dB+4DLivb5jLgHu/9A8CFZmbe+H3Oubeccy8A+73jdfq820nVeTvnXnTOPQ1Mle27FnjUOXfYOXcEeBRI1mmoduqZd7uJM/fHnHPHvI87KXYOhM6/51Hzbjdx5v7LwMeTOdG0rp1yJRGzTRkMAAcDn1/2xkK3cc5NAG8A74i5b7OoZ94AS81szMz+r5n962ZPNmxOHknuWaff70qcZGajZrbTzIYaO7WqJJ379cD3a9y3kdQzb+iCe25mN5rZc8B/BT6TZN9OoGOa24iaeRVY7Jx73czOBUbMbFnZk4poLGc453Jm9h5gh5ntcc491+5JlWNm1wKDwL9p91ySEDHvjr/nzrmvAV8zs08BtwAt9cnUy2xbGeSA0wOfT/PGQrexYu/l+cDrMfdtFjXP21t+vg7gnNtF0Sb5L5o+47I5eSS5Z51+vyNxzuW8n88DPwJWNnJyVYg1dzP7NeALwKXOubeS7Nsk6pl3V9zzAPcB/uqlnfc8Ge12WjTyRXGl8zxFR43v6FlWts2NTHfE3u+9X8Z0R8/ztM6BXM+8F/nzpOjgygELO2XegW3/kpkO5BcoOjIXeO+7Yd4LgHne+3cCz1LmTGz33CkKyueAM8vGO/qeV5h3N9zzMwPvLwFGvfdtkyuJr7PdE2jCL+5jwD94f1Rf8MZuo/ikAXAS8B2KjpwfA+8J7PsFb799wEe7Yd7Ax4G9wG7gJ8AlHTbvf0XRTnqU4gpsb2Df3/auZz/w6W6YN/BhYI/3D74HuL4D/8b/D/AL729iN7C1S+556Ly75J5/NfB/+BgBZdFOuZLkpXIUQgghZp3PQAghRA1IGQghhJAyEEIIIWUghBACKQMhhBBIGQghhEDKQAghBPD/AXjnEcFZn+6LAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LtItzvlQh2F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "e5fe3d36-662f-4e57-c1de-b7d4b32ac13f"
      },
      "source": [
        "plt.scatter(num_data.iloc[:, 0], target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbb0715cac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dq5N81MdQ62A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "27d48e9c-51e1-490c-bb66-1cf4978835ff"
      },
      "source": [
        "plt.scatter(num_data.iloc[:, 2], target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbb07406748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nkh8AczyRTzF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "9b0f4d4b-48ae-4c39-b9c1-4f5e5e759932"
      },
      "source": [
        "plt.scatter(num_data.iloc[:, 3], target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbb073a0898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Z9cQTOsRZVr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "39b2472a-719b-41e7-e97b-4e8d07540fae"
      },
      "source": [
        "cat_data.head"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of      Item_Identifier Item_Fat_Content  ... Outlet_Location_Type        Outlet_Type\n",
              "0              FDA15          Low Fat  ...               Tier 1  Supermarket Type1\n",
              "1              DRC01          Regular  ...               Tier 3  Supermarket Type2\n",
              "2              FDN15          Low Fat  ...               Tier 1  Supermarket Type1\n",
              "3              FDX07          Regular  ...               Tier 3      Grocery Store\n",
              "4              NCD19          Low Fat  ...               Tier 3  Supermarket Type1\n",
              "...              ...              ...  ...                  ...                ...\n",
              "8518           FDF22          Low Fat  ...               Tier 3  Supermarket Type1\n",
              "8519           FDS36          Regular  ...               Tier 2  Supermarket Type1\n",
              "8520           NCJ29          Low Fat  ...               Tier 2  Supermarket Type1\n",
              "8521           FDN46          Regular  ...               Tier 3  Supermarket Type2\n",
              "8522           DRG01          Low Fat  ...               Tier 1  Supermarket Type1\n",
              "\n",
              "[8523 rows x 7 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDQ40OrmRiEn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "86fc47d6-0434-4a0f-ae2a-8aa331016ee9"
      },
      "source": [
        "plt.scatter(cat_data.iloc[:, 1], target)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbb0730c2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rz9Xd44Trro",
        "colab_type": "text"
      },
      "source": [
        "Fill missing Data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPn_uG_CVkn-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "outputId": "ecaef3e9-0ea1-4c4b-f6f3-f0c10951418a"
      },
      "source": [
        "cat_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier            0\n",
              "Item_Fat_Content           0\n",
              "Item_Type                  0\n",
              "Outlet_Identifier          0\n",
              "Outlet_Size             2410\n",
              "Outlet_Location_Type       0\n",
              "Outlet_Type                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcsLQa5-VoCE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "ff064aca-4ccf-4fe6-9ded-386a22def62a"
      },
      "source": [
        "num_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Weight                  1463\n",
              "Item_Visibility                 0\n",
              "Item_MRP                        0\n",
              "Outlet_Establishment_Year       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SuTRfCf6Y4Fg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "5a8706d3-de26-43a4-fe0f-453f7874c0cf"
      },
      "source": [
        "num_data.interpolate()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.300</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>1999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.920</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>2009.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.500</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>1999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>19.200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>1998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.930</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>1987.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8518</th>\n",
              "      <td>6.865</td>\n",
              "      <td>0.056783</td>\n",
              "      <td>214.5218</td>\n",
              "      <td>1987.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8519</th>\n",
              "      <td>8.380</td>\n",
              "      <td>0.046982</td>\n",
              "      <td>108.1570</td>\n",
              "      <td>2002.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8520</th>\n",
              "      <td>10.600</td>\n",
              "      <td>0.035186</td>\n",
              "      <td>85.1224</td>\n",
              "      <td>2004.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8521</th>\n",
              "      <td>7.210</td>\n",
              "      <td>0.145221</td>\n",
              "      <td>103.1332</td>\n",
              "      <td>2009.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8522</th>\n",
              "      <td>14.800</td>\n",
              "      <td>0.044878</td>\n",
              "      <td>75.4670</td>\n",
              "      <td>1997.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8523 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year\n",
              "0           9.300         0.016047  249.8092                     1999.0\n",
              "1           5.920         0.019278   48.2692                     2009.0\n",
              "2          17.500         0.016760  141.6180                     1999.0\n",
              "3          19.200         0.000000  182.0950                     1998.0\n",
              "4           8.930         0.000000   53.8614                     1987.0\n",
              "...           ...              ...       ...                        ...\n",
              "8518        6.865         0.056783  214.5218                     1987.0\n",
              "8519        8.380         0.046982  108.1570                     2002.0\n",
              "8520       10.600         0.035186   85.1224                     2004.0\n",
              "8521        7.210         0.145221  103.1332                     2009.0\n",
              "8522       14.800         0.044878   75.4670                     1997.0\n",
              "\n",
              "[8523 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zad0vAsqY-PG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "3402d07f-038e-413f-e658-b0cfdb9f7809"
      },
      "source": [
        "num_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Weight                  1463\n",
              "Item_Visibility                 0\n",
              "Item_MRP                        0\n",
              "Outlet_Establishment_Year       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D81vim8tZAWh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in num_data:\n",
        "  num_data[i].interpolate(method = 'linear')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVFsIvY4ZS0P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "0335a22d-15ee-48fd-f9c5-87bf7bcbb1c7"
      },
      "source": [
        "num_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Weight                  1463\n",
              "Item_Visibility                 0\n",
              "Item_MRP                        0\n",
              "Outlet_Establishment_Year       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sBqWpY3wZUyz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_data = num_data.interpolate()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sor2n_6RbCbY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        },
        "outputId": "6fb90a87-c4fa-4729-bf8c-b82c22cd0356"
      },
      "source": [
        "num_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Weight                  0\n",
              "Item_Visibility              0\n",
              "Item_MRP                     0\n",
              "Outlet_Establishment_Year    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47QAv_dMbEWW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "37f5abe7-37d3-4cc3-9ccf-85cd4def7d59"
      },
      "source": [
        "num_data.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.300</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>1999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.920</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>2009.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.500</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>1999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>19.200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>1998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.930</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>1987.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>10.395</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>51.4008</td>\n",
              "      <td>2009.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>13.650</td>\n",
              "      <td>0.012741</td>\n",
              "      <td>57.6588</td>\n",
              "      <td>1987.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>14.925</td>\n",
              "      <td>0.127470</td>\n",
              "      <td>107.7622</td>\n",
              "      <td>1985.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>16.200</td>\n",
              "      <td>0.016687</td>\n",
              "      <td>96.9726</td>\n",
              "      <td>2002.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>19.200</td>\n",
              "      <td>0.094450</td>\n",
              "      <td>187.8214</td>\n",
              "      <td>2007.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Item_Weight  Item_Visibility  Item_MRP  Outlet_Establishment_Year\n",
              "0        9.300         0.016047  249.8092                     1999.0\n",
              "1        5.920         0.019278   48.2692                     2009.0\n",
              "2       17.500         0.016760  141.6180                     1999.0\n",
              "3       19.200         0.000000  182.0950                     1998.0\n",
              "4        8.930         0.000000   53.8614                     1987.0\n",
              "5       10.395         0.000000   51.4008                     2009.0\n",
              "6       13.650         0.012741   57.6588                     1987.0\n",
              "7       14.925         0.127470  107.7622                     1985.0\n",
              "8       16.200         0.016687   96.9726                     2002.0\n",
              "9       19.200         0.094450  187.8214                     2007.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zg6VOyK-bJPj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cat_data.fillna(method = 'bfill', inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4N0CAqWHbYgm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "f4fd751a-27d3-459b-a824-ec318b4a6bd4"
      },
      "source": [
        "cat_data.isnull().sum().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXAjXDRa64UH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "outputId": "7318c75e-7740-4a2d-898b-8c21fdb21322"
      },
      "source": [
        "cat_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>Meat</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>Household</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8518</th>\n",
              "      <td>FDF22</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>Snack Foods</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8519</th>\n",
              "      <td>FDS36</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Baking Goods</td>\n",
              "      <td>OUT045</td>\n",
              "      <td>Small</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8520</th>\n",
              "      <td>NCJ29</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>Health and Hygiene</td>\n",
              "      <td>OUT035</td>\n",
              "      <td>Small</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8521</th>\n",
              "      <td>FDN46</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Snack Foods</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8522</th>\n",
              "      <td>DRG01</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>OUT046</td>\n",
              "      <td>Small</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8523 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Item_Identifier Item_Fat_Content  ... Outlet_Location_Type        Outlet_Type\n",
              "0              FDA15          Low Fat  ...               Tier 1  Supermarket Type1\n",
              "1              DRC01          Regular  ...               Tier 3  Supermarket Type2\n",
              "2              FDN15          Low Fat  ...               Tier 1  Supermarket Type1\n",
              "3              FDX07          Regular  ...               Tier 3      Grocery Store\n",
              "4              NCD19          Low Fat  ...               Tier 3  Supermarket Type1\n",
              "...              ...              ...  ...                  ...                ...\n",
              "8518           FDF22          Low Fat  ...               Tier 3  Supermarket Type1\n",
              "8519           FDS36          Regular  ...               Tier 2  Supermarket Type1\n",
              "8520           NCJ29          Low Fat  ...               Tier 2  Supermarket Type1\n",
              "8521           FDN46          Regular  ...               Tier 3  Supermarket Type2\n",
              "8522           DRG01          Low Fat  ...               Tier 1  Supermarket Type1\n",
              "\n",
              "[8523 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cocTBi-u6fMy",
        "colab_type": "text"
      },
      "source": [
        "#Encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OE4sZWWk6UVe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "for i in cat_data:\n",
        "  cat_data[i] = le.fit_transform(cat_data[i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WltkTxU_7pqC",
        "colab_type": "code",
        "colab": {
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              "      Item_Identifier  Item_Fat_Content  ...  Outlet_Location_Type  Outlet_Type\n",
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        "df = pd.concat([cat_data, num_data, pd.DataFrame(target)], axis = 1)"
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      "outputs": []
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              "<bound method NDFrame.head of       Item_Identifier  Item_Fat_Content  ...  Outlet_Establishment_Year          0\n",
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              "[8523 rows x 12 columns]>"
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          "data": {
            "text/plain": [
              "False"
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              "      <td>1</td>\n",
              "      <td>8.930</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8518</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6.865</td>\n",
              "      <td>0.056783</td>\n",
              "      <td>214.5218</td>\n",
              "      <td>2778.3834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8519</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8.380</td>\n",
              "      <td>0.046982</td>\n",
              "      <td>108.1570</td>\n",
              "      <td>549.2850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8520</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.600</td>\n",
              "      <td>0.035186</td>\n",
              "      <td>85.1224</td>\n",
              "      <td>1193.1136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8521</th>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7.210</td>\n",
              "      <td>0.145221</td>\n",
              "      <td>103.1332</td>\n",
              "      <td>1845.5976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8522</th>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14.800</td>\n",
              "      <td>0.044878</td>\n",
              "      <td>75.4670</td>\n",
              "      <td>765.6700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8523 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Item_Fat_Content  Item_Type  ...  Item_MRP          0\n",
              "0                    1          4  ...  249.8092  3735.1380\n",
              "1                    2         14  ...   48.2692   443.4228\n",
              "2                    1         10  ...  141.6180  2097.2700\n",
              "3                    2          6  ...  182.0950   732.3800\n",
              "4                    1          9  ...   53.8614   994.7052\n",
              "...                ...        ...  ...       ...        ...\n",
              "8518                 1         13  ...  214.5218  2778.3834\n",
              "8519                 2          0  ...  108.1570   549.2850\n",
              "8520                 1          8  ...   85.1224  1193.1136\n",
              "8521                 2         13  ...  103.1332  1845.5976\n",
              "8522                 1         14  ...   75.4670   765.6700\n",
              "\n",
              "[8523 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUUKH0ol3RSw",
        "colab_type": "text"
      },
      "source": [
        "#Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlugUr8-3GxW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.iloc[:, : -1].values\n",
        "y = df.iloc[:, -1].values\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uonliLQB4x87",
        "colab_type": "text"
      },
      "source": [
        "#Computing values from all methods\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1rn_wGlM48AU",
        "colab_type": "text"
      },
      "source": [
        "1. Multiple Linear Regression\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3r3kQ9T47ne",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "7f87d126-2813-41d4-c877-cbcdd348c0b5"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VIS9YA56MGG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = lr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "score = r2_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXDq8Idu8skn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "cdc1e34c-f5a4-4f13-8832-089526da7aa9"
      },
      "source": [
        "print(score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5074702087220906\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ivHowrp97hb",
        "colab_type": "text"
      },
      "source": [
        "2. Polynomial Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYAn8cTG-GmJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "36b0713a-2aaf-4b40-ba22-52dfd073e9a7"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ddnu--27-THf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "6a397f9d-49a3-4ca4-fd06-30698988c997"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 3)\n",
        "X_poly = poly_reg.fit_transform(X_train)\n",
        "lr.fit(X_poly, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLKAz4F7-45X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "a994ca3f-ad4d-47f5-a7dd-104fa67f0cf6"
      },
      "source": [
        "y_pred = lr.predict(poly_reg.transform(X_test))\n",
        "from sklearn.metrics import r2_score\n",
        "score = r2_score(y_test, y_pred)\n",
        "print(score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.596423138152365\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5RO0tal_eU9",
        "colab_type": "text"
      },
      "source": [
        "3. Support Vector Machine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSg8om8A_qMw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "5489d6c0-7737-4877-9acd-0b7b06672f76"
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "svr = SVR(kernel = 'poly', degree = 2)\n",
        "svr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='scale',\n",
              "    kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMRUBnLn_Un_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "29836cf9-9eca-4875-f900-26cf235cdce9"
      },
      "source": [
        "y_pred = svr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "score = r2_score(y_test, y_pred)\n",
        "\n",
        "print(score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.2798251893319995\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kb99elJSDE8x",
        "colab_type": "text"
      },
      "source": [
        "4. Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lK9E0yQjA2TJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "594ab640-b854-440d-c20b-ffcc494adeec"
      },
      "source": [
        "from sklearn.tree  import DecisionTreeRegressor\n",
        "dtr = DecisionTreeRegressor(random_state = 0, max_depth = 1)\n",
        "dtr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=1,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=0, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYeiWlx0DnRp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "819a67b7-30d2-4183-89d4-88e67179f66c"
      },
      "source": [
        "y_pred = dtr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.23420989530535374"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z87P0ABnNOUR",
        "colab_type": "text"
      },
      "source": [
        "5. Random Forest regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tq4tCHy9DuYD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "18797b7b-55b5-43e4-adfb-7e8263cebff7"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rfr = RandomForestRegressor()\n",
        "rfr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RWl7IJPJNvG2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "38090e04-a02b-4e7b-9f2f-37c79f43727e"
      },
      "source": [
        "y_pred = rfr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5368165977375339"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_syvMqA1P19T",
        "colab_type": "text"
      },
      "source": [
        "# Model improvement"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NaMspEnSRAlt",
        "colab_type": "text"
      },
      "source": [
        "Let us try feature scaling in order to get better results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyypCHS5N8cH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import  StandardScaler\n",
        "ss = StandardScaler()\n",
        "y_train = y_train.reshape(len(y_train), 1)\n",
        "y_test = y_test.reshape(len(y_test), 1)\n",
        "y_train = ss.fit_transform(y_train)\n",
        "y_test = ss.transform(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQWyxAqVRYJb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "31d7b5c5-7c39-4562-d06d-b036a70e19ef"
      },
      "source": [
        "print(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-0.44149123]\n",
            " [-0.57306701]\n",
            " [-0.20098476]\n",
            " ...\n",
            " [ 0.41433698]\n",
            " [-1.14973593]\n",
            " [ 1.93468145]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M3uu5CbFRktJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "c46d8e5a-a39d-46d6-cf1c-caaa9660620e"
      },
      "source": [
        "print(y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1.36582118]\n",
            " [-0.63826925]\n",
            " [ 0.23317627]\n",
            " ...\n",
            " [-0.43446345]\n",
            " [ 0.59003164]\n",
            " [ 1.38690454]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQDozlQ2Rnkx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "c26b3fe2-7a0b-445c-d3fa-faf5d2e9d889"
      },
      "source": [
        "X_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.00000000e+00, 6.00000000e+00, 1.00000000e+00, ...,\n",
              "        1.06850000e+01, 8.72234200e-03, 1.23541400e+02],\n",
              "       [1.00000000e+00, 1.40000000e+01, 1.00000000e+00, ...,\n",
              "        1.87000000e+01, 1.46226250e-02, 5.20324000e+01],\n",
              "       [2.00000000e+00, 0.00000000e+00, 2.00000000e+00, ...,\n",
              "        1.58500000e+01, 1.21632721e-01, 6.06220000e+01],\n",
              "       ...,\n",
              "       [2.00000000e+00, 1.30000000e+01, 2.00000000e+00, ...,\n",
              "        1.35000000e+01, 0.00000000e+00, 7.83960000e+01],\n",
              "       [1.00000000e+00, 5.00000000e+00, 0.00000000e+00, ...,\n",
              "        1.87000000e+01, 1.04890428e-01, 1.21407200e+02],\n",
              "       [1.00000000e+00, 1.50000000e+01, 1.00000000e+00, ...,\n",
              "        1.40000000e+01, 4.13553660e-02, 1.82063400e+02]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dN2ENq5SUr9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "bf8d55f3-02c4-44a6-acde-a42f7204c206"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.300</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5.920</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>17.500</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>19.200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>8.930</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8518</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6.865</td>\n",
              "      <td>0.056783</td>\n",
              "      <td>214.5218</td>\n",
              "      <td>2778.3834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8519</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8.380</td>\n",
              "      <td>0.046982</td>\n",
              "      <td>108.1570</td>\n",
              "      <td>549.2850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8520</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.600</td>\n",
              "      <td>0.035186</td>\n",
              "      <td>85.1224</td>\n",
              "      <td>1193.1136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8521</th>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7.210</td>\n",
              "      <td>0.145221</td>\n",
              "      <td>103.1332</td>\n",
              "      <td>1845.5976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8522</th>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14.800</td>\n",
              "      <td>0.044878</td>\n",
              "      <td>75.4670</td>\n",
              "      <td>765.6700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8523 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Item_Fat_Content  Item_Type  ...  Item_MRP          0\n",
              "0                    1          4  ...  249.8092  3735.1380\n",
              "1                    2         14  ...   48.2692   443.4228\n",
              "2                    1         10  ...  141.6180  2097.2700\n",
              "3                    2          6  ...  182.0950   732.3800\n",
              "4                    1          9  ...   53.8614   994.7052\n",
              "...                ...        ...  ...       ...        ...\n",
              "8518                 1         13  ...  214.5218  2778.3834\n",
              "8519                 2          0  ...  108.1570   549.2850\n",
              "8520                 1          8  ...   85.1224  1193.1136\n",
              "8521                 2         13  ...  103.1332  1845.5976\n",
              "8522                 1         14  ...   75.4670   765.6700\n",
              "\n",
              "[8523 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reweyYFeTI4A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_x = StandardScaler()\n",
        "x_train = sc_x.fit_transform(X_train)\n",
        "X_test = sc_x.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QiLTPCJGTqGA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "515e89c2-e5aa-4f54-9b90-93727f82fcac"
      },
      "source": [
        "print(X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1.00000000e+00 6.00000000e+00 1.00000000e+00 ... 1.06850000e+01\n",
            "  8.72234200e-03 1.23541400e+02]\n",
            " [1.00000000e+00 1.40000000e+01 1.00000000e+00 ... 1.87000000e+01\n",
            "  1.46226250e-02 5.20324000e+01]\n",
            " [2.00000000e+00 0.00000000e+00 2.00000000e+00 ... 1.58500000e+01\n",
            "  1.21632721e-01 6.06220000e+01]\n",
            " ...\n",
            " [2.00000000e+00 1.30000000e+01 2.00000000e+00 ... 1.35000000e+01\n",
            "  0.00000000e+00 7.83960000e+01]\n",
            " [1.00000000e+00 5.00000000e+00 0.00000000e+00 ... 1.87000000e+01\n",
            "  1.04890428e-01 1.21407200e+02]\n",
            " [1.00000000e+00 1.50000000e+01 1.00000000e+00 ... 1.40000000e+01\n",
            "  4.13553660e-02 1.82063400e+02]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJBNKYveT1ax",
        "colab_type": "text"
      },
      "source": [
        "#Re-Evaluating models "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BpX6zGMxT8lm",
        "colab_type": "text"
      },
      "source": [
        "1. Multiple Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTh_aWJ2TwfG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "013b6ba2-0f38-4211-f5d4-c46bfb6a906f"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3sft4BKUC_G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "96110284-d3af-4f99-9ba4-9c664a9022f9"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXCYk0YwUGV9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "5d146cb4-4c68-458e-a199-0182d395d3e7"
      },
      "source": [
        "print(score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.2798251893319995\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "i_aMpa_5UiLo"
      },
      "source": [
        "2. Polynomial Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0P0drhauUiLp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "c8e8d138-5c25-4448-a172-bbdd59672b77"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uhXNdlHpUiLs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "01e90796-06ed-4624-ebfd-dcf6acf3ea9d"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 4)\n",
        "X_poly = poly_reg.fit_transform(X_train)\n",
        "lr.fit(X_poly, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hXWZW8Q2UiLu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "7a0f9b1e-62bf-4c92-ce48-6440f7e81f50"
      },
      "source": [
        "y_pred = lr.predict(poly_reg.transform(X_test))\n",
        "from sklearn.metrics import r2_score\n",
        "score = r2_score(y_test, y_pred)\n",
        "print(score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-2682337.8422195055\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "N12cxGObUiLw"
      },
      "source": [
        "3. Support Vector Machine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Hz4-7iJXUiLw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "64f74d0b-cfe9-413e-bd28-0a94a21fab37"
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "svr = SVR(kernel = 'poly', degree = 2)\n",
        "svr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: 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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=2, epsilon=0.1, gamma='scale',\n",
              "    kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "N4H9seDAUiLz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "654ddf34-6cfe-4a2f-be9c-94ee67893c9d"
      },
      "source": [
        "y_pred = svr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "score = r2_score(y_test, y_pred)\n",
        "\n",
        "print(score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "-0.8887568154777372\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "h6aJYGc-UiL1"
      },
      "source": [
        "4. Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GqRVRj7kUiL1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "d0089913-ab5e-43dd-f8d5-fa3decfd6b3e"
      },
      "source": [
        "from sklearn.tree  import DecisionTreeRegressor\n",
        "dtr = DecisionTreeRegressor(random_state = 0, max_depth = 1)\n",
        "dtr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=1,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=0, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IsfwTCy6UiL3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "f001dad0-6bc8-4f29-cada-edd76b6e7d5d"
      },
      "source": [
        "y_pred = dtr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.2357469085008821"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "u3Ms3lgVUiL5"
      },
      "source": [
        "5. Random Forest regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gLxZ-_rSUiL5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "edfd6916-9f53-4ba5-a1e0-050e3af9cc95"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rfr = RandomForestRegressor()\n",
        "rfr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/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"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=100, n_jobs=None, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BnzNglnFUiL7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "2fc6bd5e-2a89-4cb8-f34b-d685f4bf990f"
      },
      "source": [
        "y_pred = rfr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.2669240686787733"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_EcSfdm5VEib",
        "colab_type": "text"
      },
      "source": [
        "Since scaling resulted in poor performance, so let us revert the chnages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OyJDaC8NUsn4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.iloc[:, : -1].values\n",
        "y = df.iloc[:, -1].values\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIEa4bSBVSC1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "2b98a82c-c5a5-4ec1-e0af-437fc2677e56"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.300</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5.920</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>17.500</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>19.200</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>8.930</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8518</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>6.865</td>\n",
              "      <td>0.056783</td>\n",
              "      <td>214.5218</td>\n",
              "      <td>2778.3834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8519</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8.380</td>\n",
              "      <td>0.046982</td>\n",
              "      <td>108.1570</td>\n",
              "      <td>549.2850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8520</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.600</td>\n",
              "      <td>0.035186</td>\n",
              "      <td>85.1224</td>\n",
              "      <td>1193.1136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8521</th>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>7.210</td>\n",
              "      <td>0.145221</td>\n",
              "      <td>103.1332</td>\n",
              "      <td>1845.5976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8522</th>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14.800</td>\n",
              "      <td>0.044878</td>\n",
              "      <td>75.4670</td>\n",
              "      <td>765.6700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8523 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Item_Fat_Content  Item_Type  ...  Item_MRP          0\n",
              "0                    1          4  ...  249.8092  3735.1380\n",
              "1                    2         14  ...   48.2692   443.4228\n",
              "2                    1         10  ...  141.6180  2097.2700\n",
              "3                    2          6  ...  182.0950   732.3800\n",
              "4                    1          9  ...   53.8614   994.7052\n",
              "...                ...        ...  ...       ...        ...\n",
              "8518                 1         13  ...  214.5218  2778.3834\n",
              "8519                 2          0  ...  108.1570   549.2850\n",
              "8520                 1          8  ...   85.1224  1193.1136\n",
              "8521                 2         13  ...  103.1332  1845.5976\n",
              "8522                 1         14  ...   75.4670   765.6700\n",
              "\n",
              "[8523 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z7bn9WqQVzxR",
        "colab_type": "text"
      },
      "source": [
        "Let us do the plotting again, in order to see which dependent variable can be removed in order to get better results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEqqHCUwVWIP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "dc07f94e-8850-41f3-aa7a-7c31443fc054"
      },
      "source": [
        "plt.scatter(X[:, -3], y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbb00a47b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXOB0oB1ViXW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.drop('Item_Weight', inplace = True, axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82cMGEgYWtrP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "797b7b98-e3b5-4072-a0e3-08efc3c15856"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8518</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.056783</td>\n",
              "      <td>214.5218</td>\n",
              "      <td>2778.3834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8519</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.046982</td>\n",
              "      <td>108.1570</td>\n",
              "      <td>549.2850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8520</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.035186</td>\n",
              "      <td>85.1224</td>\n",
              "      <td>1193.1136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8521</th>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.145221</td>\n",
              "      <td>103.1332</td>\n",
              "      <td>1845.5976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8522</th>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.044878</td>\n",
              "      <td>75.4670</td>\n",
              "      <td>765.6700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8523 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Item_Fat_Content  Item_Type  ...  Item_MRP          0\n",
              "0                    1          4  ...  249.8092  3735.1380\n",
              "1                    2         14  ...   48.2692   443.4228\n",
              "2                    1         10  ...  141.6180  2097.2700\n",
              "3                    2          6  ...  182.0950   732.3800\n",
              "4                    1          9  ...   53.8614   994.7052\n",
              "...                ...        ...  ...       ...        ...\n",
              "8518                 1         13  ...  214.5218  2778.3834\n",
              "8519                 2          0  ...  108.1570   549.2850\n",
              "8520                 1          8  ...   85.1224  1193.1136\n",
              "8521                 2         13  ...  103.1332  1845.5976\n",
              "8522                 1         14  ...   75.4670   765.6700\n",
              "\n",
              "[8523 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djOTgeZpWuav",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = df.iloc[:, : -1].values\n",
        "y = df.iloc[:, -1].values\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sd5Xr-aSXE3D",
        "colab_type": "text"
      },
      "source": [
        "#Recomputation of models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8ZWDJfoXXWfT"
      },
      "source": [
        "1. Multiple Linear Regression\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9i5d1tywXWfT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "4b67fda5-b26f-4cd1-fa7e-8d5c8d617c8b"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KkaJ1pGtXWfW",
        "colab": {}
      },
      "source": [
        "y_pred = lr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "score = r2_score(y_test, y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "v2ULhlLAXWfY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "10147712-1c03-49f0-a570-febc8a876686"
      },
      "source": [
        "print(score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5074782834962333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YTVFpLVeXWfa"
      },
      "source": [
        "2. Polynomial Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HF-girrAXWfa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "954393e2-c94d-4355-8cb7-c2534e2e589a"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YlhsfZGvXWfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "63dea7ee-368d-4f60-e3b0-78b44a38aa19"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "poly_reg = PolynomialFeatures(degree = 3)\n",
        "X_poly = poly_reg.fit_transform(X_train)\n",
        "lr.fit(X_poly, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PrZ-fcCNXWfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "f475c211-1880-4da6-a85f-b7fc8ef77943"
      },
      "source": [
        "y_pred = lr.predict(poly_reg.transform(X_test))\n",
        "from sklearn.metrics import r2_score\n",
        "score1 = r2_score(y_test, y_pred)\n",
        "print(score1)\n",
        "lr.score(X_poly, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6002117979646328\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6073100178846744"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "X9VPOB3ZXWfg"
      },
      "source": [
        "3. Support Vector Machine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uRe9vptZXWfg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "2ae55848-b5a1-4e21-e931-c38a4a8be281"
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "svr = SVR(kernel = 'linear')\n",
        "svr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='scale',\n",
              "    kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DBtiPIe5XWfj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "dd3029eb-f868-4304-b34f-4788171a4d9d"
      },
      "source": [
        "y_pred = svr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "score = r2_score(y_test, y_pred)\n",
        "\n",
        "print(score)\n",
        "svr.score(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.46777597798936277\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.46650038949905004"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qDVeN6m4XWfl"
      },
      "source": [
        "4. Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yFUoKkanXWfl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "outputId": "13c8a661-7dc2-4e3b-dfda-e8a7d45e689a"
      },
      "source": [
        "from sklearn.tree  import DecisionTreeRegressor\n",
        "dtr = DecisionTreeRegressor(random_state = 0, max_depth = 30, min_samples_leaf= 100)\n",
        "dtr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=30,\n",
              "                      max_features=None, max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=100, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                      random_state=0, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GATl1GWVXWfn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "c231d6da-2511-45bd-dd1c-9a8481e7141e"
      },
      "source": [
        "y_pred = dtr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(r2)\n",
        "dtr.score(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5816379657766548\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6149317523750002"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "A9pS-tVbXWfp"
      },
      "source": [
        "5. Random Forest regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "I6TcObecXWfp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "d447f9df-b941-4304-aec2-e14e4bbed8bf"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rfr = RandomForestRegressor(n_estimators= 400, max_depth = 6, min_samples_leaf= 100, n_jobs = 4)\n",
        "rfr.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=6, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=100,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=400, n_jobs=4, oob_score=False,\n",
              "                      random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xK5M-9CJXWfr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "23a997d0-3ffa-4c73-c039-265033cae227"
      },
      "source": [
        "y_pred = rfr.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(r2)\n",
        "rfr.score(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5894412536659465\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6070398125496734"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vW5ALP7OXd_S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "29a2b340-ca48-4772-d767-adb173922f4a"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Outlet_Size</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8518</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.056783</td>\n",
              "      <td>214.5218</td>\n",
              "      <td>2778.3834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8519</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.046982</td>\n",
              "      <td>108.1570</td>\n",
              "      <td>549.2850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8520</th>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.035186</td>\n",
              "      <td>85.1224</td>\n",
              "      <td>1193.1136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8521</th>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.145221</td>\n",
              "      <td>103.1332</td>\n",
              "      <td>1845.5976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8522</th>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.044878</td>\n",
              "      <td>75.4670</td>\n",
              "      <td>765.6700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8523 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Item_Fat_Content  Item_Type  ...  Item_MRP          0\n",
              "0                    1          4  ...  249.8092  3735.1380\n",
              "1                    2         14  ...   48.2692   443.4228\n",
              "2                    1         10  ...  141.6180  2097.2700\n",
              "3                    2          6  ...  182.0950   732.3800\n",
              "4                    1          9  ...   53.8614   994.7052\n",
              "...                ...        ...  ...       ...        ...\n",
              "8518                 1         13  ...  214.5218  2778.3834\n",
              "8519                 2          0  ...  108.1570   549.2850\n",
              "8520                 1          8  ...   85.1224  1193.1136\n",
              "8521                 2         13  ...  103.1332  1845.5976\n",
              "8522                 1         14  ...   75.4670   765.6700\n",
              "\n",
              "[8523 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xISEvkqNtdr",
        "colab_type": "text"
      },
      "source": [
        "6. XGBRegressor "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YS5l_FGFLqFx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "outputId": "7d9cd0be-171f-44fd-cc32-4380884fc0b9"
      },
      "source": [
        "from xgboost import XGBRegressor\n",
        "xgb = XGBRegressor(n_estimators =1000, learning_rate = 0.05)\n",
        "xgb.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[06:21:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "             importance_type='gain', learning_rate=0.05, max_delta_step=0,\n",
              "             max_depth=3, min_child_weight=1, missing=None, n_estimators=1000,\n",
              "             n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
              "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "             silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0x1N_8L6MJtJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "6c08a3dc-6d05-43d4-846d-17f8de5ba86c"
      },
      "source": [
        "y_pred = xgb.predict(X_test)\n",
        "from sklearn.metrics import r2_score\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "print(r2)\n",
        "xgb.score(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5649115814837713\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7113539581705703"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0XzTge-MvKd",
        "colab_type": "text"
      },
      "source": [
        "Our Max score obtained is for XGBRegressor i.e. 71%, but highest r2_score is for Multiple Linear Regression model i.e. 61%, which is a good result."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bz8L9D-HNSjN",
        "colab_type": "text"
      },
      "source": [
        "But still, the accuracy is low, which can be dependent on many factors like:\n",
        "1. Less data\n",
        "2. More missing values\n"
      ]
    }
  ]
}