ModelTraining2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fD7KLUQmB_pR"
      },
      "source": [
        "# Loading Dependencies\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2Fc4mfCxCOop"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UKgHKyZCCV3P"
      },
      "source": [
        "# Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "VYAhBV7XCcjb",
        "outputId": "6fbd9829-88e3-4c7f-9dce-268ec5a32345"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Brand Category  Color Size Material  Price\n",
              "0   New Balance    Dress  White   XS    Nylon    182\n",
              "1   New Balance    Jeans  Black   XS     Silk     57\n",
              "2  Under Armour    Dress    Red    M     Wool    127\n",
              "3          Nike    Shoes  Green    M   Cotton     77\n",
              "4        Adidas  Sweater  White    M    Nylon    113"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Brand</th>\n",
              "      <th>Category</th>\n",
              "      <th>Color</th>\n",
              "      <th>Size</th>\n",
              "      <th>Material</th>\n",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>New Balance</td>\n",
              "      <td>Dress</td>\n",
              "      <td>White</td>\n",
              "      <td>XS</td>\n",
              "      <td>Nylon</td>\n",
              "      <td>182</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>New Balance</td>\n",
              "      <td>Jeans</td>\n",
              "      <td>Black</td>\n",
              "      <td>XS</td>\n",
              "      <td>Silk</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Under Armour</td>\n",
              "      <td>Dress</td>\n",
              "      <td>Red</td>\n",
              "      <td>M</td>\n",
              "      <td>Wool</td>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Nike</td>\n",
              "      <td>Shoes</td>\n",
              "      <td>Green</td>\n",
              "      <td>M</td>\n",
              "      <td>Cotton</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adidas</td>\n",
              "      <td>Sweater</td>\n",
              "      <td>White</td>\n",
              "      <td>M</td>\n",
              "      <td>Nylon</td>\n",
              "      <td>113</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"New Balance\",\n          \"Under Armour\",\n          \"Puma\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Dress\",\n          \"Jeans\",\n          \"T-shirt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"White\",\n          \"Black\",\n          \"Blue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"XS\",\n          \"M\",\n          \"L\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Material\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Nylon\",\n          \"Silk\",\n          \"Denim\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53,\n        \"min\": 10,\n        \"max\": 199,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          70,\n          189,\n          117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df = pd.read_csv('data.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tX7IZGDKE8qd",
        "outputId": "5d458611-c28f-4ce1-bdf5-92cff4b40f17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Brand     1000 non-null   object\n",
            " 1   Category  1000 non-null   object\n",
            " 2   Color     1000 non-null   object\n",
            " 3   Size      1000 non-null   object\n",
            " 4   Material  1000 non-null   object\n",
            " 5   Price     1000 non-null   int64 \n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 47.0+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "-YYpR1KYEMoe",
        "outputId": "a62b4572-84a8-4615-d0f1-4106df5cc70c"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "could not convert string to float: 'New Balance'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-becb50483c66>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcorrelation_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorrelation_matrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mcorr\u001b[0;34m(self, method, min_periods, numeric_only)\u001b[0m\n\u001b[1;32m  10052\u001b[0m         \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10053\u001b[0m         \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10054\u001b[0;31m         \u001b[0mmat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m  10055\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10056\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pearson\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mto_numpy\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1836\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1837\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1839\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1840\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mas_array\u001b[0;34m(self, dtype, copy, na_value)\u001b[0m\n\u001b[1;32m   1730\u001b[0m                 \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwriteable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1731\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1732\u001b[0;31m             \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_interleave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1733\u001b[0m             \u001b[0;31m# The underlying data was copied within _interleave, so no need\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;31m# to further copy if copy=True or setting na_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m_interleave\u001b[0;34m(self, dtype, na_value)\u001b[0m\n\u001b[1;32m   1792\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1793\u001b[0m                 \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1794\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1795\u001b[0m             \u001b[0mitemmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'New Balance'"
          ]
        }
      ],
      "source": [
        "correlation_matrix = df.corr()\n",
        "print(correlation_matrix)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IFNdz1dQhXZ3",
        "outputId": "8af26413-729f-4dab-c39b-63add915c2a7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Brand Category  Color Size Material  Price\n",
              "0   New Balance    Dress  White   XS    Nylon    182\n",
              "1   New Balance    Jeans  Black   XS     Silk     57\n",
              "2  Under Armour    Dress    Red    M     Wool    127\n",
              "3          Nike    Shoes  Green    M   Cotton     77\n",
              "4        Adidas  Sweater  White    M    Nylon    113"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-21c37890-88b3-41cd-958c-54f924b0872f\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "      <th>Brand</th>\n",
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              "      <th>0</th>\n",
              "      <td>New Balance</td>\n",
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              "      <td>XS</td>\n",
              "      <td>Nylon</td>\n",
              "      <td>182</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>New Balance</td>\n",
              "      <td>Jeans</td>\n",
              "      <td>Black</td>\n",
              "      <td>XS</td>\n",
              "      <td>Silk</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Under Armour</td>\n",
              "      <td>Dress</td>\n",
              "      <td>Red</td>\n",
              "      <td>M</td>\n",
              "      <td>Wool</td>\n",
              "      <td>127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Nike</td>\n",
              "      <td>Shoes</td>\n",
              "      <td>Green</td>\n",
              "      <td>M</td>\n",
              "      <td>Cotton</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adidas</td>\n",
              "      <td>Sweater</td>\n",
              "      <td>White</td>\n",
              "      <td>M</td>\n",
              "      <td>Nylon</td>\n",
              "      <td>113</td>\n",
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              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21c37890-88b3-41cd-958c-54f924b0872f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-21c37890-88b3-41cd-958c-54f924b0872f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-21c37890-88b3-41cd-958c-54f924b0872f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-db7e58a3-e567-48d9-9132-01c9339c91b8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-db7e58a3-e567-48d9-9132-01c9339c91b8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-db7e58a3-e567-48d9-9132-01c9339c91b8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"New Balance\",\n          \"Under Armour\",\n          \"Puma\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Dress\",\n          \"Jeans\",\n          \"T-shirt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"White\",\n          \"Black\",\n          \"Blue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"XS\",\n          \"M\",\n          \"L\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Material\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Nylon\",\n          \"Silk\",\n          \"Denim\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53,\n        \"min\": 10,\n        \"max\": 199,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          70,\n          189,\n          117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JaIS4lEeFe9-"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1utyrneFmcV",
        "outputId": "447f080b-a365-43d4-f10c-d413abd2d976"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brand       0\n",
              "Category    0\n",
              "Color       0\n",
              "Size        0\n",
              "Material    0\n",
              "Price       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JhzoNpXnFrQo"
      },
      "outputs": [],
      "source": [
        "drop_df = df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "14zYMzfNFuXh",
        "outputId": "36836996-14f2-4d9b-89cd-002fe0883090"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brand       0\n",
              "Category    0\n",
              "Color       0\n",
              "Size        0\n",
              "Material    0\n",
              "Price       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "drop_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97W84kYhFyMp",
        "outputId": "51572a29-9c56-4c70-ece1-2286bbb5875b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Brand     1000 non-null   object\n",
            " 1   Category  1000 non-null   object\n",
            " 2   Color     1000 non-null   object\n",
            " 3   Size      1000 non-null   object\n",
            " 4   Material  1000 non-null   object\n",
            " 5   Price     1000 non-null   int64 \n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 47.0+ KB\n"
          ]
        }
      ],
      "source": [
        "drop_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uEjOF11cGmA2"
      },
      "outputs": [],
      "source": [
        "no_duplicates = drop_df.drop_duplicates()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Af0YSALGrpc",
        "outputId": "f4f2070a-6f76-49bc-97b8-07d5f44893eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Brand     1000 non-null   object\n",
            " 1   Category  1000 non-null   object\n",
            " 2   Color     1000 non-null   object\n",
            " 3   Size      1000 non-null   object\n",
            " 4   Material  1000 non-null   object\n",
            " 5   Price     1000 non-null   int64 \n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 47.0+ KB\n"
          ]
        }
      ],
      "source": [
        "no_duplicates.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tNKQiEj93bv7"
      },
      "source": [
        "# One Hot Categorical Endcoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i88eJQpfniy_",
        "outputId": "c8f3e326-9d5c-494e-9425-dc621e4e909d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Dress', 'Jeans', 'Shoes', 'Sweater', 'Jacket', 'T-shirt'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df['Category'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TDHFpGlH3kLV"
      },
      "outputs": [],
      "source": [
        "df = pd.get_dummies(df, columns=['Category'], prefix='Category', dtype='int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J6J_fqZ3xkb7",
        "outputId": "72b8ecd6-7368-4744-f37e-4af7343eac5d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['White', 'Black', 'Red', 'Green', 'Yellow', 'Blue'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df['Color'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4KFHWIrX4bPY"
      },
      "outputs": [],
      "source": [
        "df = pd.get_dummies(df, columns=['Color'], prefix='Color', dtype='int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jvn1MCYFxoBl",
        "outputId": "b3c710e7-6c80-428d-bb20-ab0cd6499457"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['XS', 'M', 'XL', 'XXL', 'S', 'L'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df['Size'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vEDqQsGs4hKu"
      },
      "outputs": [],
      "source": [
        "df = pd.get_dummies(df, columns=['Size'], prefix='Size', dtype='int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d82R9O-_xtNb",
        "outputId": "99438c7f-10c3-4be1-ea47-ada0096db5dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Nylon', 'Silk', 'Wool', 'Cotton', 'Polyester', 'Denim'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df['Material'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VVunzRtJ4iG3"
      },
      "outputs": [],
      "source": [
        "df = pd.get_dummies(df, columns=['Material'], prefix='Material', dtype='int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0jQrxfKaEcN",
        "outputId": "8f187902-5ad7-4a87-924c-f258790154e4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['New Balance', 'Under Armour', 'Nike', 'Adidas', 'Reebok', 'Puma'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df['Brand'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_yUn1Z5RZfVH"
      },
      "outputs": [],
      "source": [
        "df = pd.get_dummies(df, columns=['Brand'], prefix='Brand', dtype='int64')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "0bmNFs1a4Veg",
        "outputId": "a1bbbf22-a0b5-4cd7-e050-65234aa5769a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Price  Category_Dress  Category_Jacket  Category_Jeans  Category_Shoes  \\\n",
              "0      182               1                0               0               0   \n",
              "1       57               0                0               1               0   \n",
              "2      127               1                0               0               0   \n",
              "3       77               0                0               0               1   \n",
              "4      113               0                0               0               0   \n",
              "..     ...             ...              ...             ...             ...   \n",
              "995    176               0                0               1               0   \n",
              "996    110               0                1               0               0   \n",
              "997    127               0                0               0               0   \n",
              "998     69               0                0               0               0   \n",
              "999    174               0                1               0               0   \n",
              "\n",
              "     Category_Sweater  Category_T-shirt  Color_Black  Color_Blue  Color_Green  \\\n",
              "0                   0                 0            0           0            0   \n",
              "1                   0                 0            1           0            0   \n",
              "2                   0                 0            0           0            0   \n",
              "3                   0                 0            0           0            1   \n",
              "4                   1                 0            0           0            0   \n",
              "..                ...               ...          ...         ...          ...   \n",
              "995                 0                 0            1           0            0   \n",
              "996                 0                 0            0           0            0   \n",
              "997                 1                 0            0           1            0   \n",
              "998                 1                 0            1           0            0   \n",
              "999                 0                 0            0           0            0   \n",
              "\n",
              "     ...  Material_Nylon  Material_Polyester  Material_Silk  Material_Wool  \\\n",
              "0    ...               1                   0              0              0   \n",
              "1    ...               0                   0              1              0   \n",
              "2    ...               0                   0              0              1   \n",
              "3    ...               0                   0              0              0   \n",
              "4    ...               1                   0              0              0   \n",
              "..   ...             ...                 ...            ...            ...   \n",
              "995  ...               0                   1              0              0   \n",
              "996  ...               0                   0              1              0   \n",
              "997  ...               0                   0              0              0   \n",
              "998  ...               0                   0              0              0   \n",
              "999  ...               0                   0              0              1   \n",
              "\n",
              "     Brand_Adidas  Brand_New Balance  Brand_Nike  Brand_Puma  Brand_Reebok  \\\n",
              "0               0                  1           0           0             0   \n",
              "1               0                  1           0           0             0   \n",
              "2               0                  0           0           0             0   \n",
              "3               0                  0           1           0             0   \n",
              "4               1                  0           0           0             0   \n",
              "..            ...                ...         ...         ...           ...   \n",
              "995             0                  0           0           1             0   \n",
              "996             0                  0           0           1             0   \n",
              "997             0                  0           0           0             1   \n",
              "998             0                  0           0           0             0   \n",
              "999             0                  1           0           0             0   \n",
              "\n",
              "     Brand_Under Armour  \n",
              "0                     0  \n",
              "1                     0  \n",
              "2                     1  \n",
              "3                     0  \n",
              "4                     0  \n",
              "..                  ...  \n",
              "995                   0  \n",
              "996                   0  \n",
              "997                   0  \n",
              "998                   1  \n",
              "999                   0  \n",
              "\n",
              "[1000 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc37f070-4a40-4631-bc8e-7be087641157\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>...</th>\n",
              "      <th>Material_Nylon</th>\n",
              "      <th>Material_Polyester</th>\n",
              "      <th>Material_Silk</th>\n",
              "      <th>Material_Wool</th>\n",
              "      <th>Brand_Adidas</th>\n",
              "      <th>Brand_New Balance</th>\n",
              "      <th>Brand_Nike</th>\n",
              "      <th>Brand_Puma</th>\n",
              "      <th>Brand_Reebok</th>\n",
              "      <th>Brand_Under Armour</th>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>113</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>176</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>110</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>127</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc37f070-4a40-4631-bc8e-7be087641157')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fc37f070-4a40-4631-bc8e-7be087641157 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fc37f070-4a40-4631-bc8e-7be087641157');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d51e6f4e-e913-4d8f-a6e5-1a59fde7d2ea\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d51e6f4e-e913-4d8f-a6e5-1a59fde7d2ea')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d51e6f4e-e913-4d8f-a6e5-1a59fde7d2ea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eI6wybEf48ZK"
      },
      "source": [
        "# Min Max Scaling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "-N2_AudFlIvu",
        "outputId": "61f40f16-9c4b-47e6-9abf-fc0acb8ecb94"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Price  Category_Dress  Category_Jacket  Category_Jeans  \\\n",
              "count  1000.000000     1000.000000      1000.000000     1000.000000   \n",
              "mean    106.289000        0.166000         0.191000        0.167000   \n",
              "std      53.695444        0.372267         0.393286        0.373162   \n",
              "min      10.000000        0.000000         0.000000        0.000000   \n",
              "25%      59.750000        0.000000         0.000000        0.000000   \n",
              "50%     108.000000        0.000000         0.000000        0.000000   \n",
              "75%     150.000000        0.000000         0.000000        0.000000   \n",
              "max     199.000000        1.000000         1.000000        1.000000   \n",
              "\n",
              "       Category_Shoes  Category_Sweater  Category_T-shirt  Color_Black  \\\n",
              "count     1000.000000       1000.000000       1000.000000   1000.00000   \n",
              "mean         0.172000          0.160000          0.144000      0.16300   \n",
              "std          0.377569          0.366789          0.351265      0.36955   \n",
              "min          0.000000          0.000000          0.000000      0.00000   \n",
              "25%          0.000000          0.000000          0.000000      0.00000   \n",
              "50%          0.000000          0.000000          0.000000      0.00000   \n",
              "75%          0.000000          0.000000          0.000000      0.00000   \n",
              "max          1.000000          1.000000          1.000000      1.00000   \n",
              "\n",
              "       Color_Blue  Color_Green  ...  Material_Nylon  Material_Polyester  \\\n",
              "count  1000.00000  1000.000000  ...     1000.000000         1000.000000   \n",
              "mean      0.16300     0.162000  ...        0.155000            0.175000   \n",
              "std       0.36955     0.368635  ...        0.362086            0.380157   \n",
              "min       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "25%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "50%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "75%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "max       1.00000     1.000000  ...        1.000000            1.000000   \n",
              "\n",
              "       Material_Silk  Material_Wool  Brand_Adidas  Brand_New Balance  \\\n",
              "count    1000.000000    1000.000000   1000.000000        1000.000000   \n",
              "mean        0.173000       0.172000      0.166000           0.164000   \n",
              "std         0.378437       0.377569      0.372267           0.370461   \n",
              "min         0.000000       0.000000      0.000000           0.000000   \n",
              "25%         0.000000       0.000000      0.000000           0.000000   \n",
              "50%         0.000000       0.000000      0.000000           0.000000   \n",
              "75%         0.000000       0.000000      0.000000           0.000000   \n",
              "max         1.000000       1.000000      1.000000           1.000000   \n",
              "\n",
              "        Brand_Nike   Brand_Puma  Brand_Reebok  Brand_Under Armour  \n",
              "count  1000.000000  1000.000000   1000.000000         1000.000000  \n",
              "mean      0.165000     0.168000      0.158000            0.179000  \n",
              "std       0.371366     0.374053      0.364924            0.383544  \n",
              "min       0.000000     0.000000      0.000000            0.000000  \n",
              "25%       0.000000     0.000000      0.000000            0.000000  \n",
              "50%       0.000000     0.000000      0.000000            0.000000  \n",
              "75%       0.000000     0.000000      0.000000            0.000000  \n",
              "max       1.000000     1.000000      1.000000            1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb03e8a2-67c6-4ceb-a1a8-39484f2f31e6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Category_Dress</th>\n",
              "      <th>Category_Jacket</th>\n",
              "      <th>Category_Jeans</th>\n",
              "      <th>Category_Shoes</th>\n",
              "      <th>Category_Sweater</th>\n",
              "      <th>Category_T-shirt</th>\n",
              "      <th>Color_Black</th>\n",
              "      <th>Color_Blue</th>\n",
              "      <th>Color_Green</th>\n",
              "      <th>...</th>\n",
              "      <th>Material_Nylon</th>\n",
              "      <th>Material_Polyester</th>\n",
              "      <th>Material_Silk</th>\n",
              "      <th>Material_Wool</th>\n",
              "      <th>Brand_Adidas</th>\n",
              "      <th>Brand_New Balance</th>\n",
              "      <th>Brand_Nike</th>\n",
              "      <th>Brand_Puma</th>\n",
              "      <th>Brand_Reebok</th>\n",
              "      <th>Brand_Under Armour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.00000</td>\n",
              "      <td>1000.00000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>106.289000</td>\n",
              "      <td>0.166000</td>\n",
              "      <td>0.191000</td>\n",
              "      <td>0.167000</td>\n",
              "      <td>0.172000</td>\n",
              "      <td>0.160000</td>\n",
              "      <td>0.144000</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.162000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.155000</td>\n",
              "      <td>0.175000</td>\n",
              "      <td>0.173000</td>\n",
              "      <td>0.172000</td>\n",
              "      <td>0.166000</td>\n",
              "      <td>0.164000</td>\n",
              "      <td>0.165000</td>\n",
              "      <td>0.168000</td>\n",
              "      <td>0.158000</td>\n",
              "      <td>0.179000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>53.695444</td>\n",
              "      <td>0.372267</td>\n",
              "      <td>0.393286</td>\n",
              "      <td>0.373162</td>\n",
              "      <td>0.377569</td>\n",
              "      <td>0.366789</td>\n",
              "      <td>0.351265</td>\n",
              "      <td>0.36955</td>\n",
              "      <td>0.36955</td>\n",
              "      <td>0.368635</td>\n",
              "      <td>...</td>\n",
              "      <td>0.362086</td>\n",
              "      <td>0.380157</td>\n",
              "      <td>0.378437</td>\n",
              "      <td>0.377569</td>\n",
              "      <td>0.372267</td>\n",
              "      <td>0.370461</td>\n",
              "      <td>0.371366</td>\n",
              "      <td>0.374053</td>\n",
              "      <td>0.364924</td>\n",
              "      <td>0.383544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>59.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>108.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>199.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb03e8a2-67c6-4ceb-a1a8-39484f2f31e6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bb03e8a2-67c6-4ceb-a1a8-39484f2f31e6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bb03e8a2-67c6-4ceb-a1a8-39484f2f31e6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f5b20ac7-703b-4fdb-a6cc-3a4c5357fc0d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f5b20ac7-703b-4fdb-a6cc-3a4c5357fc0d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f5b20ac7-703b-4fdb-a6cc-3a4c5357fc0d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B_E3qA2lA0mZ"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sNHFaBRjDoPH"
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2jhnPboSMFaf"
      },
      "outputs": [],
      "source": [
        "standardized_data = scaler.fit_transform(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b49ChDspUhl6"
      },
      "outputs": [],
      "source": [
        "standardized_df = pd.DataFrame(standardized_data, columns=df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "js1IRjAFDtcn",
        "outputId": "98b5dd22-0a87-4138-8d77-c131cd1a2265"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 31 columns):\n",
            " #   Column              Non-Null Count  Dtype\n",
            "---  ------              --------------  -----\n",
            " 0   Price               1000 non-null   int64\n",
            " 1   Category_Dress      1000 non-null   int64\n",
            " 2   Category_Jacket     1000 non-null   int64\n",
            " 3   Category_Jeans      1000 non-null   int64\n",
            " 4   Category_Shoes      1000 non-null   int64\n",
            " 5   Category_Sweater    1000 non-null   int64\n",
            " 6   Category_T-shirt    1000 non-null   int64\n",
            " 7   Color_Black         1000 non-null   int64\n",
            " 8   Color_Blue          1000 non-null   int64\n",
            " 9   Color_Green         1000 non-null   int64\n",
            " 10  Color_Red           1000 non-null   int64\n",
            " 11  Color_White         1000 non-null   int64\n",
            " 12  Color_Yellow        1000 non-null   int64\n",
            " 13  Size_L              1000 non-null   int64\n",
            " 14  Size_M              1000 non-null   int64\n",
            " 15  Size_S              1000 non-null   int64\n",
            " 16  Size_XL             1000 non-null   int64\n",
            " 17  Size_XS             1000 non-null   int64\n",
            " 18  Size_XXL            1000 non-null   int64\n",
            " 19  Material_Cotton     1000 non-null   int64\n",
            " 20  Material_Denim      1000 non-null   int64\n",
            " 21  Material_Nylon      1000 non-null   int64\n",
            " 22  Material_Polyester  1000 non-null   int64\n",
            " 23  Material_Silk       1000 non-null   int64\n",
            " 24  Material_Wool       1000 non-null   int64\n",
            " 25  Brand_Adidas        1000 non-null   int64\n",
            " 26  Brand_New Balance   1000 non-null   int64\n",
            " 27  Brand_Nike          1000 non-null   int64\n",
            " 28  Brand_Puma          1000 non-null   int64\n",
            " 29  Brand_Reebok        1000 non-null   int64\n",
            " 30  Brand_Under Armour  1000 non-null   int64\n",
            "dtypes: int64(31)\n",
            "memory usage: 242.3 KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "TNf8zzKZTruc",
        "outputId": "0e321922-deae-4a15-d45e-632681050a16"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Price  Category_Dress  Category_Jacket  Category_Jeans  Category_Shoes  \\\n",
              "0    182               1                0               0               0   \n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
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          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "lqDQ3etnVwOG",
        "outputId": "ab0dff16-d089-4c0d-bf3b-8a493f4edee2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Price  Category_Dress  Category_Jacket  Category_Jeans  Category_Shoes  \\\n",
              "0  0.910053             1.0              0.0             0.0             0.0   \n",
              "1  0.248677             0.0              0.0             1.0             0.0   \n",
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              "3  0.354497             0.0              0.0             0.0             1.0   \n",
              "4  0.544974             0.0              0.0             0.0             0.0   \n",
              "\n",
              "   Category_Sweater  Category_T-shirt  Color_Black  Color_Blue  Color_Green  \\\n",
              "0               0.0               0.0          0.0         0.0          0.0   \n",
              "1               0.0               0.0          1.0         0.0          0.0   \n",
              "2               0.0               0.0          0.0         0.0          0.0   \n",
              "3               0.0               0.0          0.0         0.0          1.0   \n",
              "4               1.0               0.0          0.0         0.0          0.0   \n",
              "\n",
              "   ...  Material_Nylon  Material_Polyester  Material_Silk  Material_Wool  \\\n",
              "0  ...             1.0                 0.0            0.0            0.0   \n",
              "1  ...             0.0                 0.0            1.0            0.0   \n",
              "2  ...             0.0                 0.0            0.0            1.0   \n",
              "3  ...             0.0                 0.0            0.0            0.0   \n",
              "4  ...             1.0                 0.0            0.0            0.0   \n",
              "\n",
              "   Brand_Adidas  Brand_New Balance  Brand_Nike  Brand_Puma  Brand_Reebok  \\\n",
              "0           0.0                1.0         0.0         0.0           0.0   \n",
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              "      <th>Color_Green</th>\n",
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              "      <th>Material_Polyester</th>\n",
              "      <th>Material_Silk</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.248677</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.619048</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.354497</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.544974</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3be6e0dc-f75e-42f0-93c8-76ca0e14b278')\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "standardized_df"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "standardized_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "7Juio3gbV94Q",
        "outputId": "e853be4e-8501-464f-f078-d86e49934474"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Price  Category_Dress  Category_Jacket  Category_Jeans  \\\n",
              "count  1000.000000     1000.000000      1000.000000     1000.000000   \n",
              "mean      0.509466        0.166000         0.191000        0.167000   \n",
              "std       0.284103        0.372267         0.393286        0.373162   \n",
              "min       0.000000        0.000000         0.000000        0.000000   \n",
              "25%       0.263228        0.000000         0.000000        0.000000   \n",
              "50%       0.518519        0.000000         0.000000        0.000000   \n",
              "75%       0.740741        0.000000         0.000000        0.000000   \n",
              "max       1.000000        1.000000         1.000000        1.000000   \n",
              "\n",
              "       Category_Shoes  Category_Sweater  Category_T-shirt  Color_Black  \\\n",
              "count     1000.000000       1000.000000       1000.000000   1000.00000   \n",
              "mean         0.172000          0.160000          0.144000      0.16300   \n",
              "std          0.377569          0.366789          0.351265      0.36955   \n",
              "min          0.000000          0.000000          0.000000      0.00000   \n",
              "25%          0.000000          0.000000          0.000000      0.00000   \n",
              "50%          0.000000          0.000000          0.000000      0.00000   \n",
              "75%          0.000000          0.000000          0.000000      0.00000   \n",
              "max          1.000000          1.000000          1.000000      1.00000   \n",
              "\n",
              "       Color_Blue  Color_Green  ...  Material_Nylon  Material_Polyester  \\\n",
              "count  1000.00000  1000.000000  ...     1000.000000         1000.000000   \n",
              "mean      0.16300     0.162000  ...        0.155000            0.175000   \n",
              "std       0.36955     0.368635  ...        0.362086            0.380157   \n",
              "min       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "25%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "50%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "75%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "max       1.00000     1.000000  ...        1.000000            1.000000   \n",
              "\n",
              "       Material_Silk  Material_Wool  Brand_Adidas  Brand_New Balance  \\\n",
              "count    1000.000000    1000.000000   1000.000000        1000.000000   \n",
              "mean        0.173000       0.172000      0.166000           0.164000   \n",
              "std         0.378437       0.377569      0.372267           0.370461   \n",
              "min         0.000000       0.000000      0.000000           0.000000   \n",
              "25%         0.000000       0.000000      0.000000           0.000000   \n",
              "50%         0.000000       0.000000      0.000000           0.000000   \n",
              "75%         0.000000       0.000000      0.000000           0.000000   \n",
              "max         1.000000       1.000000      1.000000           1.000000   \n",
              "\n",
              "        Brand_Nike   Brand_Puma  Brand_Reebok  Brand_Under Armour  \n",
              "count  1000.000000  1000.000000   1000.000000         1000.000000  \n",
              "mean      0.165000     0.168000      0.158000            0.179000  \n",
              "std       0.371366     0.374053      0.364924            0.383544  \n",
              "min       0.000000     0.000000      0.000000            0.000000  \n",
              "25%       0.000000     0.000000      0.000000            0.000000  \n",
              "50%       0.000000     0.000000      0.000000            0.000000  \n",
              "75%       0.000000     0.000000      0.000000            0.000000  \n",
              "max       1.000000     1.000000      1.000000            1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Category_Dress</th>\n",
              "      <th>Category_Jacket</th>\n",
              "      <th>Category_Jeans</th>\n",
              "      <th>Category_Shoes</th>\n",
              "      <th>Category_Sweater</th>\n",
              "      <th>Category_T-shirt</th>\n",
              "      <th>Color_Black</th>\n",
              "      <th>Color_Blue</th>\n",
              "      <th>Color_Green</th>\n",
              "      <th>...</th>\n",
              "      <th>Material_Nylon</th>\n",
              "      <th>Material_Polyester</th>\n",
              "      <th>Material_Silk</th>\n",
              "      <th>Material_Wool</th>\n",
              "      <th>Brand_Adidas</th>\n",
              "      <th>Brand_New Balance</th>\n",
              "      <th>Brand_Nike</th>\n",
              "      <th>Brand_Puma</th>\n",
              "      <th>Brand_Reebok</th>\n",
              "      <th>Brand_Under Armour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.00000</td>\n",
              "      <td>1000.00000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>...</td>\n",
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              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.509466</td>\n",
              "      <td>0.166000</td>\n",
              "      <td>0.191000</td>\n",
              "      <td>0.167000</td>\n",
              "      <td>0.172000</td>\n",
              "      <td>0.160000</td>\n",
              "      <td>0.144000</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.162000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.155000</td>\n",
              "      <td>0.175000</td>\n",
              "      <td>0.173000</td>\n",
              "      <td>0.172000</td>\n",
              "      <td>0.166000</td>\n",
              "      <td>0.164000</td>\n",
              "      <td>0.165000</td>\n",
              "      <td>0.168000</td>\n",
              "      <td>0.158000</td>\n",
              "      <td>0.179000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.284103</td>\n",
              "      <td>0.372267</td>\n",
              "      <td>0.393286</td>\n",
              "      <td>0.373162</td>\n",
              "      <td>0.377569</td>\n",
              "      <td>0.366789</td>\n",
              "      <td>0.351265</td>\n",
              "      <td>0.36955</td>\n",
              "      <td>0.36955</td>\n",
              "      <td>0.368635</td>\n",
              "      <td>...</td>\n",
              "      <td>0.362086</td>\n",
              "      <td>0.380157</td>\n",
              "      <td>0.378437</td>\n",
              "      <td>0.377569</td>\n",
              "      <td>0.372267</td>\n",
              "      <td>0.370461</td>\n",
              "      <td>0.371366</td>\n",
              "      <td>0.374053</td>\n",
              "      <td>0.364924</td>\n",
              "      <td>0.383544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.263228</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.518519</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.740741</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6049cc8-f5b9-4c03-8a9d-37ac2f5574aa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c6049cc8-f5b9-4c03-8a9d-37ac2f5574aa button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c6049cc8-f5b9-4c03-8a9d-37ac2f5574aa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ad3876f1-678d-43c7-9b6a-7af40c003d54\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ad3876f1-678d-43c7-9b6a-7af40c003d54')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ad3876f1-678d-43c7-9b6a-7af40c003d54 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "standardized_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IW61DcwrU0gL"
      },
      "source": [
        "# Removing Outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A-ES1q-fVUcd"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7h3o-RD8VKxT"
      },
      "source": [
        "Z scaling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "bXzXmMsIU7Jr",
        "outputId": "cbf60ada-9974-4b5b-fd35-6729c7f11923"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "upper limit: 267.3753312547564\n",
            "lower limit: -54.79733125475643\n",
            "before removing outliers: 1000\n",
            "after removing outliers: 1000\n",
            "outliers: 0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Price'>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "upper_limit = df['Price'].mean() + 3*df['Price'].std()\n",
        "lower_limit = df['Price'].mean() - 3*df['Price'].std()\n",
        "print('upper limit:', upper_limit)\n",
        "print('lower limit:', lower_limit)\n",
        "df.loc[(df['Price'] > upper_limit) | (df['Price'] < lower_limit)]\n",
        "new_df = df.loc[(df['Price'] <= upper_limit) & (df['Price'] >= lower_limit)]\n",
        "print('before removing outliers:', len(df))\n",
        "print('after removing outliers:',len(new_df))\n",
        "print('outliers:', len(df)-len(new_df))\n",
        "sns.boxplot(new_df['Price'])\n",
        "new_df = df.copy()\n",
        "new_df.loc[(new_df['Price']>=upper_limit),'Price'] = upper_limit\n",
        "new_df.loc[(new_df['Price']<=lower_limit), 'Price'] = lower_limit\n",
        "sns.boxplot(new_df['Price'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "72brhok8VMgt"
      },
      "source": [
        "IQR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "nfTLVSt0VOFg",
        "outputId": "9ae2be03-0987-430c-ca8a-755b8ee3127c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "before removing outliers: 1000\n",
            "after removing outliers: 1000\n",
            "outliers: 0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Price'>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGKCAYAAADqqIAWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAf80lEQVR4nO3dfVCVdf7/8ddB4qAGBw/J3XRUcistwbtaZHRdTFZF12ql3dFoVzdHqkEaYXczdqzEqYXVNsuWrO1Ga1bWthmzzVoLLKESSW3I6WbZYDFquNHJLxzFEUHO7w/X8+us4g0euA4fn4+Za+K6ORfv0x1Pr3Odg83j8XgEAABgqCCrBwAAAOhNxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAowVbPUAg6OrqUkNDg8LCwmSz2aweBwAAXACPx6MjR44oLi5OQUHdX78hdiQ1NDTI5XJZPQYAAOiBb775RldffXW3+4kdSWFhYZJO/c0KDw+3eBoAAHAh3G63XC6X9+d4d4gdyfvSVXh4OLEDAEA/c75bULhBGQAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0S2OnoKBAN998s8LCwhQVFaXbb79d1dXVPsccP35cWVlZioyM1JVXXqn09HQ1Nzf7HFNfX685c+Zo0KBBioqK0u9+9zt1dnb25VMBAAABytLYKSsrU1ZWlnbv3q2SkhJ1dHRoxowZamtr8x6Tk5OjN998U6+99prKysrU0NCgefPmefefPHlSc+bM0YkTJ7Rr1y69/PLL2rhxox5++GErnhIAAAgwNo/H47F6iNMOHTqkqKgolZWVaerUqWptbdXQoUNVXFysO+64Q5L0r3/9S6NHj1ZFRYUmTZqkf/7zn/rpT3+qhoYGRUdHS5KeffZZLV++XIcOHVJISMh5v6/b7ZbD4VBrayu/LgIAgH7iQn9+B9Q9O62trZIkp9MpSdq3b586OjqUmprqPWbUqFEaNmyYKioqJEkVFRVKSEjwho4kzZw5U263W59//vlZv097e7vcbrfPAgAAzBQwvwi0q6tLy5Yt0+TJkzVmzBhJUlNTk0JCQhQREeFzbHR0tJqamrzHfD90Tu8/ve9sCgoKlJ+f7+dnAJzS3Nys0tJSq8cAAlZqauoZ/98GelPAxE5WVpY+++wzffjhh73+vfLy8pSbm+tdP/0r4gF/KC0t1XPPPWf1GEBAy8jIsHoEXEYCInaWLl2qbdu2qby8XFdffbV3e0xMjE6cOKGWlhafqzvNzc2KiYnxHvPxxx/7nO/0u7VOH/O/7Ha77Ha7n58FcMr3X3aF9aqqqlRZWamkpCSNGzfO6nEg/htB37M0djwej7Kzs/X6669r586dio+P99k/ceJEXXHFFdqxY4fS09MlSdXV1aqvr1dycrIkKTk5WY899pgOHjyoqKgoSVJJSYnCw8N1ww039O0TAnTqZVT+1BpYKisrNW7cOP65AJcpS2MnKytLxcXFeuONNxQWFua9x8bhcGjgwIFyOBxavHixcnNz5XQ6FR4eruzsbCUnJ2vSpEmSpBkzZuiGG27QL3/5S61evVpNTU1asWKFsrKyuHoDAACsjZ3169dLklJSUny2b9iwQYsWLZIkrV27VkFBQUpPT1d7e7tmzpypZ555xnvsgAEDtG3bNt13331KTk7W4MGDtXDhQq1ataqvngYAAAhglr+MdT6hoaEqKipSUVFRt8cMHz5cb7/9tj9HAwAAhgioz9kBAADwN2IHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRLY6e8vFxz585VXFycbDabtm7d6rPfZrOddVmzZo33mBEjRpyxv7CwsI+fCQAACFSWxk5bW5vGjh2roqKis+5vbGz0WV566SXZbDalp6f7HLdq1Sqf47Kzs/tifAAA0A8EW/nN09LSlJaW1u3+mJgYn/U33nhD06ZN0zXXXOOzPSws7IxjAQAApH50z05zc7PeeustLV68+Ix9hYWFioyM1Pjx47VmzRp1dnae81zt7e1yu90+CwAAMJOlV3Yuxssvv6ywsDDNmzfPZ/v999+vCRMmyOl0ateuXcrLy1NjY6OeeOKJbs9VUFCg/Pz83h4ZAAAEgH4TOy+99JIyMjIUGhrqsz03N9f7dWJiokJCQnTPPfeooKBAdrv9rOfKy8vzeZzb7ZbL5eqdwQEAgKX6Rex88MEHqq6u1quvvnreY5OSktTZ2akDBw7o+uuvP+sxdru92xACAABm6Rf37Lz44ouaOHGixo4de95jq6qqFBQUpKioqD6YDAAABDpLr+wcPXpUNTU13vW6ujpVVVXJ6XRq2LBhkk69xPTaa6/pT3/60xmPr6ioUGVlpaZNm6awsDBVVFQoJydHd911l4YMGdJnzwMAAAQuS2Nn7969mjZtmnf99H00Cxcu1MaNGyVJmzdvlsfj0YIFC854vN1u1+bNm7Vy5Uq1t7crPj5eOTk5PvfjAACAy5ulsZOSkiKPx3POYzIzM5WZmXnWfRMmTNDu3bt7YzQAAGCIfnHPDgAAQE8ROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjWRo75eXlmjt3ruLi4mSz2bR161af/YsWLZLNZvNZZs2a5XPM4cOHlZGRofDwcEVERGjx4sU6evRoHz4LAAAQyCyNnba2No0dO1ZFRUXdHjNr1iw1NjZ6l7/97W8++zMyMvT555+rpKRE27ZtU3l5uTIzM3t7dAAA0E8EW/nN09LSlJaWds5j7Ha7YmJizrrvyy+/1Pbt27Vnzx7ddNNNkqSnn35as2fP1uOPP664uDi/zwwAAPoXS2PnQuzcuVNRUVEaMmSIbrnlFj366KOKjIyUJFVUVCgiIsIbOpKUmpqqoKAgVVZW6mc/+9lZz9ne3q729nbvutvt7t0n0Uc++ugjHThwwOoxgIBSVVXl81cAp4wYMUKTJ0+2eow+EdCxM2vWLM2bN0/x8fGqra3V73//e6WlpamiokIDBgxQU1OToqKifB4THBwsp9Oppqambs9bUFCg/Pz83h6/T3300UfK+32e5LF6EiAwVVZWqrKy0uoxgMBhkwr+UHBZBE9Ax878+fO9XyckJCgxMVEjR47Uzp07NX369B6fNy8vT7m5ud51t9stl8t1SbNa7cCBA5JH6vphlzzhFA8AoHs2t01BHwfpwIEDxE6gueaaa3TVVVeppqZG06dPV0xMjA4ePOhzTGdnpw4fPtztfT7SqfuA7HZ7b49rCU+4Rxpi9RQAgEDmucxeBuhXn7Pz7bff6rvvvlNsbKwkKTk5WS0tLdq3b5/3mPfee09dXV1KSkqyakwAABBALL2yc/ToUdXU1HjX6+rqVFVVJafTKafTqfz8fKWnpysmJka1tbV64IEH9IMf/EAzZ86UJI0ePVqzZs3SkiVL9Oyzz6qjo0NLly7V/PnzeScWAACQZPGVnb1792r8+PEaP368JCk3N1fjx4/Xww8/rAEDBmj//v269dZbdd1112nx4sWaOHGiPvjgA5+XoDZt2qRRo0Zp+vTpmj17tqZMmaK//OUvVj0lAAAQYCy9spOSkiKPp/vXDd95553znsPpdKq4uNifYwEAAIP0q3t2AAAALhaxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxmaeyUl5dr7ty5iouLk81m09atW737Ojo6tHz5ciUkJGjw4MGKi4vTr371KzU0NPicY8SIEbLZbD5LYWFhHz8TAAAQqCyNnba2No0dO1ZFRUVn7Dt27Jg++eQTPfTQQ/rkk0+0ZcsWVVdX69Zbbz3j2FWrVqmxsdG7ZGdn98X4AACgHwi28punpaUpLS3trPscDodKSkp8tv35z3/WD3/4Q9XX12vYsGHe7WFhYYqJienVWQEAQP/Ur+7ZaW1tlc1mU0REhM/2wsJCRUZGavz48VqzZo06OzvPeZ729na53W6fBQAAmMnSKzsX4/jx41q+fLkWLFig8PBw7/b7779fEyZMkNPp1K5du5SXl6fGxkY98cQT3Z6roKBA+fn5fTE2AACwWL+InY6ODv3iF7+Qx+PR+vXrffbl5uZ6v05MTFRISIjuueceFRQUyG63n/V8eXl5Po9zu91yuVy9MzwAALBUwMfO6dD5+uuv9d577/lc1TmbpKQkdXZ26sCBA7r++uvPeozdbu82hAAAgFkCOnZOh85XX32l999/X5GRked9TFVVlYKCghQVFdUHEwIAgEBnaewcPXpUNTU13vW6ujpVVVXJ6XQqNjZWd9xxhz755BNt27ZNJ0+eVFNTkyTJ6XQqJCREFRUVqqys1LRp0xQWFqaKigrl5OTorrvu0pAhQ6x6WgAAIIBYGjt79+7VtGnTvOun76NZuHChVq5cqX/84x+SpHHjxvk87v3331dKSorsdrs2b96slStXqr29XfHx8crJyfG5HwcAAFzeLI2dlJQUeTyebvefa58kTZgwQbt37/b3WAAAwCD96nN2AAAALhaxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaJcUOydOnFB1dbU6Ozv9NQ8AAIBf9Sh2jh07psWLF2vQoEG68cYbVV9fL0nKzs5WYWGhXwcEAAC4FD2Knby8PH366afauXOnQkNDvdtTU1P16quv+m04AACASxXckwdt3bpVr776qiZNmiSbzebdfuONN6q2ttZvwwEAAFyqHl3ZOXTokKKios7Y3tbW5hM/AAAAVutR7Nx000166623vOunA+eFF15QcnKyfyYDAADwgx69jPWHP/xBaWlp+uKLL9TZ2amnnnpKX3zxhXbt2qWysjJ/z4iLMKB+gHTQ6ikAAAHtuNUD9K0exc6UKVNUVVWlwsJCJSQk6N1339WECRNUUVGhhIQEf8+ICxQcHKzOf/MxAACA8wsO7lEC9Es9fqYjR47U888/789ZcIk6Ozul6ySFnvdQAMDl7Lguqz8c9yh23n77bQ0YMEAzZ8702f7OO++oq6tLaWlpfhkOF+/ksJPSEKunAAAEtP+TBvx7gNVT9Jke3aD84IMP6uTJk2ds93g8evDBBy95KAAAAH/pUex89dVXuuGGG87YPmrUKNXU1FzyUAAAAP7So9hxOBz6z3/+c8b2mpoaDR48+JKHAgAA8Jcexc5tt92mZcuW+Xxack1NjX7zm9/o1ltv9dtwAAAAl6pHsbN69WoNHjxYo0aNUnx8vOLj4zV69GhFRkbq8ccf9/eMAAAAPdajd2M5HA7t2rVLJSUl+vTTTzVw4EAlJiZq6tSp/p4PAADgkvT4c3ZsNptmzJihGTNm+HMeAAAAv7rg2Fm3bp0yMzMVGhqqdevWnfPY+++//4LOWV5erjVr1mjfvn1qbGzU66+/rttvv9273+Px6JFHHtHzzz+vlpYWTZ48WevXr9e1117rPebw4cPKzs7Wm2++qaCgIKWnp+upp57SlVdeeaFPDQAAGOyCY2ft2rXKyMhQaGio1q5d2+1xNpvtgmOnra1NY8eO1d1336158+adsX/16tVat26dXn75ZcXHx+uhhx7SzJkz9cUXXyg09NTHBGdkZKixsVElJSXq6OjQr3/9a2VmZqq4uPhCnxoAADDYBcdOXV3dWb++FGlpad1+2rLH49GTTz6pFStW6LbbbpMkvfLKK4qOjtbWrVs1f/58ffnll9q+fbv27Nmjm266SZL09NNPa/bs2Xr88ccVFxfnlzkBAED/ddHvxuro6NDIkSP15Zdf9sY8XnV1dWpqalJqaqp3m8PhUFJSkioqKiRJFRUVioiI8IaOJKWmpiooKEiVlZXdnru9vV1ut9tnAQAAZrro2Lniiit0/Hjv/274pqYmSVJ0dLTP9ujoaO++pqYmRUVF+ewPDg6W0+n0HnM2BQUFcjgc3sXlcvl5egAAECh69Dk7WVlZ+uMf/3jqt2z3Q3l5eWptbfUu33zzjdUjAQCAXtKjt57v2bNHO3bs0LvvvquEhIQzfkXEli1bLnmwmJgYSVJzc7NiY2O925ubmzVu3DjvMQcPHvR5XGdnpw4fPux9/NnY7XbZ7fZLnhEAAAS+HsVORESE0tPT/T2Lj/j4eMXExGjHjh3euHG73aqsrNR9990nSUpOTlZLS4v27duniRMnSpLee+89dXV1KSkpqVfnAwAA/cNFxU5XV5fWrFmjf//73zpx4oRuueUWrVy5UgMHDuzRNz969KjPb0mvq6tTVVWVnE6nhg0bpmXLlunRRx/Vtdde633reVxcnPezeEaPHq1Zs2ZpyZIlevbZZ9XR0aGlS5dq/vz5vBMLAABIusjYeeyxx7Ry5UqlpqZq4MCBWrdunQ4dOqSXXnqpR9987969mjZtmnc9NzdXkrRw4UJt3LhRDzzwgNra2pSZmamWlhZNmTJF27dv937GjiRt2rRJS5cu1fTp070fKni+Dz0EAACXj4uKnVdeeUXPPPOM7rnnHklSaWmp5syZoxdeeEFBQRd/r3NKSoo8Hk+3+202m1atWqVVq1Z1e4zT6eQDBAEAQLcuqlDq6+s1e/Zs73pqaqpsNpsaGhr8PhgAAIA/XFTsdHZ2+ryEJJ363J2Ojg6/DgUAAOAvF/Uylsfj0aJFi3zetn38+HHde++9Pm8/98dbzwEAAPzhomJn4cKFZ2y76667/DYMAACAv11U7GzYsKG35gAAAOgVPfp1EQAAAP0FsQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMFrAx86IESNks9nOWLKysiRJKSkpZ+y79957LZ4aAAAEimCrBzifPXv26OTJk971zz77TD/5yU/085//3LttyZIlWrVqlXd90KBBfTojAAAIXAEfO0OHDvVZLyws1MiRI/XjH//Yu23QoEGKiYnp69EAAEA/EPAvY33fiRMn9Ne//lV33323bDabd/umTZt01VVXacyYMcrLy9OxY8fOeZ729na53W6fBQAAmCngr+x839atW9XS0qJFixZ5t915550aPny44uLitH//fi1fvlzV1dXasmVLt+cpKChQfn5+H0wMAACs1q9i58UXX1RaWpri4uK82zIzM71fJyQkKDY2VtOnT1dtba1Gjhx51vPk5eUpNzfXu+52u+VyuXpvcAAAYJl+Eztff/21SktLz3nFRpKSkpIkSTU1Nd3Gjt1ul91u9/uMAAAg8PSbe3Y2bNigqKgozZkz55zHVVVVSZJiY2P7YCoAABDo+sWVna6uLm3YsEELFy5UcPD/H7m2tlbFxcWaPXu2IiMjtX//fuXk5Gjq1KlKTEy0cGIAABAo+kXslJaWqr6+XnfffbfP9pCQEJWWlurJJ59UW1ubXC6X0tPTtWLFCosmBQAAgaZfxM6MGTPk8XjO2O5yuVRWVmbBRAAAoL/oN/fsAAAA9ASxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjBVs9APzL5rbJI4/VYwAAApjNbbN6hD5F7BhixIgRkk0K+piLdQCAC2D778+Oy4DN4/Fc9pcB3G63HA6HWltbFR4ebvU4PfbRRx/pwIEDVo8BBJSqqipVVlYqKSlJ48aNs3ocIGCMGDFCkydPtnqMS3KhP7+5smOQyZMn9/t/cYHeUFlZqXHjxikjI8PqUQBYgNc8AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYLSAjp2VK1fKZrP5LKNGjfLuP378uLKyshQZGakrr7xS6enpam5utnBiAAAQaAI6diTpxhtvVGNjo3f58MMPvftycnL05ptv6rXXXlNZWZkaGho0b948C6cFAACBJuB/EWhwcLBiYmLO2N7a2qoXX3xRxcXFuuWWWyRJGzZs0OjRo7V7925NmjSpr0cFAAABKOCv7Hz11VeKi4vTNddco4yMDNXX10uS9u3bp46ODqWmpnqPHTVqlIYNG6aKiopznrO9vV1ut9tnAQAAZgro2ElKStLGjRu1fft2rV+/XnV1dfrRj36kI0eOqKmpSSEhIYqIiPB5THR0tJqams553oKCAjkcDu/icrl68VkAAAArBfTLWGlpad6vExMTlZSUpOHDh+vvf/+7Bg4c2OPz5uXlKTc317vudrsJHgAADBXQV3b+V0REhK677jrV1NQoJiZGJ06cUEtLi88xzc3NZ73H5/vsdrvCw8N9FgAAYKZ+FTtHjx5VbW2tYmNjNXHiRF1xxRXasWOHd391dbXq6+uVnJxs4ZQAACCQBPTLWL/97W81d+5cDR8+XA0NDXrkkUc0YMAALViwQA6HQ4sXL1Zubq6cTqfCw8OVnZ2t5ORk3okFAAC8Ajp2vv32Wy1YsEDfffedhg4dqilTpmj37t0aOnSoJGnt2rUKCgpSenq62tvbNXPmTD3zzDMWTw0AAAJJQMfO5s2bz7k/NDRURUVFKioq6qOJAABAf9Ov7tkBAAC4WMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMFpAx05BQYFuvvlmhYWFKSoqSrfffruqq6t9jklJSZHNZvNZ7r33XosmBgAAgSagY6esrExZWVnavXu3SkpK1NHRoRkzZqitrc3nuCVLlqixsdG7rF692qKJAQBAoAm2eoBz2b59u8/6xo0bFRUVpX379mnq1Kne7YMGDVJMTExfjwcAAPqBgL6y879aW1slSU6n02f7pk2bdNVVV2nMmDHKy8vTsWPHznme9vZ2ud1unwUAAJgpoK/sfF9XV5eWLVumyZMna8yYMd7td955p4YPH664uDjt379fy5cvV3V1tbZs2dLtuQoKCpSfn98XYwMAAIv1m9jJysrSZ599pg8//NBne2ZmpvfrhIQExcbGavr06aqtrdXIkSPPeq68vDzl5uZ6191ut1wuV+8MDgAALNUvYmfp0qXatm2bysvLdfXVV5/z2KSkJElSTU1Nt7Fjt9tlt9v9PicAAAg8AR07Ho9H2dnZev3117Vz507Fx8ef9zFVVVWSpNjY2F6eDgAA9AcBHTtZWVkqLi7WG2+8obCwMDU1NUmSHA6HBg4cqNraWhUXF2v27NmKjIzU/v37lZOTo6lTpyoxMdHi6QEAQCAI6NhZv369pFMfHPh9GzZs0KJFixQSEqLS0lI9+eSTamtrk8vlUnp6ulasWGHBtAAAIBAFdOx4PJ5z7ne5XCorK+ujaQAAQH/Urz5nBwAA4GIROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMFWDwCYprm5WaWlpVaPgf+qqqry+Susl5qaqujoaKvHwGWE2AH8rLS0VM8995zVY+B/VFZWqrKy0uox8F8ZGRlWj4DLCLED+FlqaqrVIwABjf9G0NeIHcDPoqOj+VMrAAQQblAGAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABG47eeS/J4PJIkt9tt8SQAAOBCnf65ffrneHeIHUlHjhyRJLlcLosnAQAAF+vIkSNyOBzd7rd5zpdDl4Guri41NDQoLCxMNpvN6nEA+JHb7ZbL5dI333yj8PBwq8cB4Ecej0dHjhxRXFycgoK6vzOH2AFgNLfbLYfDodbWVmIHuExxgzIAADAasQMAAIxG7AAwmt1u1yOPPCK73W71KAAswj07AADAaFzZAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABjt/wGRCgJDcCfFmwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "q1 = df['Price'].quantile(0.25)\n",
        "q3 = df['Price'].quantile(0.75)\n",
        "iqr = q3-q1\n",
        "q1, q3, iqr\n",
        "upper_limit = q3 + (1.5 * iqr)\n",
        "lower_limit = q1 - (1.5 * iqr)\n",
        "lower_limit, upper_limit\n",
        "sns.boxplot(df['Price'])\n",
        "df.loc[(df['Price'] > upper_limit) | (df['Price'] < lower_limit)]\n",
        "new_df = df.loc[(df['Price'] <= upper_limit) & (df['Price'] >= lower_limit)]\n",
        "print('before removing outliers:', len(df))\n",
        "print('after removing outliers:',len(new_df))\n",
        "print('outliers:', len(df)-len(new_df))\n",
        "sns.boxplot(new_df['Price'])\n",
        "new_df = df.copy()\n",
        "new_df.loc[(new_df['Price']>upper_limit), 'Price'] = upper_limit\n",
        "new_df.loc[(new_df['Price']<lower_limit), 'Price'] = lower_limit\n",
        "\n",
        "sns.boxplot(new_df['Price'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSSL06giVkYu"
      },
      "source": [
        "Percentile"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "sWFkfDNLVd3T",
        "outputId": "df778e41-cf8d-46c7-cd97-45274ee208c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "upper limit: 197.01\n",
            "lower limit: 11.99\n",
            "before removing outliers: 1000\n",
            "after removing outliers: 980\n",
            "outliers: 20\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Price', ylabel='Price'>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "upper_limit = df['Price'].quantile(0.99)\n",
        "lower_limit = df['Price'].quantile(0.01)\n",
        "print('upper limit:', upper_limit)\n",
        "print('lower limit:', lower_limit)\n",
        "sns.boxplot(df['Price'])\n",
        "df.loc[(df['Price'] > upper_limit) | (df['Price'] < lower_limit)]\n",
        "new_df = df.loc[(df['Price'] <= upper_limit) & (df['Price'] >= lower_limit)]\n",
        "print('before removing outliers:', len(df))\n",
        "print('after removing outliers:',len(new_df))\n",
        "print('outliers:', len(df)-len(new_df))\n",
        "sns.boxplot(new_df['Price'])\n",
        "\n",
        "new_df = df.copy()\n",
        "new_df.loc[(new_df['Price']>upper_limit), 'Price'] = upper_limit\n",
        "new_df.loc[(new_df['Price']<lower_limit), 'Price'] = lower_limit\n",
        "sns.boxplot(new_df['Price'])\n",
        "sns.distplot(df['Price'])\n",
        "sns.distplot(new_df['Price'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "65Ql8sn54Vpy"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr_model = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "qCrrNObj4k4q",
        "outputId": "317a1e44-d664-4f92-cdff-be1e3d0c47d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Price  Category_Dress  Category_Jacket  Category_Jeans  \\\n",
              "0    0.910053             1.0              0.0             0.0   \n",
              "1    0.248677             0.0              0.0             1.0   \n",
              "2    0.619048             1.0              0.0             0.0   \n",
              "3    0.354497             0.0              0.0             0.0   \n",
              "4    0.544974             0.0              0.0             0.0   \n",
              "..        ...             ...              ...             ...   \n",
              "995  0.878307             0.0              0.0             1.0   \n",
              "996  0.529101             0.0              1.0             0.0   \n",
              "997  0.619048             0.0              0.0             0.0   \n",
              "998  0.312169             0.0              0.0             0.0   \n",
              "999  0.867725             0.0              1.0             0.0   \n",
              "\n",
              "     Category_Shoes  Category_Sweater  Category_T-shirt  Color_Black  \\\n",
              "0               0.0               0.0               0.0          0.0   \n",
              "1               0.0               0.0               0.0          1.0   \n",
              "2               0.0               0.0               0.0          0.0   \n",
              "3               1.0               0.0               0.0          0.0   \n",
              "4               0.0               1.0               0.0          0.0   \n",
              "..              ...               ...               ...          ...   \n",
              "995             0.0               0.0               0.0          1.0   \n",
              "996             0.0               0.0               0.0          0.0   \n",
              "997             0.0               1.0               0.0          0.0   \n",
              "998             0.0               1.0               0.0          1.0   \n",
              "999             0.0               0.0               0.0          0.0   \n",
              "\n",
              "     Color_Blue  Color_Green  ...  Material_Nylon  Material_Polyester  \\\n",
              "0           0.0          0.0  ...             1.0                 0.0   \n",
              "1           0.0          0.0  ...             0.0                 0.0   \n",
              "2           0.0          0.0  ...             0.0                 0.0   \n",
              "3           0.0          1.0  ...             0.0                 0.0   \n",
              "4           0.0          0.0  ...             1.0                 0.0   \n",
              "..          ...          ...  ...             ...                 ...   \n",
              "995         0.0          0.0  ...             0.0                 1.0   \n",
              "996         0.0          0.0  ...             0.0                 0.0   \n",
              "997         1.0          0.0  ...             0.0                 0.0   \n",
              "998         0.0          0.0  ...             0.0                 0.0   \n",
              "999         0.0          0.0  ...             0.0                 0.0   \n",
              "\n",
              "     Material_Silk  Material_Wool  Brand_Adidas  Brand_New Balance  \\\n",
              "0              0.0            0.0           0.0                1.0   \n",
              "1              1.0            0.0           0.0                1.0   \n",
              "2              0.0            1.0           0.0                0.0   \n",
              "3              0.0            0.0           0.0                0.0   \n",
              "4              0.0            0.0           1.0                0.0   \n",
              "..             ...            ...           ...                ...   \n",
              "995            0.0            0.0           0.0                0.0   \n",
              "996            1.0            0.0           0.0                0.0   \n",
              "997            0.0            0.0           0.0                0.0   \n",
              "998            0.0            0.0           0.0                0.0   \n",
              "999            0.0            1.0           0.0                1.0   \n",
              "\n",
              "     Brand_Nike  Brand_Puma  Brand_Reebok  Brand_Under Armour  \n",
              "0           0.0         0.0           0.0                 0.0  \n",
              "1           0.0         0.0           0.0                 0.0  \n",
              "2           0.0         0.0           0.0                 1.0  \n",
              "3           1.0         0.0           0.0                 0.0  \n",
              "4           0.0         0.0           0.0                 0.0  \n",
              "..          ...         ...           ...                 ...  \n",
              "995         0.0         1.0           0.0                 0.0  \n",
              "996         0.0         1.0           0.0                 0.0  \n",
              "997         0.0         0.0           1.0                 0.0  \n",
              "998         0.0         0.0           0.0                 1.0  \n",
              "999         0.0         0.0           0.0                 0.0  \n",
              "\n",
              "[1000 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb6ae708-3abf-4034-92a1-056cb59c3925\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Category_Dress</th>\n",
              "      <th>Category_Jacket</th>\n",
              "      <th>Category_Jeans</th>\n",
              "      <th>Category_Shoes</th>\n",
              "      <th>Category_Sweater</th>\n",
              "      <th>Category_T-shirt</th>\n",
              "      <th>Color_Black</th>\n",
              "      <th>Color_Blue</th>\n",
              "      <th>Color_Green</th>\n",
              "      <th>...</th>\n",
              "      <th>Material_Nylon</th>\n",
              "      <th>Material_Polyester</th>\n",
              "      <th>Material_Silk</th>\n",
              "      <th>Material_Wool</th>\n",
              "      <th>Brand_Adidas</th>\n",
              "      <th>Brand_New Balance</th>\n",
              "      <th>Brand_Nike</th>\n",
              "      <th>Brand_Puma</th>\n",
              "      <th>Brand_Reebok</th>\n",
              "      <th>Brand_Under Armour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.910053</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.248677</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.619048</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.354497</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.544974</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "      <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>0.878307</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>0.529101</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>0.619048</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>0.312169</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>0.867725</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-bb6ae708-3abf-4034-92a1-056cb59c3925 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bb6ae708-3abf-4034-92a1-056cb59c3925');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-09e5e493-f717-4a3f-9099-24976e0006b6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-09e5e493-f717-4a3f-9099-24976e0006b6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "standardized_df"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "standardized_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ij9dzUYWZM0a"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dT8_pLtsZTAf"
      },
      "outputs": [],
      "source": [
        "X = new_df.drop(['Price'],axis=1)\n",
        "Y = new_df['Price']\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, random_state=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Decision Tree**"
      ],
      "metadata": {
        "id": "00rmbLcKw6Js"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QndTsr87wSpL",
        "outputId": "d2dff971-bd14-48ad-f2a1-d6c97f33700d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 5764.220104\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "# Assuming X_train, X_test, Y_train, Y_test are already defined\n",
        "\n",
        "# Initialize the DecisionTreeRegressor\n",
        "model = DecisionTreeRegressor(random_state=42)\n",
        "\n",
        "# Train the model on the training data\n",
        "model.fit(X_train, Y_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "Y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model using mean squared error\n",
        "mse = mean_squared_error(Y_test, Y_pred)\n",
        "print(\"Mean Squared Error:\", mse)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2J7aXIMyEVr",
        "outputId": "bfe8abe1-47b7-4951-ce36-e3a1ad0bd82a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 5764.220104\n",
            "Root Mean Squared Error: 75.92246112976053\n",
            "R-squared (R2) Score: -1.0183467344461365\n"
          ]
        }
      ],
      "source": [
        "#finding the root square value\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "# Calculate Mean Squared Error (MSE)\n",
        "mse = mean_squared_error(Y_test, Y_pred)\n",
        "print(\"Mean Squared Error:\", mse)\n",
        "\n",
        "# Calculate Root Mean Squared Error (RMSE)\n",
        "rmse = np.sqrt(mse)\n",
        "print(\"Root Mean Squared Error:\", rmse)\n",
        "\n",
        "# Calculate R-squared (R2) score\n",
        "r2 = r2_score(Y_test, Y_pred)\n",
        "print(\"R-squared (R2) Score:\", r2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_20mtXDg0sCg",
        "outputId": "7bc02d37-6c30-4763-f730-cbc6c85f645c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters: {'max_depth': 5, 'max_features': 'log2', 'min_samples_leaf': 2, 'min_samples_split': 2}\n",
            "Mean Squared Error (Tuned): 3182.609512894749\n",
            "R-squared (R2) Score (Tuned): -0.11439351750477944\n"
          ]
        }
      ],
      "source": [
        "# tunning the model using best hyperparameters(grid search)\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Initialize the DecisionTreeRegressor\n",
        "model = DecisionTreeRegressor(random_state=42)\n",
        "\n",
        "# Define the hyperparameters to tune\n",
        "param_grid = {\n",
        "    'max_depth': [None, 5, 10, 15],  # Maximum depth of the tree\n",
        "    'min_samples_split': [2, 5, 10],  # Minimum samples required to split a node\n",
        "    'min_samples_leaf': [1, 2, 4],  # Minimum samples required at a leaf node\n",
        "    'max_features': ['auto', 'sqrt', 'log2']  # Maximum number of features to consider for each split\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='r2')\n",
        "\n",
        "# Perform grid search to find the best hyperparameters\n",
        "grid_search.fit(X_train, Y_train)\n",
        "\n",
        "# Get the best hyperparameters\n",
        "best_params = grid_search.best_params_\n",
        "print(\"Best Hyperparameters:\", best_params)\n",
        "\n",
        "# Use the best model for prediction\n",
        "best_model = grid_search.best_estimator_\n",
        "Y_pred_tuned = best_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model using mean squared error\n",
        "mse_tuned = mean_squared_error(Y_test, Y_pred_tuned)\n",
        "print(\"Mean Squared Error (Tuned):\", mse_tuned)\n",
        "\n",
        "# Calculate R-squared (R2) score for the tuned model\n",
        "r2_tuned = r2_score(Y_test, Y_pred_tuned)\n",
        "print(\"R-squared (R2) Score (Tuned):\", r2_tuned)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7K3K77B1i8e",
        "outputId": "f76bd954-679e-4ea7-de80-08cab8f45011"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters: {'ccp_alpha': 1.0, 'max_depth': 5, 'max_features': 'log2', 'min_samples_leaf': 16, 'min_samples_split': 2}\n",
            "Root Mean Squared Error (RMSE) for Best Model: 54.87264901138868\n",
            "R-squared (R2) Score for Best Model: -0.054306960253681646\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "# Initialize the DecisionTreeRegressor\n",
        "model = DecisionTreeRegressor(random_state=42)\n",
        "\n",
        "# Define the expanded hyperparameters to tune\n",
        "param_grid = {\n",
        "    'max_depth': [None, 5, 10, 15, 20],  # Maximum depth of the tree\n",
        "    'min_samples_split': [2, 5, 10, 15, 20],  # Minimum samples required to split a node\n",
        "    'min_samples_leaf': [1, 2, 4, 8, 16],  # Minimum samples required at a leaf node\n",
        "    'max_features': ['auto', 'sqrt', 'log2', None],  # Maximum number of features to consider for each split\n",
        "    'ccp_alpha': [0.0, 0.01, 0.1, 1.0]  # Complexity parameter used for Minimal Cost-Complexity Pruning\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='neg_mean_squared_error')\n",
        "\n",
        "# Perform grid search to find the best hyperparameters\n",
        "grid_search.fit(X_train, Y_train)\n",
        "\n",
        "# Get the best hyperparameters\n",
        "best_params = grid_search.best_params_\n",
        "print(\"Best Hyperparameters:\", best_params)\n",
        "\n",
        "# Use the best model for prediction\n",
        "best_model = grid_search.best_estimator_\n",
        "Y_pred = best_model.predict(X_test)\n",
        "\n",
        "# Calculate RMSE for the best model\n",
        "rmse = np.sqrt(mean_squared_error(Y_test, Y_pred))\n",
        "print(\"Root Mean Squared Error (RMSE) for Best Model:\", rmse)\n",
        "\n",
        "# Calculate R2 score for the best model\n",
        "r2 = r2_score(Y_test, Y_pred)\n",
        "print(\"R-squared (R2) Score for Best Model:\", r2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "OtajYlsZ4vs0",
        "outputId": "f6451350-6876-4af7-8e6c-b11490942f35"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'X_train_poly' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-6dfd6e0202eb>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Fit the model on the polynomial features of the training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_poly\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Make predictions on the polynomial features of the testing data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'X_train_poly' is not defined"
          ]
        }
      ],
      "source": [
        "#tunning through feature engineering\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "# Initialize a linear regression model\n",
        "model = LinearRegression()\n",
        "\n",
        "# Fit the model on the polynomial features of the training data\n",
        "model.fit(X_train_poly, Y_train)\n",
        "\n",
        "# Make predictions on the polynomial features of the testing data\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Calculate RMSE\n",
        "rmse = np.sqrt(mean_squared_error(Y_test, Y_pred))\n",
        "print(\"Root Mean Squared Error (RMSE) for Polynomial Regression Model:\", rmse)\n",
        "\n",
        "# Calculate R-squared (R2) score\n",
        "r2 = r2_score(Y_test, Y_pred)\n",
        "print(\"R-squared (R2) Score for Polynomial Regression Model:\", r2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ja57HLa5zYm"
      },
      "outputs": [],
      "source": [
        "# feature importance\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming X_train and Y_train are your training features and target variable\n",
        "# Assuming X_test and Y_test are your testing features and target variable\n",
        "\n",
        "# Initialize the DecisionTreeRegressor\n",
        "model = DecisionTreeRegressor(random_state=42)\n",
        "\n",
        "# Fit the model\n",
        "model.fit(X_train, Y_train)\n",
        "\n",
        "# Get feature importances\n",
        "feature_importances = model.feature_importances_\n",
        "\n",
        "# Sort feature importances in descending order\n",
        "indices = np.argsort(feature_importances)[::-1]\n",
        "\n",
        "# Print feature ranking\n",
        "print(\"Feature ranking:\")\n",
        "for f in range(X_train.shape[1]):\n",
        "    print(\"%d. feature %d (%f)\" % (f + 1, indices[f], feature_importances[indices[f]]))\n",
        "\n",
        "# Plot feature importances\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.title(\"Feature Importances\")\n",
        "plt.bar(range(X_train.shape[1]), feature_importances[indices], color=\"b\", align=\"center\")\n",
        "plt.xticks(range(X_train.shape[1]), indices)\n",
        "plt.xlim([-1, X_train.shape[1]])\n",
        "plt.xlabel(\"Feature Index\")\n",
        "plt.ylabel(\"Feature Importance\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3o5BUN4OaA2l"
      },
      "source": [
        "**Linear Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "kUhhDhOWZhEg",
        "outputId": "c4092dbe-9a82-4d9f-9925-f3cff9777281"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared Error :  0.013936370715154411\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "lin_reg_model = LinearRegression()\n",
        "lin_reg_model.fit(X_train,Y_train)\n",
        "training_data_prediction = lin_reg_model.predict(X_train)\n",
        "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
        "print(\"R squared Error : \", error_score)\n",
        "\n",
        "plt.scatter(Y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices vs Predicted Prices\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "7JuZPgT9ZyRE",
        "outputId": "536bcaef-73e9-494d-dc1a-345aa3d9d2f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared Error :  -0.03540224129664016\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_data_prediction = lin_reg_model.predict(X_test)\n",
        "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print(\"R squared Error : \", error_score)\n",
        "plt.scatter(Y_test, test_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices vs Predicted Prices\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxyPI0Vmac4A"
      },
      "source": [
        "**Lasso Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YGFnAhD-ae0p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "8d92f5de-4096-4a00-81ad-490524f4c3be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared Error :  0.0060694545676101\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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xPz/fYt1EURS1Wq2+vbds2VJv/dKlS8Xu3buLQUFBore3t9imTRtx2bJlYnl5udlydR89//DDD81upzuG165dM7p+27ZtYq9evURfX1/R19dXbNOmjThz5kzxzJkzBtu98cYbolqtFhUKhdi1a1dx//799Y5xY47Fs88+KzZr1kx0c3Or15dsGaMpcu+H1LQJoljn2iYRERGRjHDODhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlnjQwVR8/0qV65cgb+/v82/4ZqIiIjsQxRFFBQUIDo6Gm5upq/fMNlBzdN2Y2JinB0GERERNcClS5fQvHlzk+uZ7AD6R4VfunQJAQEBTo6GiIiIpNBqtYiJibH4lR9MdvDXNwYHBAQw2SEiImpiLE1B4QRlIiIikjUmO0RERCRrTHaIiIhI1pjsEBERkawx2SEiIiJZc2qys3//fowcORLR0dEQBAHbt283WP/xxx9j8ODBCA0NhSAISE9Pr1fG2rVr0a9fPwQEBEAQBOTl5TkkdiIiImoanJrsFBUVoWPHjnj99ddNru/Vqxeef/55k2UUFxdj6NCh+Pe//22vMImIiKgJc+pzdoYNG4Zhw4aZXD9x4kQAQEZGhsltZs+eDQD49ttvbRgZERERycUt+VDBsrIylJWV6f/WarVOjIaIiIjs6ZZMdlJTU7FkyRJnh0EuqKpaxCFNLq4WlCLCX4nu6hC4u/HLYQG2jb3dKu17q9TTWdi+xt2Syc78+fMxZ84c/d+679agW9uuk1lYsvMUsvJL9cuiApVYNDIRQ9tFOTEy52Pb2Net0r63Sj2dhe1r2i350XOFQqH/Hix+HxYBNYPEw1uOGgwSAJCdX4qHtxzFrpNZTorM+dg29nWrtO+tUk9nYfuad0te2blVufLlzcbE1th6VVWLWLLzFEQj60QAAoAlO09hUKLKpu3lysdDpyFt44x66faZnV+C3KJyhPgpoApwjTbVx6YtRW5hGUJ8vaAK9EZ3dQgAOLzvlVdW4520DFzMLUZciA8mJsfDy8O2//fW7QNd4oKdco7Zkr3GKFucL84aw6TG5grjnFOTncLCQpw/f17/t0ajQXp6OkJCQhAbG4vc3FxkZmbiypUrAIAzZ84AAFQqFVQqFQAgOzsb2dnZ+nJOnDgBf39/xMbGIiQkxME1cl2ufHmzMbHZol6HNLn1/huqTQSQlV+KQ5pcJCeESirTElc+HrVZ2zbOqJexfTpq35ZYim1ctxiH9r3UL05h3QENqmu9Ky774jdM663G/OGJjS4fMF7nEF8v5BaVm3yNPc4xW7LXGAXAJueLM8YwKVxpnHPqbazDhw8jKSkJSUlJAIA5c+YgKSkJCxcuBADs2LEDSUlJGDFiBABg3LhxSEpKwpo1a/RlrFmzBklJSZg2bRoAoE+fPkhKSsKOHTscXBvX5cqXNxsTm63qdbXA9CDRkO0sceXjUZc1beOMepnap06WE9tUSmwvfXNOUlm26HupX5zCm/sNEx0AqBaBN/drkPrFqUbvw1SdzSU6tdnqHLMle41RD205iodsdL44egyTwtXGOacmO/369YMoivV+Nm3aBACYMmWK0fWLFy/Wl7F48WKj20yZMsUpdXI1li5vAjX/WVTVHQEdoDGx2bJeEf5KSfFK3c4cVz4exkitc5ifwuH1MteWdTm6Ta2JTYrG9r3yymqsO6Axu826AxqUV1Y3eB+2qLMtzjFbsvcYZUxDzhdHjmFSuOI4d0tOUL6VWHN509EaE5st69VdHYKoQCVM3UUWUHPpVTfHojFc+XgYI7VtIMLh9bLUlvbctyVSY7PEVn3vnbSMeld06qoWa7ZrqMbU2ZbnmC3Zc4wyx9o+68gxTApXHOeY7MicK17etHafxrazZb3c3QT9/fO6g4Xu70UjE20yqc6Vj4cxUtvmelEZpLBlvawty5Ft2pB92bPvXcwttul2xjS0fW19jtmSI8YoW+zfkWOYFK44zjHZkTlXu7zZkH0a287W9RraLgqrJ3SGKtBwe1WgEqsndLbZZDpXPh6mSGkbZ9TL2rIc2abW7uvxlNvs2vfiQnxsup0xUusc4utp8LetzzFbcsQYZYv9A44bw6RwxXGOHz2XOd3lzez8UqP3TwXUnAzOuHzcmNjsUa+h7aIwKFFl149JuvLxMMdS2zijXrp9WrpV4Iw2tdQetUUFKvHIgJZ4ZEBLu/W9icnxWPbFb2ZvZbkJNds1lNQ+8N0T/XHk4k2nfxRZCnuOUeY0tM86YgyTwhXHOV7ZkTlXu7xZW2Nis1e93N0EJCeE4q5OzZCcEGrzdnHl42GJubZxRr10+5RSoqPbtHZ7mCPgr9js2fe8PNwwrbfa7DbTeqsb9bwdqX3Ay8PNrueYLdl7jGpIuVJidnb7uuI4x2TnFuBKlzfrakxsrlwvc5pq3JY4o166fUYFGr8cHuXENnW12OYPT8SMPmrUfX9xE4AZfWzznB059m17jVFrJnTGGpm1VW2u1hcEURRd4zOuTqTVahEYGIj8/HxZf3WEqzzJ0hhnPkHZWZpq3JbwCcomYjPyBGVnxOaMJyi7wnFoLFd+grIrs3f9pL5/M9nBrZPsEBERyYnU92/exiIiIiJZY7JDREREssZkh4iIiGSNyQ4RERHJGpMdIiIikjUmO0RERCRrTHaIiIhI1pjsEBERkawx2SEiIiJZY7JDREREssZkh4iIiGSNyQ4RERHJGpMdIiIikjUmO0RERCRrTHaIiIhI1pjsEBERkawx2SEiIiJZY7JDREREssZkh4iIiGSNyQ4RERHJGpMdIiIikjUmO0RERCRrTHaIiIhI1pjsEBERkawx2SEiIiJZY7JDREREssZkh4iIiGSNyQ4RERHJGpMdIiIikjUmO0RERCRrTk129u/fj5EjRyI6OhqCIGD79u0G6z/++GMMHjwYoaGhEAQB6enp9cooLS3FzJkzERoaCj8/P4wZMwY5OTmOqQARERG5PKcmO0VFRejYsSNef/11k+t79eqF559/3mQZjz/+OHbu3IkPP/wQ3333Ha5cuYLRo0fbK2QiIiJqYjycufNhw4Zh2LBhJtdPnDgRAJCRkWF0fX5+PtavX4/33nsPAwYMAABs3LgRbdu2xU8//YQ77rjD5jETERFR09Kk5+wcOXIEFRUVSElJ0S9r06YNYmNjkZaWZvJ1ZWVl0Gq1Bj9EREQkT0062cnOzoaXlxeCgoIMlkdGRiI7O9vk61JTUxEYGKj/iYmJsXOkRERE5CxNOtlpqPnz5yM/P1//c+nSJWeHRERERHbi1Dk7jaVSqVBeXo68vDyDqzs5OTlQqVQmX6dQKKBQKBwQIRERETlbk76y06VLF3h6emLPnj36ZWfOnEFmZiaSk5OdGBkRERG5Cqde2SksLMT58+f1f2s0GqSnpyMkJASxsbHIzc1FZmYmrly5AqAmkQFqruioVCoEBgZi6tSpmDNnDkJCQhAQEIBHH30UycnJ/CQWERERAQAEURRFZ+3822+/Rf/+/estnzx5MjZt2oRNmzbh/vvvr7d+0aJFWLx4MYCahwrOnTsXW7duRVlZGYYMGYI33njD7G2surRaLQIDA5Gfn4+AgIAG14eIiIgcR+r7t1OTHVfBZIeIiKjpkfr+3aTn7BARERFZwmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREcmah7MDIHmrqhZxSJOLqwWliPBXors6BO5uQqO3t7ZcV9QU6mCvGO3VLxqzfwAufzxckav0EXPKK6vxTloGLuYWIy7EBxOT4+HlYdv/9c3Fa4u62Lt8a/fpyDJsgckO2c2uk1lYsvMUsvJL9cuiApVYNDIRQ9tFNXh7a8t1RU2hDvaK0V79ojH7D/LxBADkFVfYZB+3ClfpI+akfnEK6w5oUC3+tWzZF79hWm815g9PbHCMUuMF0Oi62Lt8a/dpi7gdfV4JoiiKljeTN61Wi8DAQOTn5yMgIMDZ4cjCrpNZeHjLUdTtXLp8fvWEzvUSGCnbW1uuK2oKdbBXjPbqF43dvzGudDxckav0EXNSvziFN/drTK6f0afxCY+5eE31M2vqYu/yrd2n1HIdNc5Jff926pyd/fv3Y+TIkYiOjoYgCNi+fbvBelEUsXDhQkRFRcHb2xspKSk4d+6cwTZHjx7FoEGDEBQUhNDQUEyfPh2FhYUOrAXVVVUtYsnOU0ZPRN2yJTtPoerPf7Wkbl9eWW1Vua7I2rZxBnvFaK9+ITUOc+UZ4yrHwxW5Sh8xp7yyGusOmE50AGDdAQ3KK6utirE2KfEaI7Uu9i6/ofu0RdyOPq+cehurqKgIHTt2xAMPPIDRo0fXW79ixQqsWrUKmzdvhlqtxoIFCzBkyBCcOnUKSqUSV65cQUpKCsaOHYvXXnsNWq0Ws2fPxpQpU/DRRx85oUbm1b53GearAATgakEZcgvLEOLrBVWgt8W5A7XLCPH2wumcAly6Wf8+dGPuk1p7b7huvNXVosFly7pEAFn5pfjpwg30bBWGQ5pcSdu/k5YhabuHtxxBD3UIJibHw91NaPDcEN0xys4vRfqlmwCA+FBfi+Waaz+pdf3x/HWczSkwmGNQe5/m+o+1c5wM6uynwKkr+ZJiPKTJRXJCqMnt6u77h/PXJZfbXR2CTT9opPWj32/gjhahJvtltrYUuYVlyC0qN1ue2X1cuAE3N0HfRhCB60VliPBXoktcMI5cvGnynJB6rHTzSjJuFAMQ0a5ZIE5ezgcAxIb4oo3KH9f/rEeInwIRfjXlZuWV4Ktfs1FcUQV1qC8GtYnE1cJSfH0qByUV1VCH+eDfwxPh5eFm0B6Wxhxzy344f03yOa5rN3P9Lkjpid2/5SD9Up6kcl/8+gxCfL3M9vt30jJg6b20WgSe/OgXjO0Wi+7qEFRVi/Xm9ujKMnYuWurT5khpI0vjhZTyX9p9Bj3iQ02+V9QldYy689UDSIoNxqC2kcgrrTCIXWoZUscQW3CZ21iCIOCTTz7BqFGjANRc1YmOjsbcuXMxb948AEB+fj4iIyOxadMmjBs3DmvXrsWCBQuQlZUFN7eaA3fixAl06NAB586dQ8uWLSXt2xG3sYzduzTG3NwBoP692drcBGBabzWSYoMbfJ/U2nvDxuIN8vZEXslff5usq7cnlo9pj7LKajz2v3SL209KjsPbaRctbqcjCIC3pzuKy6v0y6ydG2JtuYD5++fP7PwVG37IsBw7DP9zM7ZPY6yd42QsXqleGdcJd3VqZnYbqW1a2wM94/HlyWzJr/HxcoeXh5thHzTSLxvDx8t027sJMHhTlbrv2sfK2LwSW1N4uKHMyFUMo+ewxGWW1G0bW/Q7Y4z1+4WfnrRqvPDxckdJRRXqviPWOxf/3LbIwrkoVd3xsnZdnt35K9ZLGC+spXuvMHYLT+oYZYwu9kOaXEllSBlDLJH6/u2yyc7vv/+OhIQEHDt2DJ06ddJv17dvX3Tq1AmvvPIKXn31VaxYsQKXLl3Srz9//jxatWqFjRs3YsqUKUb3VVZWhrKyMv3fWq0WMTExdkt2rJknYIy5e7PWlAGYv0/akHvDjSUAmJ3SCi99c87itgtGtMWzn//W6P0B0ueGWFOupfvn0/uozc4fsBUB0uY4NfaYbp12h9n/yhrbprcCAUBKYgR2n7rq7FAcwt5jSe3zev2B3xs9XjiDI8eLunOWdp3MwkNbjja4PGuPr6UxRIomMWfHnOzsbABAZGSkwfLIyEj9ugEDBiA7OxsrV65EeXk5bt68iX/9618AgKysLJNlp6amIjAwUP8TExNjp1pYP0/AGFsMDpbukzb03rAtbD2UCVWAEoKJ9QJq/mO4t0ccGvuJRWvnhlhbrql1ImBx/oCtiJA+x6mhogL/uq1hTEPb9Fb7pLcI3DKJDmDfsUTX73Xn9cTk+CbZn3Rt5IjxovacJd052xjWHF83AegSF9yo/VnDZZMdKW6//XZs3rwZ//3vf+Hj4wOVSgW1Wo3IyEj9bS1j5s+fj/z8fP1P7StDttaYe662Vvs+aV3OilMEkK0tw/jusQBQL+HR/b1oZCLSL+XZ5DJ/3XZwVN0dOcdVyhynxlg0MtHs3KeGtinnAVNj1D6vvTzcMK232skRNYwI686FhuZ01WLNfCTA8e8B1SJw5OJNh+3PZZMdlUoFAMjJyTFYnpOTo18HAPfeey+ys7Nx+fJl3LhxA4sXL8a1a9fQokULk2UrFAoEBAQY/NjL1QLXSHRqMxaTveL08XSXtF18mA9WT+gMVaDSYLkqUKm/NG3rGHXlueIxsoWLucV2KXdqz3iLc78a0qb9bgtraEhEerX73vzhiZjRR+30KzxB3p5m/26oB3rGY42RcdMaunHCGeOgI/fpsg8VVKvVUKlU2LNnj37OjlarxcGDB/Hwww/X2153u2vDhg1QKpUYNGiQI8M1KcK/4Z3QXozFZK84Hx90G5Z9Yfm+eYS/EskJoRiUqDL5qSFbx6grzxWPkS3EhfjYpdyURJXFbRrSpr1bhePbs9cbEhKRXt2+N394IuYOboN30jJwUJOLr0/lmHil/bx+X2e4CX994qpaFHHfWwcbXe6gRJXBuPnD+et4bd95q8rQjRPOGAcduU+nXtkpLCxEeno60tPTAQAajQbp6enIzMyEIAiYPXs2li5dih07duDEiROYNGkSoqOj9ZOYAeC1117D0aNHcfbsWbz++ut45JFHkJqaiqCgIKfUqa7u6hBEBZqej+JIurkvxuZa2DpO3b4m/y3ebLl1Y3J3E5CcEIq7OjVDckKowa0SW8VYd5+OOkZugunLzbqYbBVDVKASE5PNt31DyzU3V0fHmjbV1V0XL1FDmeqfXh5umNq7BVZP6OLQ8VjXt+9oEWowrt3RItTiuGjuapSpcfPxQbdZVT83AfqP1zvyvcrce5G9ODXZOXz4MJKSkpCUlAQAmDNnDpKSkrBw4UIAwJNPPolHH30U06dPR7du3VBYWIhdu3ZBqfxrQDx06BAGDRqE9u3bY+3atXjzzTcxa9Ysp9THGHc3Qf9Ry4Z2IsHE71JfU/tvU3MtzMVp7f5r78vLw81iuZbmf0iJUSpj+7T3MRL+/NHNHzDXDtP7NH6OgQDpbW9snaVybXmsjPUVV/jHwFEEAIMSI5wdRoNZe6wa0u+sKdtS/7TFuW5NPDARk5TxdlpvtX7ssEW5xkzrrdY/b8fW46AlUscSW3GZj547E5+zYz5Oc/sP9vGEaCJee32flbGyfBXuKC6rsvhsGmc/Z0dKOxh75oqzn7Njy2PVkHh1jLWDsT5obJk55ra3VFbdZ8lI3XdTe86OuXP9jW/P4/gf2nple7gJqKxVKUc+Z8ccY32sMc/ZkToOSonD2vFCarm1mXvOjtHx1csdxUbapi4px9fW343V5J6z40yO+m6sW+UJyo74pnJjZRl7+qmrPUFZajsY+5ZmRz1BGSJwtVBaudYeK13c1wvLJPWVK3klktoeMP2039pPDI7wV9a0m7ZU/yRiVUCd7fNLTK7jE5RNn+uFpZV4/P1jyLxZgthgb7w0NgneXu6S+p3uCcoZN4rg7emOwberEB3oXa/d9MdPYr+X2i/NjSGA6ScoN2QclBKHteOFpXLNvVdY2zYXrhXiTHYBlJ7uUIf5Gn2Ccu0yjJ1Ptryiw2THCvwiUCIioqanyT9UkIiIiMgWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGsNSnbeeecd9OzZE9HR0bh48SIA4OWXX8ann35q0+CIiIiIGsvqZGf16tWYM2cOhg8fjry8PFRVVQEAgoKC8PLLL9s6PiIiIqJGsTrZefXVV7Fu3To8/fTTcHd31y/v2rUrTpw4YdPgiIiIiBrL6mRHo9EgKSmp3nKFQoGioiKbBEVERERkK1YnO2q1Gunp6fWW79q1C23btrVFTEREREQ242HtC+bMmYOZM2eitLQUoiji0KFD2Lp1K1JTU/HWW2/ZI0YiIiKiBrM62XnwwQfh7e2N//znPyguLsa9996L6OhovPLKKxg3bpw9YiQiIiJqMEEURbGhLy4uLkZhYSEiIiJsGZPDabVaBAYGIj8/HwEBAc4Oh4iIiCSQ+v5t9ZUdjUaDyspKtGrVCj4+PvDx8QEAnDt3Dp6enoiPj29w0ERERES2ZvUE5SlTpuDHH3+st/zgwYOYMmWKLWIiIiIishmrk51jx46hZ8+e9ZbfcccdRj+lRURERORMVic7giCgoKCg3vL8/Hz905SJiIiIXIXVyU6fPn2QmppqkNhUVVUhNTUVvXr1smlwRERERI1l9QTl559/Hn369EHr1q3Ru3dvAMCBAweg1Wqxd+9emwdIRERE1BhWX9lJTEzE8ePHcc899+Dq1asoKCjApEmTcPr0abRr184eMRIRERE1WKOesyMXfM4OERFR02PT5+wcP34c7dq1g5ubG44fP2522w4dOlgXKREREZEdSUp2OnXqhOzsbERERKBTp04QBAHGLggJgsBPZBEREZFLkZTsaDQahIeH638nIiIiaiokJTtxcXEAgIqKCixZsgQLFiyAWq22a2BEREREtmDVp7E8PT2xbds2m+18//79GDlyJKKjoyEIArZv326wXhRFLFy4EFFRUfD29kZKSgrOnTtnsM3Zs2dx1113ISwsDAEBAejVqxf27dtnsxiJiIioabP6o+ejRo2ql5Q0VFFRETp27IjXX3/d6PoVK1Zg1apVWLNmDQ4ePAhfX18MGTIEpaWl+m3+7//+D5WVldi7dy+OHDmCjh074v/+7/+QnZ1tkxiJiIioabP6o+dLly7Ff//7XwwcOBBdunSBr6+vwfpZs2Y1LBBBwCeffIJRo0YBqLmqEx0djblz52LevHkAar6SIjIyEps2bcK4ceNw/fp1hIeHY//+/foHHBYUFCAgIAC7d+9GSkqKpH3zo+dERERNj00/el7b+vXrERQUhCNHjuDIkSMG6wRBaHCyU5dGo0F2drZBwhIYGIgePXogLS0N48aNQ2hoKFq3bo23334bnTt3hkKhwJtvvomIiAh06dLFZNllZWUoKyvT/63Vam0SMxEREbkeq5MdR30aS3cbKjIy0mB5ZGSkfp0gCPjmm28watQo+Pv7w83NDREREdi1axeCg4NNlp2amoolS5bYL3giIiJyGVbN2fnpp5/w9NNP44knnsCuXbvsFZNkoihi5syZiIiIwIEDB3Do0CGMGjUKI0eORFZWlsnXzZ8/H/n5+fqfS5cuOTBqIiIiciTJyc5HH32Enj174pVXXsFbb72FESNG4IUXXrBbYCqVCgCQk5NjsDwnJ0e/bu/evfjss8/wv//9Dz179kTnzp3xxhtvwNvbG5s3bzZZtkKhQEBAgMEPERERyZPkZCc1NRXTpk1Dfn4+bt68iaVLl+K5556zW2BqtRoqlQp79uzRL9NqtTh48CCSk5MBAMXFxQAANzfDari5uaG6utpusREREVHTITnZOXPmDObNmwd3d3cAwNy5c1FQUICrV682eOeFhYVIT09Heno6gJr5QOnp6cjMzIQgCJg9ezaWLl2KHTt24MSJE5g0aRKio6P1n9hKTk5GcHAwJk+ejF9++QVnz57FE088AY1GgxEjRjQ4LiIiIpIPyROUi4uLDW73eHl5QalUorCwEBEREQ3a+eHDh9G/f3/933PmzAEATJ48GZs2bcKTTz6JoqIiTJ8+HXl5eejVqxd27doFpVIJAAgLC8OuXbvw9NNPY8CAAaioqMDtt9+OTz/9FB07dmxQTERERCQvkp+z4+bmhqVLl8LPz0+/7KmnnsITTzyBsLAw/TJbffTckficHSIioqZH6vu35GQnPj4egiCY3UYQBPz+++/WReoCmOwQERE1PTZ/qGBGRoYt4iIiIiJyKKu/G4uIiIioKWGyQ0RERLLGZIeIiIhkjckOERERyRqTHSIiIpI1SZ/G0mq1kgvkR7eJiIjIlUhKdoKCgiw+Y0enqqqqUQERERER2ZKkZGffvn363zMyMvCvf/0LU6ZM0X8hZ1paGjZv3ozU1FT7RElERETUQJKfoKwzcOBAPPjggxg/frzB8vfeew9r167Ft99+a8v4HIJPUCYiImp6pL5/Wz1BOS0tDV27dq23vGvXrjh06JC1xRERERHZldXJTkxMDNatW1dv+VtvvYWYmBibBEVERERkK5K/G0vnpZdewpgxY/Dll1+iR48eAIBDhw7h3Llz2LZtm80DJCIiImoMq6/sDB8+HGfPnsXIkSORm5uL3NxcjBw5EmfPnsXw4cPtESMRERFRg1k9QVmOOEGZiIio6bHbBGUAOHDgACZMmIC//e1vuHz5MgDgnXfewffff9+waImIiIjsxOpkZ9u2bRgyZAi8vb1x9OhRlJWVAQDy8/Px3HPP2TxAIiIiosawOtlZunQp1qxZg3Xr1sHT01O/vGfPnjh69KhNgyMiIiJqLKuTnTNnzqBPnz71lgcGBiIvL88WMRERERHZjNXJjkqlwvnz5+st//7779GiRQubBEVERERkK1YnO9OmTcNjjz2GgwcPQhAEXLlyBe+++y7mzZuHhx9+2B4xEhERETWY1Q8V/Ne//oXq6moMHDgQxcXF6NOnDxQKBebNm4dHH33UHjESERERNViDn7NTXl6O8+fPo7CwEImJifDz87N1bA7D5+wQERE1PXZ7zs4DDzyAgoICeHl5ITExEd27d4efnx+KiorwwAMPNCpoIiIiIluzOtnZvHkzSkpK6i0vKSnB22+/bZOgiIiIiGxF8pwdrVYLURQhiiIKCgqgVCr166qqqvDFF18gIiLCLkESERERNZTkZCcoKAiCIEAQBNx222311guCgCVLltg0OCIiIqLGkpzs7Nu3D6IoYsCAAdi2bRtCQkL067y8vBAXF4fo6Gi7BElERETUUJKTnb59+wIANBoNYmNjIQiC3YIiIiIishWrJyjv3bsXH330Ub3lH374ITZv3myToIiIiIhsxepkJzU1FWFhYfWWR0RE8FvPiYiIyOVYnexkZmZCrVbXWx4XF4fMzEybBEVERERkK1YnOxERETh+/Hi95b/88gtCQ0NtEhQRERGRrVid7IwfPx6zZs3Cvn37UFVVhaqqKuzduxePPfYYxo0bZ48YiYiIiBrM6i8CffbZZ5GRkYGBAwfCw6Pm5dXV1Zg0aRLn7BAREZHLafAXgZ49exa//PILvL290b59e8TFxdk6NofhF4ESERE1PVLfv62+sqNz2223GX2SMhEREZErkZTszJkzB88++yx8fX0xZ84cs9u++OKLkne+f/9+rFy5EkeOHEFWVhY++eQTjBo1Sr9eFEUsWrQI69atQ15eHnr27InVq1ejVatWAIBvv/0W/fv3N1r2oUOH0K1bN8mxEBERkTxJSnaOHTuGiooK/e+mWPtU5aKiInTs2BEPPPAARo8eXW/9ihUrsGrVKmzevBlqtRoLFizAkCFDcOrUKSiVSvztb39DVlaWwWsWLFiAPXv2oGvXrlbFQkRERPLU4Dk7tiYIgsGVHVEUER0djblz52LevHkAgPz8fERGRmLTpk1GP/lVUVGBZs2a4dFHH8WCBQsk75tzdoiIiJoeqe/fVn/03FE0Gg2ys7ORkpKiXxYYGIgePXogLS3N6Gt27NiBGzdu4P777zdbdllZGbRarcEPERERyZOk21jGbjGZ8vHHHzc4mNqys7MBAJGRkQbLIyMj9evqWr9+PYYMGYLmzZubLTs1NRVLliyxSZxERETk2iRd2QkMDNT/BAQEYM+ePTh8+LB+/ZEjR7Bnzx4EBgbaLVBL/vjjD3z11VeYOnWqxW3nz5+P/Px8/c+lS5ccECERERE5g6QrOxs3btT//tRTT+Gee+7BmjVr4O7uDgCoqqrCP//5T5vOd1GpVACAnJwcREVF6Zfn5OSgU6dORmMMDQ3FnXfeabFshUIBhUJhs1iJiIjIdVk9Z2fDhg2YN2+ePtEBAHd3d8yZMwcbNmywWWBqtRoqlQp79uzRL9NqtTh48CCSk5MNthVFERs3bsSkSZPg6elpsxiIiIio6bP6oYKVlZU4ffo0WrdubbD89OnTqK6utqqswsJCnD9/Xv+3RqNBeno6QkJCEBsbi9mzZ2Pp0qVo1aqV/qPn0dHRBs/iAYC9e/dCo9HgwQcftLY6REREJHNWJzv3338/pk6digsXLqB79+4AgIMHD2L58uUWPwVV1+HDhw0eCqh7YOHkyZOxadMmPPnkkygqKsL06dORl5eHXr16YdeuXVAqlQblrF+/Hn/729/Qpk0ba6tDREREMmf1c3aqq6vxwgsv4JVXXtE/0C8qKgqPPfYY5s6da3B7q6ngc3aIiIiaHqnv3416qKDu+TRNPUFgskNERNT02PWhgpWVlfjmm2+wdetW/VdEXLlyBYWFhQ2LloiIiMhOrJ6zc/HiRQwdOhSZmZkoKyvDoEGD4O/vj+effx5lZWVYs2aNPeIkIiIiahCrr+w89thj6Nq1K27evAlvb2/98rvvvtvgY+JERERErsDqKzsHDhzAjz/+CC8vL4Pl8fHxuHz5ss0CIyIiIrIFq6/sVFdXo6qqqt7yP/74A/7+/jYJioiIiMhWrE52Bg8ejJdffln/tyAIKCwsxKJFizB8+HBbxkZERETUaFZ/9PzSpUsYOnQoRFHEuXPn0LVrV5w7dw5hYWHYv38/IiIi7BWr3fCj50RERE2PXZ+zU1lZiffffx+//PILCgsL0blzZ9x3330GE5abEiY7RERETY9dkp2Kigq0adMGn332Gdq2bWuTQF0Bkx0iIqKmxy4PFfT09ERpaWmjgyMiIiJyFKsnKM+cORPPP/88Kisr7REPERERkU1Z/Zydn3/+GXv27MHXX3+N9u3bw9fX12D9xx9/bLPgiIiIiBrL6mQnKCgIY8aMsUcsRERERDZndbKzceNGe8RBREREZBeS5+xUV1fj+eefR8+ePdGtWzf861//QklJiT1jIyIiImo0ycnOsmXL8O9//xt+fn5o1qwZXnnlFcycOdOesRERERE1muRk5+2338Ybb7yBr776Ctu3b8fOnTvx7rvvorq62p7xERERETWK5GQnMzPT4LuvUlJSIAgCrly5YpfAiIiIiGxBcrJTWVkJpVJpsMzT0xMVFRU2D4qIiIjIViR/GksURUyZMgUKhUK/rLS0FA899JDBs3b4nB0iIiJyJZKTncmTJ9dbNmHCBJsGQ0RERGRrkpMdPl+HiIiImiKrvxuLiIiIqClhskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWmOwQERGRrDHZISIiIlljskNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7REREJGtMdoiIiEjWPJy58/3792PlypU4cuQIsrKy8Mknn2DUqFH69aIoYtGiRVi3bh3y8vLQs2dPrF69Gq1atTIo5/PPP8czzzyD48ePQ6lUom/fvti+fbtjK9NAVdUiDmlycbWgFBH+SnRXhwBAvWXuboKTI22adO2bnV+C3KJyhPgpoApoWm1qrI84M/bGxONqdXGW8spqvJOWgYu5xYgL8cHE5Hh4eRj+7yl1bDC2zFybWjoGrjYm2aLP2KvfSS1X7v1eV78rN4uR/kceAAHxocb7tbM4NdkpKipCx44d8cADD2D06NH11q9YsQKrVq3C5s2boVarsWDBAgwZMgSnTp2CUqkEAGzbtg3Tpk3Dc889hwEDBqCyshInT550dFUaZNfJLCzZeQpZ+aX6ZUE+ngCAvOIK/bKoQCUWjUzE0HZRDo+xKTPWvjpNpU2N1cGZsTcmHleri7OkfnEK6w5oUC3+tWzZF79hWm815g9PBCB9bLB2vLB0DFxtTLJFn7FXv5Nartz7vblxtm6/diZBFEXR8mb2JwiCwZUdURQRHR2NuXPnYt68eQCA/Px8REZGYtOmTRg3bhwqKysRHx+PJUuWYOrUqQ3et1arRWBgIPLz8xEQEGCL6li062QWHt5yFFIaX5f/r57QWRYnhyNIaV8Brt2mpurgrP7QmHhcrS7OkvrFKby5X2Ny/Yw+aiTFBkseG4wx1aaWjsH0Pmqs3a9xmTHJFn3GXv1Oarly7/dS38dm9LFfwiP1/ds1ri8ZodFokJ2djZSUFP2ywMBA9OjRA2lpaQCAo0eP4vLly3Bzc0NSUhKioqIwbNgwi1d2ysrKoNVqDX4cqapaxJKdpyQPZrrtluw8hapql8hNXZo17euqbWquDs7oD42Jx9Xq4izlldVYd8B0ogMA6w5osOjTkw1OdADjbWrpGIh/7ttVxiRb9Bl79Tup5ZZXVsu631szzq47oEF5ZbXdYzLHqbexzMnOzgYAREZGGiyPjIzUr/v9998BAIsXL8aLL76I+Ph4/Pe//0W/fv1w9uxZhISEGC07NTUVS5YssWP0hvT3M/NKkH7pJrLzS41e8jNHBJCVX4pDmlwkJ4TaJB5r7snr5hhk3CgGIKJT8yBEB/tYvPdsbG6Cu5ug31eYnwIQgauFZcgtLEOIrxdUgd7org5BVbWId9IyoLlRBAFAUkwwooK868Ub5qsABOB6YRki/JWoFkVJ7WusTWu3jS6260Vl9drE2FygCD/DOIxtf7WgFCHeXjidU4CM64U4nV0AL3cB5VUiblP5oWW4PyYmx+PIxZtm62CuPzRmnpKpuSSHNLmS4nl4yxF0iw9GG1UAcovLEeGvRHlFlaTXvrT7LHq2DDM4vll5JTh26SaqRBHugmDQB3T9w1TfCvHxwulsLS7dLKk3L0bfRtrSev1O6vwVXfsG+3jhZnG5yTJ03knLgKX3tWoRyCkoN7+RBLo2/enCDbi5Cfjh/HWL54S177m6fXx/7hq++S0HGTeKER/qg38PT4S3l7vJ10kZfyydw1LGQ6l99pAmF93VISbHv7rxVldLi23jD79L2k53jCyNOcZImftlrA61y9WVce5aAc5lF0Lp6Y74MB8MTlQhr6TC9DGy0A61VYtAz+V70CkmCC+NTYKf0vGph8vexvrxxx/Rs2dPXLlyBVFRf13mu+eeeyAIAt5//3289957uO+++/Dmm29i+vTpAGqu2jRv3hxLly7FjBkzjO6rrKwMZWVl+r+1Wi1iYmLschvL3P3MhnhlXCfc1amZTeOxdE/e2BwDY9vVZex1AgAfL3cUlVeZjdPHyx0lFVUw1juNxWuw3tsTeSXG1xmja1NLx0pXVwCSjqm12+u4CUD/1uHYc/qa5Nh1GjNPydjxchOAab3VqKgSseGHDEnxN5al4wsAvl7uKC6vMviv0lLf0tUlKTZYUhtJPVfMlVHbwk9P4u20iyZfZw/Wngu2MigxAusmdau3XGqb+vx5fC0xNx5+mn4Zj/0v3WIZU3vG44uT2Ubn1AD1z11bt6m58qwdX3V9vPYtI3Nzho5l3jQ5thvEaGy+WCPaoUPzAOx4pHeDXluX1NtYLpvs/P7770hISMCxY8fQqVMn/XZ9+/ZFp06d8Morr2Dfvn0YMGAADhw4gF69eum36dGjB1JSUrBs2TJJ+7bXnB1r5uVItXXaHQ2+stOQeUIpiRHYfeqqxW3r3nu2NDfBlWyddgfyS8olzfGx5lhau31D1O4PjZmn1JSOl70JsG7+iqky6rbz+gO/49nPf7NFiE1C3YTH0eNh2oUbGL/upwaV64hzVwpTc3ukzP2aPzzR7JwhZ9fPVglPk5+zo1aroVKpsGfPHv0yrVaLgwcPIjk5GQDQpUsXKBQKnDlzRr9NRUUFMjIyEBcX5/CYa7N2Xo4lAmqycd3lRHvHo7uPbynR0W1b+96zlLkJrkDXpl3igiW1jbXH0t6DiZsAdIkLBtC4eUpN5Xg5irXzV0yVUbedJybHQ0afNrZo96mrKPnz6owzxsPu6hBEBSphqskFwOTxcHYioGNsbo/UuV8l5VUW5ww50/E/tCgsrXTY/pya7BQWFiI9PR3p6ekAaiYlp6enIzMzE4IgYPbs2Vi6dCl27NiBEydOYNKkSYiOjtZf/QkICMBDDz2ERYsW4euvv8aZM2fw8MMPAwD+8Y9/OKlWNSzdL7aG7nxcNDKxwc9msGU8xujufQPS5ia4ikUjEy3Oj3FV1SJw5OJNANKPb+15CjpN6Xg5ii3ao247e3m4YVpvdeMLbkKe++IUANuPPyIsj4fuboL+VlTdrXRXNppCv697zkqd+/XcF7abPmEvj79/zGH7cuoE5cOHD6N///76v+fMmQMAmDx5MjZt2oQnn3wSRUVFmD59OvLy8tCrVy/s2rVL/4wdAFi5ciU8PDwwceJElJSUoEePHti7dy+Cg4MdXp/arhY0rJMF+3hChOG9UZUNnsnQ0Hgaso+LucV235dUpu4r174X/mn6ZSdEZhu6Nrf2+Nbe3pWOl9zUPS7zhyfi3NVC7JUwH0sOaj7QYPvx54Ge8ZLGw6HtorB6Qud6c1ZUgUoMa6dy2Dw0W7B2fNW1vSvLvFnisH05Ndnp168fzE0ZEgQBzzzzDJ555hmT23h6euKFF17ACy+8YI8QGyzCX2l5o1pGdYrG2G6xdntaqbXxNGYfcSE+dt+XVK/f2xluboLZTyY5om3sRRe7tXWovb0rHS+5MXZcpvVOaBLJTrvoAJy80rjHcsSH1vQtW59jgxJVkrcd2i4KgxJV9cbUQ5rcJpXsWDu+xof64MA5e0bUeLHB3g7bl8vO2WnqdPeLpXATgBV/74jkhFC4uwlwdxOQnBCKuzo10y+zVTz2mjJQ+/65K8xN0N3TvyMhFMkJobi7c3NM7d0CdyfVb1N7t4091J2zILW/GZvr4ArHy9W4CfVvfVjL1JwSZ/c3Kft1E4D/TU9udL/495+fCrJVnRs6d9HYmOrs4yBV3TpLOV/dhJq2d/X6vTQ2yWH7YrJjJ7r7xVI62rTeart/f4i5+9fGCH/+DEqMkLRt7fvnzp6bYO0cJ6ltY+2gIZj4XapBiRH642Cs3Nr1s6a/1W0XqcfLVBxyIwD69mhoHeueE7VZey42hqljZum8ntZbDT+lR6PO40GJEfrn7diizraYu1ibpTk9xn63tK4hpIw51o6v03qr4e3lLql+DdXYdujQPMChz9thsmNHuvvFpv7jdhPs+xhtU/Go6sQT5OOpf46CjipQidUTOmPdpG6Y0Udt8j+JqD+3q3v/fP7wRKOvE1DzjBRLfLzcIZjYZ7CReI3Fbs0cJ1NtU7fcNRM6Y42ZY2pqe3Pl1qXrF+smdTMak6n6Wepvpo4VYPp46WIxVgdd/cz1j4Yw1h/r8lW4Gx3AzfWt2nWx1EbzhycabXtLfa92Geb6n5T+VpfUJo4y0+8sndd1xyRT/UJHYeKfNGPP2TFVZ2Ntam5MsuXXK5iKydy5K/W8FoSaccwYc+dV3X1ZM77WPX6W6if13DV1PEy1g6XzxJbP2ZHKZZ6z40z2/m6suk9QBoD4UF+nfSOs3J+g3Jg5Tq7yBOXa/cLab0y2xxOULcVR+3Uxwd4GT1DuFBOE9w5eNFh3vahcf7wjApT12ll3fOX0BGWL/e3Pfny1oOZcCPD2xPE/8gD8NV4A0Ne5WZASAgRculkCU+ekpb4j9Qm85s7/8spqPPfFKZs+QdnR37Rurp2krtOd15du1u+P5r4NXOqYY4wzn6BsqY1022fmFuGDny+hsKwSsSE+Nn+CcpN7qKAzOeOLQImIiKhxmvxDBYmIiIhsgckOERERyRqTHSIiIpI1JjtEREQka0x2iIiISNaY7BAREZGsMdkhIiIiWWOyQ0RERLLGZIeIiIhkjckOERERyRqTHSIiIpI1JjtEREQka0x2iIiISNaY7BAREZGsMdkhIiIiWWOyQ0RERLLGZIeIiIhkjckOERERyRqTHSIiIpI1JjtEREQka0x2iIiISNaY7BAREZGsMdkhIiIiWWOyQ0RERLLGZIeIiIhkjckOERERyRqTHSIiIpI1JjtEREQka0x2iIiISNaY7BAREZGsMdkhIiIiWWOyQ0RERLLGZIeIiIhkzanJzv79+zFy5EhER0dDEARs377dYL0oili4cCGioqLg7e2NlJQUnDt3zmCb+Ph4CIJg8LN8+XIH1oKIiIhcmVOTnaKiInTs2BGvv/660fUrVqzAqlWrsGbNGhw8eBC+vr4YMmQISktLDbZ75plnkJWVpf959NFHHRE+ERERNQEeztz5sGHDMGzYMKPrRFHEyy+/jP/85z+46667AABvv/02IiMjsX37dowbN06/rb+/P1QqlUNiJiIioqbFZefsaDQaZGdnIyUlRb8sMDAQPXr0QFpamsG2y5cvR2hoKJKSkrBy5UpUVlaaLbusrAxardbgh4iIiOTJqVd2zMnOzgYAREZGGiyPjIzUrwOAWbNmoXPnzggJCcGPP/6I+fPnIysrCy+++KLJslNTU7FkyRL7BE5EREQuxWWTHanmzJmj/71Dhw7w8vLCjBkzkJqaCoVCYfQ18+fPN3idVqtFTEyM3WMlIiIix3PZ21i6OTg5OTkGy3NycszOz+nRowcqKyuRkZFhchuFQoGAgACDHyIiIpInl0121Go1VCoV9uzZo1+m1Wpx8OBBJCcnm3xdeno63NzcEBER4YgwiYiIyMU59TZWYWEhzp8/r/9bo9EgPT0dISEhiI2NxezZs7F06VK0atUKarUaCxYsQHR0NEaNGgUASEtLw8GDB9G/f3/4+/sjLS0Njz/+OCZMmIDg4GAn1YqIiIhciVOTncOHD6N///76v3XzaCZPnoxNmzbhySefRFFREaZPn468vDz06tULu3btglKpBFBzO+p///sfFi9ejLKyMqjVajz++OMG83GIiIjo1iaIoig6Owhn02q1CAwMRH5+PufvEBERNRFS379dds4OERERkS0w2SEiIiJZY7JDREREssZkh4iIiGSNyQ4RERHJGpMdIiIikjUmO0RERCRrTHaIiIhI1pjsEBERkawx2SEiIiJZY7JDREREssZkh4iIiGSNyQ4RERHJGpMdIiIikjUmO0RERCRrTHaIiIhI1pjsEBERkawx2SEiIiJZY7JDREREssZkh4iIiGSNyQ4RERHJGpMdIiIikjUmO0RERCRrTHaIiIhI1pjsEBERkawx2SEiIiJZY7JDREREssZkh4iIiGSNyQ4RERHJGpMdIiIikjUmO0RERCRrTHaIiIhI1jycHcCtrqpaxCFNLq4WlCLMTwGIwPWiMkT4K9FdHQJ3N8Fgu+z8EuQWlSPETwFVQM02VdUi3knLwMXcYsQE+6CNyh+5xeX6MgDo92GyXG0pcgvLEOLrBVWgt8E2deM0VW6XuGAcuXizpi6+CkAArhfWr4vU9pASv6XX1q2npdhqbxfi7YXTOQW4dLMYcSE+uLdHHNIv5TWqflKVV1brj2lciA8mJsfDy6P+/ybGjl+YrwKncwqQmVsMQESn5kFQBXoDAnC1oOyv7fwUOJ1dgMzcIgBAp5hgRAfVHPuS8io8/v4xZN4sQWywN14amwQ/penhwlzbW9pe15a1Y4vwV9Ys05Yit6gcwT5euFlcbrAuO78U6ZduAgDiQ33rtZGxNnR3Eyz2WaltBNTvl7plxs4nS+epufYqKa/Cc1+cQsaNYsSH+uDfwxPh5eHWoHPF2LGqF1ukP64XlenHmgj/mrHpakFpvfHH3D4b0y+sPa+kjh3WtlGIjxdOZ2tx6WaJpHPRWLnm+mJWXgmOXboJEUBssA9EiLicV2qyz0p9X2ho3Wuzdhwyei4Yic2WY6VUgiiKosP36mK0Wi0CAwORn5+PgIAAh+1318ksLNl5Cln5pUbXRwUqsWhkIgCY3M7Xyx3F5VUwdRCDfDwBAHnFFVaVq9tmaLsoo3EaK9dNAKpNBFK7PFOk7sdYWcZeK6We1mxnjpT6SZX6xSmsO6AxaEs3AZjWW435wxP1yyz1n4bycBNQaeRAdmgegB2P9K633FzbG2sPe8Vdu42MtaEgAN6eNedL7deY6rPmGOuXxpbp+Hi5o6SiCqZGW3PtNe3tn7H71NV6y7083FBeWW12/3XLNdb2lmIzx9rxxdp+IfW8kjp2NLSNapN6LurKPZZ5U1JfNEZAzfEpqrWdlPGqoXWvrTHjkLlzwZZjJSD9/ZvJDpyT7Ow6mYWHtxw1maQANR3dHgdHarkCgOl91Fi7X9PoOHR5/OoJnU0OdJbaw1RZpl5rTT3tXT+pUr84hTf3a0yun9GnZqCxpr1sqW7CY67tgfrt4Yi4OzQPwPE/tHbcg+2Zai9TiU5DygXgkD5j7nyytl9IOa8a26ca2kaWzkVnj99SywLqt6+9xyHByD4bismOFeyR7KRn5GHUmh/0f0f7Ciisqrn811rlh5OXC1BSUW3q5bLl4+WOjs38caOoAmH+CjQP9MaRi7m4cKPE6rLcBKC7Ohi/XMp3mbZUeLghNsQbAoCkuCC0DPdHG1UArheWmbz9eO5aAc5lF8LLww1pv+da3MfSUbfjha9OI6/E/H+F9tIyzBuBPgr8o2tzLP/yNPJKKk1u6wYgPlSJ8iqgWbA3jmXehIV/Zm9pvp4C7kpqjmsFZQj08cBHR67YrOyGXsWyl64x/gj280ZKYiSe++wU8stMdwx3AXh2VDvEhfoa3E4L9vHCkp0nkV/a+E7lIQAQjF/VNOXofwah5/N7XGb8aagApQdGdoiCm5uA9s0C8dS2ExYTmO7xQfjlj3yUVVrfqQQAqkAlvn9qQKNvaTHZsYKtk534f31ug6hIzizdfiQikrut0+5AckJoo8qQ+v7NCco2xkSHpCji5Q0iusVdLbDtvD1znPrR8/3792PkyJGIjo6GIAjYvn27wXpRFLFw4UJERUXB29sbKSkpOHfunNGyysrK0KlTJwiCgPT0dPsHb0R6Rp5T9ktERNTUBCk9HbYvpyY7RUVF6NixI15//XWj61esWIFVq1ZhzZo1OHjwIHx9fTFkyBCUltbPBp988klER0fbO2Szas/RISIiItN2/5bjsH059TbWsGHDMGzYMKPrRFHEyy+/jP/85z+46667AABvv/02IiMjsX37dowbN06/7Zdffomvv/4a27Ztw5dffumQ2ImIiKjhLuYWO2xfLvsEZY1Gg+zsbKSkpOiXBQYGokePHkhLS9Mvy8nJwbRp0/DOO+/Ax8fHGaESERGRleJDHfee7bLJTnZ2NgAgMjLSYHlkZKR+nSiKmDJlCh566CF07dpVctllZWXQarUGP7aw/aGeNimHiIhI7v5d6+GE9uayyY4Ur776KgoKCjB//nyrXpeamorAwED9T0xMjE3i6RQfZJNyiIiI5GxQYgS8vdwdtj+XTXZUKhWAmttUteXk5OjX7d27F2lpaVAoFPDw8EDLli0BAF27dsXkyZNNlj1//nzk5+frfy5dumSzuDOWj7BZWSRfvgp3NO5RWkREDdeheQBm9FHDCV9ThUGJEVg3qZtD9+myz9lRq9VQqVTYs2cPOnXqBKDm4UEHDx7Eww8/DABYtWoVli5dqn/NlStXMGTIELz//vvo0aOHybIVCgUUCoXdYs9YPsLsE5TbRPmjZbgfYoN9kXmzGGeyC6D0dEN8iC+ah/rgcl5JzRN4Y4Lh4+mOZz8/heuFpXB3c8PtzQKQX1yBMD8vqMP9MDhRhRxtKd7/+RIKyyrRPNgbXWKDkaWt+cRap5hgqAL+/OLEvBKk/5GH8spq/HD+OiqqqhEd5I2U1pG4oi3B6ewCKDzd0CLMF4PaqpClLcGG7zXQllYiOsgbM3snIK+sAul/5AEQEBWowJGLN5GZW4yS8io0C1YiQOmFwberEOTpgRV7zuBqQRlEUUTbSF+0iAiECBGZN4pwvbD8rycoZ+biemEF/JUeeKBnC3x+4goKyioRE6xE19gQZNwsxg/nrqOyqhqqICVaRfjjj5vFuFFYjnB/BWJDau77ZuaWoLSiCrdF+qFZkDf2nr6KK/mlqKyqRlyoEjnaCnh7uSMm2BsR/kpculmzfWuVP5oHe+PIxZu4lFsCQERSXBBignyw9/RVZOWXwl/pgUl/i8OZ7MKaugcocCQzF5dulsJf4YGx3WIQ7qvAuz9nIvNGsb4Ma56grPR0R1yoD2KDfXA5vxS6L/EM8fHCuz9n4lJuMfz+3FdUgDdO5xRAc70QZ7IL4O4O5BVVIMxPgbhQHwACMq4X4tLNEnh71ZR7X7c43Cgux1e/ZqOkogrNg7xxtaAUmTeKcbWwDN6e7ogOUiKltQpXCkpxVVuKCH8FWoT7YWJyPMorq2u+HNRIHBnXC3H6z34cE+IDAQIybhThUm4xvNyB8iqgeYgPYoK9ce5qAbLzy+DhJqBny1AkhPtDBHA5768vWrymLcOwVd+hqKwKCg83PD44AZk3ygAIiA5S4o/cYmTcKK453io/xAT54EjmTWiuFiC7oBweHm6I9FNgZMco5GjLcLWgDBEByj+fbi3gjz/3NbZbLN7/ORMXc4sRHajExdwi/H6tCL9l1dzajvRX4M5O0bicX6o/Ri3CffHv4Ymoqhbx+PvHcD4nHzkFFfD0cEOEnwJPDG6DwvJK/Zc7xoX4oI0qAFfyS/DBn+dpTIgP7usai+vFZfj6VA5KKqoRG+KN2BAfXLpZgqvaUoT5K3C9oAwRAQo0D/YxqHNrlT+aB9WcO5m5JSgpr0JMiDdiQ33gBjdcvFGE64VlCPT2gOZ6MdzdBDQL8sY/+7VCQXllvS+N1dwoxDVtGSIDlIgNqfkySs31Ihy9WPNFq9GBStwsqUCOtua4/a1lGBQebujUPAgR/kqDL8ydmByP/OIK3P3G97heWAo3wQ3tmgciJsgb+05fRUF5JdwgoEMzfwT5KjH4dhVClJ54ff95ZOXVnGv391IjwkeB1/dfQFZeSc3Y0EuNmBDfel9IGqj0xHsHM5D557nbrrk/Ll4vhdLTHc2DlTibU4DLucXIL62CwtMN4X5e6BwXjMt5pSirqP7z/PfBkYwbuHSz5knuHWOC8Pu1Iig93aD+czy8VliGr37NRnFFFVqE+epvwei+oDUm2BuxwT64lFdz/CICFIgN8YWAmom4V7WlCPLxwO/XiqH0dEezYCXO5RQgq/a5EOYPUQB+v16IY3+2fbMgbwAi/rhZgpKKakQFKvTHtHmwDzZM6Q4vDzd9HHEhPhjUNhLZ2hJ8cPgPFJZVoVmQAqJYjSvainpf7Dt3cBu8k5aBjBt/fdltmJ8Xvj6VjYs3iuHt6Y4ht6sQ6uuFdw9dxMUbNeN9bIg34sN8ERvsi0s3i3E6uwBe7gLKq0TcpvJD3J/vb5rrhbjx53ifEO6Hfw9PdOgVHR2nPkG5sLAQ58+fBwAkJSXhxRdfRP/+/RESEoLY2Fg8//zzWL58OTZv3gy1Wo0FCxbg+PHjOHXqFJRKZb3yMjIyoFarcezYMX2CJIWzvgiUiIiIGq5JPEH58OHD6N+/v/7vOXPmAAAmT56MTZs24cknn0RRURGmT5+OvLw89OrVC7t27TKa6BAREREZw+/GAq/sEBERNUVS379ddoIyERERkS0w2SEiIiJZY7JDREREssZkh4iIiGSNyQ4RERHJGpMdIiIikjUmO0RERCRrLvt1EY6ke9SQrb79nIiIiOxP975t6ZGBTHYAFBQUAIDNvv2ciIiIHKegoACBgYEm1/MJygCqq6tx5coV+Pv7QxDk813UWq0WMTExuHTp0i3zZGjWmXWWK9aZdZarxtRZFEUUFBQgOjoabm6mZ+bwyg4ANzc3NG/e3Nlh2E1AQMAtc9LosM63Btb51sA63xoaWmdzV3R0OEGZiIiIZI3JDhEREckakx0ZUygUWLRoERQKhbNDcRjW+dbAOt8aWOdbgyPqzAnKREREJGu8skNERESyxmSHiIiIZI3JDhEREckakx0iIiKSNSY7TVxqaiq6desGf39/REREYNSoUThz5ozBNv369YMgCAY/Dz30kJMibrzFixfXq0+bNm3060tLSzFz5kyEhobCz88PY8aMQU5OjhMjbrz4+Ph6dRYEATNnzgQgj2O8f/9+jBw5EtHR0RAEAdu3bzdYL4oiFi5ciKioKHh7eyMlJQXnzp0z2CY3Nxf33XcfAgICEBQUhKlTp6KwsNCBtbCOuTpXVFTgqaeeQvv27eHr64vo6GhMmjQJV65cMSjDWN9Yvny5g2sinaXjPGXKlHr1GTp0qME2cjrOAIye24IgYOXKlfptmtpxlvLeJGWszszMxIgRI+Dj44OIiAg88cQTqKystDoeJjtN3HfffYeZM2fip59+wu7du1FRUYHBgwejqKjIYLtp06YhKytL/7NixQonRWwbt99+u0F9vv/+e/26xx9/HDt37sSHH36I7777DleuXMHo0aOdGG3j/fzzzwb13b17NwDgH//4h36bpn6Mi4qK0LFjR7z++utG169YsQKrVq3CmjVrcPDgQfj6+mLIkCEoLS3Vb3Pffffh119/xe7du/HZZ59h//79mD59uqOqYDVzdS4uLsbRo0exYMECHD16FB9//DHOnDmDO++8s962zzzzjMGxf/TRRx0RfoNYOs4AMHToUIP6bN261WC9nI4zAIO6ZmVlYcOGDRAEAWPGjDHYrikdZynvTZbG6qqqKowYMQLl5eX48ccfsXnzZmzatAkLFy60PiCRZOXq1asiAPG7777TL+vbt6/42GOPOS8oG1u0aJHYsWNHo+vy8vJET09P8cMPP9Qv++2330QAYlpamoMitL/HHntMTEhIEKurq0VRlN8xBiB+8skn+r+rq6tFlUolrly5Ur8sLy9PVCgU4tatW0VRFMVTp06JAMSff/5Zv82XX34pCoIgXr582WGxN1TdOhtz6NAhEYB48eJF/bK4uDjxpZdesm9wdmKszpMnTxbvuusuk6+5FY7zXXfdJQ4YMMBgWVM+zqJY/71Jylj9xRdfiG5ubmJ2drZ+m9WrV4sBAQFiWVmZVfvnlR2Zyc/PBwCEhIQYLH/33XcRFhaGdu3aYf78+SguLnZGeDZz7tw5REdHo0WLFrjvvvuQmZkJADhy5AgqKiqQkpKi37ZNmzaIjY1FWlqas8K1qfLycmzZsgUPPPCAwRfXyu0Y16bRaJCdnW1wXAMDA9GjRw/9cU1LS0NQUBC6du2q3yYlJQVubm44ePCgw2O2h/z8fAiCgKCgIIPly5cvR2hoKJKSkrBy5coGXeZ3Jd9++y0iIiLQunVrPPzww7hx44Z+ndyPc05ODj7//HNMnTq13rqmfJzrvjdJGavT0tLQvn17REZG6rcZMmQItFotfv31V6v2zy8ClZHq6mrMnj0bPXv2RLt27fTL7733XsTFxSE6OhrHjx/HU089hTNnzuDjjz92YrQN16NHD2zatAmtW7dGVlYWlixZgt69e+PkyZPIzs6Gl5dXvTeDyMhIZGdnOydgG9u+fTvy8vIwZcoU/TK5HeO6dMeu9qCn+1u3Ljs7GxEREQbrPTw8EBISIotjX1paiqeeegrjx483+LLEWbNmoXPnzggJCcGPP/6I+fPnIysrCy+++KITo224oUOHYvTo0VCr1bhw4QL+/e9/Y9iwYUhLS4O7u7vsj/PmzZvh7+9f79Z7Uz7Oxt6bpIzV2dnZRs953TprMNmRkZkzZ+LkyZMG81cAGNzLbt++PaKiojBw4EBcuHABCQkJjg6z0YYNG6b/vUOHDujRowfi4uLwwQcfwNvb24mROcb69esxbNgwREdH65fJ7RiToYqKCtxzzz0QRRGrV682WDdnzhz97x06dICXlxdmzJiB1NTUJvmVA+PGjdP/3r59e3To0AEJCQn49ttvMXDgQCdG5hgbNmzAfffdB6VSabC8KR9nU+9NjsTbWDLxyCOP4LPPPsO+ffvQvHlzs9v26NEDAHD+/HlHhGZ3QUFBuO2223D+/HmoVCqUl5cjLy/PYJucnByoVCrnBGhDFy9exDfffIMHH3zQ7HZyO8a6Y1f3kxq1j6tKpcLVq1cN1ldWViI3N7dJH3tdonPx4kXs3r3b4KqOMT169EBlZSUyMjIcE6CdtWjRAmFhYfq+LNfjDAAHDhzAmTNnLJ7fQNM5zqbem6SM1SqVyug5r1tnDSY7TZwoinjkkUfwySefYO/evVCr1RZfk56eDgCIioqyc3SOUVhYiAsXLiAqKgpdunSBp6cn9uzZo19/5swZZGZmIjk52YlR2sbGjRsRERGBESNGmN1ObsdYrVZDpVIZHFetVouDBw/qj2tycjLy8vJw5MgR/TZ79+5FdXW1PvlranSJzrlz5/DNN98gNDTU4mvS09Ph5uZW71ZPU/XHH3/gxo0b+r4sx+Oss379enTp0gUdO3a0uK2rH2dL701Sxurk5GScOHHCILnVJfyJiYlWB0RN2MMPPywGBgaK3377rZiVlaX/KS4uFkVRFM+fPy8+88wz4uHDh0WNRiN++umnYosWLcQ+ffo4OfKGmzt3rvjtt9+KGo1G/OGHH8SUlBQxLCxMvHr1qiiKovjQQw+JsbGx4t69e8XDhw+LycnJYnJyspOjbryqqioxNjZWfOqppwyWy+UYFxQUiMeOHROPHTsmAhBffPFF8dixY/pPHi1fvlwMCgoSP/30U/H48ePiXXfdJarVarGkpERfxtChQ8WkpCTx4MGD4vfffy+2atVKHD9+vLOqZJG5OpeXl4t33nmn2Lx5czE9Pd3g/NZ9EuXHH38UX3rpJTE9PV28cOGCuGXLFjE8PFycNGmSk2tmmrk6FxQUiPPmzRPT0tJEjUYjfvPNN2Lnzp3FVq1aiaWlpfoy5HScdfLz80UfHx9x9erV9V7fFI+zpfcmUbQ8VldWVort2rUTBw8eLKanp4u7du0Sw8PDxfnz51sdD5OdJg6A0Z+NGzeKoiiKmZmZYp8+fcSQkBBRoVCILVu2FJ944gkxPz/fuYE3wtixY8WoqCjRy8tLbNasmTh27Fjx/Pnz+vUlJSXiP//5TzE4OFj08fER7777bjErK8uJEdvGV199JQIQz5w5Y7BcLsd43759Rvvy5MmTRVGs+fj5ggULxMjISFGhUIgDBw6s1xY3btwQx48fL/r5+YkBAQHi/fffLxYUFDihNtKYq7NGozF5fu/bt08URVE8cuSI2KNHDzEwMFBUKpVi27Ztxeeee84gMXA15upcXFwsDh48WAwPDxc9PT3FuLg4cdq0aQYfPRZFeR1nnTfffFP09vYW8/Ly6r2+KR5nS+9NoihtrM7IyBCHDRsment7i2FhYeLcuXPFiooKq+MR/gyKiIiISJY4Z4eIiIhkjckOERERyRqTHSIiIpI1JjtEREQka0x2iIiISNaY7BAREZGsMdkhIiIiWWOyQ0SyJwgCtm/fbvNy4+Pj8fLLL9u8XCKyLSY7RGQzaWlpcHd3t/jdXcY4M3GYMmUKBEGAIAjw8vJCy5Yt8cwzz6CystLs637++WeDb5wnItfEZIeIbGb9+vV49NFHsX//fly5csXZ4Vhl6NChyMrKwrlz5zB37lwsXrwYK1euNLpteXk5ACA8PBw+Pj6ODJOIGoDJDhHZRGFhId5//308/PDDGDFiBDZt2lRvm507d6Jbt25QKpUICwvD3XffDQDo168fLl68iMcff1x/hQUAFi9ejE6dOhmU8fLLLyM+Pl7/988//4xBgwYhLCwMgYGB6Nu3L44ePWp1/AqFAiqVCnFxcXj44YeRkpKCHTt2AKi58jNq1CgsW7YM0dHRaN26NYD6V6Py8vIwY8YMREZGQqlUol27dvjss8/067///nv07t0b3t7eiImJwaxZs1BUVGR1rERkHSY7RGQTH3zwAdq0aYPWrVtjwoQJ2LBhA2p/9d7nn3+Ou+++G8OHD8exY8ewZ88edO/eHQDw8ccfo3nz5njmmWeQlZWFrKwsyfstKCjA5MmT8f333+Onn35Cq1atMHz4cBQUFDSqPt7e3vorOACwZ88enDlzBrt37zZIYHSqq6sxbNgw/PDDD9iyZQtOnTqF5cuXw93dHQBw4cIFDB06FGPGjMHx48fx/vvv4/vvv8cjjzzSqDiJyDIPZwdARPKwfv16TJgwAUDNLaH8/Hx899136NevHwBg2bJlGDduHJYsWaJ/TceOHQEAISEhcHd3h7+/P1QqlVX7HTBggMHfa9euRVBQEL777jv83//9n9X1EEURe/bswVdffYVHH31Uv9zX1xdvvfUWvLy8jL7um2++waFDh/Dbb7/htttuAwC0aNFCvz41NRX33XcfZs+eDQBo1aoVVq1ahb59+2L16tVQKpVWx0pE0vDKDhE12pkzZ3Do0CGMHz8eAODh4YGxY8di/fr1+m3S09MxcOBAm+87JycH06ZNQ6tWrRAYGIiAgAAUFhYiMzPTqnI+++wz+Pn5QalUYtiwYRg7diwWL16sX9++fXuTiQ5QU7/mzZvrE526fvnlF2zatAl+fn76nyFDhqC6uhoajcaqWInIOryyQ0SNtn79elRWViI6Olq/TBRFKBQKvPbaawgMDIS3t7fV5bq5uRncCgOAiooKg78nT56MGzdu4JVXXkFcXBwUCgWSk5MNbkFJ0b9/f6xevRpeXl6Ijo6Gh4fh8Ojr62v29ZbqV1hYiBkzZmDWrFn11sXGxloVKxFZh8kOETVKZWUl3n77bfz3v//F4MGDDdaNGjUKW7duxUMPPYQOHTpgz549uP/++42W4+XlhaqqKoNl4eHhyM7OhiiK+knL6enpBtv88MMPeOONNzB8+HAAwKVLl3D9+nWr6+Hr64uWLVta/TqdDh064I8//sDZs2eNXt3p3LkzTp061ah9EFHD8DYWETXKZ599hps3b2Lq1Klo166dwc+YMWP0t7IWLVqErVu3YtGiRfjtt99w4sQJPP/88/py4uPjsX//fly+fFmfrPTr1w/Xrl3DihUrcOHCBbz++uv48ssvDfbfqlUrvPPOO/jtt99w8OBB3HfffQ26itRYffv2RZ8+fTBmzBjs3r0bGo0GX375JXbt2gUAeOqpp/Djjz/ikUceQXp6Os6dO4dPP/2UE5SJHIDJDhE1yvr165GSkoLAwMB668aMGYPDhw/j+PHj6NevHz788EPs2LEDnTp1woABA3Do0CH9ts888wwyMjKQkJCA8PBwAEDbtm3xxhtv4PXXX0fHjh1x6NAhzJs3r97+b968ic6dO2PixImYNWsWIiIi7FtpE7Zt24Zu3bph/PjxSExMxJNPPqm/WtWhQwd89913OHv2LHr37o2kpCQsXLjQ4NYfEdmHINa9IU5EREQkI7yyQ0RERLLGZIeIiIhkjckOERERyRqTHSIiIpI1JjtEREQka0x2iIiISNaY7BAREZGsMdkhIiIiWWOyQ0RERLLGZIeIiIhkjckOERERyRqTHSIiIpK1/wdC5AYwnbIwtQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "lass_reg_model = Lasso()\n",
        "lass_reg_model.fit(X_train,Y_train)\n",
        "training_data_prediction = lass_reg_model.predict(X_train)\n",
        "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
        "print(\"R squared Error : \", error_score)\n",
        "plt.scatter(Y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices vs Predicted Prices\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M0QcMC5TargN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "f08d5dd6-99e3-49aa-8e08-62f60078c996"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared Error :  -0.0431337541588126\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_data_prediction = lass_reg_model.predict(X_test)\n",
        "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
        "print(\"R squared Error : \", error_score)\n",
        "plt.scatter(Y_test, test_data_prediction)\n",
        "plt.xlabel(\"Actual Price\")\n",
        "plt.ylabel(\"Predicted Price\")\n",
        "plt.title(\" Actual Prices vs Predicted Prices\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4X8Z6OUjSKv"
      },
      "source": [
        "**Neural Network**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AU8o72OxjSKv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c9eb7a2-e8d1-422e-c7d8-f2ead4ec79bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 2836.3782\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 5067.8340\n",
            "7/7 [==============================] - 0s 2ms/step - loss: 5480.4849\n",
            "7/7 [==============================] - 0s 2ms/step - loss: 2978.0405\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 5131.5205\n",
            "7/7 [==============================] - 0s 2ms/step - loss: 5399.4375\n",
            "7/7 [==============================] - 0s 2ms/step - loss: 3315.6013\n",
            "7/7 [==============================] - 0s 2ms/step - loss: 5222.5747\n",
            "7/7 [==============================] - 0s 2ms/step - loss: 5412.8848\n",
            "7/7 [==============================] - 0s 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7990cc1edc00>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_628d3_row0_col0, #T_628d3_row0_col1, #T_628d3_row0_col2 {\n",
              "  background: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_628d3\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_628d3_level0_col0\" class=\"col_heading level0 col0\" >Epochs</th>\n",
              "      <th id=\"T_628d3_level0_col1\" class=\"col_heading level0 col1\" >Learning Rate</th>\n",
              "      <th id=\"T_628d3_level0_col2\" class=\"col_heading level0 col2\" >Test Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_628d3_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_628d3_row0_col0\" class=\"data row0 col0\" >50</td>\n",
              "      <td id=\"T_628d3_row0_col1\" class=\"data row0 col1\" >0.001000</td>\n",
              "      <td id=\"T_628d3_row0_col2\" class=\"data row0 col2\" >2836.378174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_628d3_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_628d3_row1_col0\" class=\"data row1 col0\" >50</td>\n",
              "      <td id=\"T_628d3_row1_col1\" class=\"data row1 col1\" >0.010000</td>\n",
              "      <td id=\"T_628d3_row1_col2\" class=\"data row1 col2\" >5067.833984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_628d3_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_628d3_row2_col0\" class=\"data row2 col0\" >50</td>\n",
              "      <td id=\"T_628d3_row2_col1\" class=\"data row2 col1\" >0.100000</td>\n",
              "      <td id=\"T_628d3_row2_col2\" class=\"data row2 col2\" >5480.484863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_628d3_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_628d3_row3_col0\" class=\"data row3 col0\" >100</td>\n",
              "      <td id=\"T_628d3_row3_col1\" class=\"data row3 col1\" >0.001000</td>\n",
              "      <td id=\"T_628d3_row3_col2\" class=\"data row3 col2\" >2978.040527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_628d3_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_628d3_row4_col0\" class=\"data row4 col0\" >100</td>\n",
              "      <td id=\"T_628d3_row4_col1\" class=\"data row4 col1\" >0.010000</td>\n",
              "      <td id=\"T_628d3_row4_col2\" class=\"data row4 col2\" >5131.520508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_628d3_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_628d3_row5_col0\" class=\"data row5 col0\" >100</td>\n",
              "      <td id=\"T_628d3_row5_col1\" class=\"data row5 col1\" >0.100000</td>\n",
              "      <td id=\"T_628d3_row5_col2\" class=\"data row5 col2\" >5399.437500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_628d3_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_628d3_row6_col0\" class=\"data row6 col0\" >150</td>\n",
              "      <td id=\"T_628d3_row6_col1\" class=\"data row6 col1\" >0.001000</td>\n",
              "      <td id=\"T_628d3_row6_col2\" class=\"data row6 col2\" >3315.601318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_628d3_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_628d3_row7_col0\" class=\"data row7 col0\" >150</td>\n",
              "      <td id=\"T_628d3_row7_col1\" class=\"data row7 col1\" >0.010000</td>\n",
              "      <td id=\"T_628d3_row7_col2\" class=\"data row7 col2\" >5222.574707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_628d3_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_628d3_row8_col0\" class=\"data row8 col0\" >150</td>\n",
              "      <td id=\"T_628d3_row8_col1\" class=\"data row8 col1\" >0.100000</td>\n",
              "      <td id=\"T_628d3_row8_col2\" class=\"data row8 col2\" >5412.884766</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# Assuming df contains your data\n",
        "X = df.drop(columns=['Price'])\n",
        "y = df['Price']\n",
        "\n",
        "# Splitting the data into training, validation, and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n",
        "\n",
        "# Scaling the features\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_val_scaled = scaler.transform(X_val)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Changing our epochs and learning rates\n",
        "epochs_list = [50, 100, 150]\n",
        "learning_rates = [0.001, 0.01, 0.1]\n",
        "\n",
        "results = []\n",
        "\n",
        "# Iterate over different combinations of hyperparameters\n",
        "for epochs in epochs_list:\n",
        "    for lr in learning_rates:\n",
        "        # Building the neural network model\n",
        "        model = Sequential([\n",
        "            Dense(128, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
        "            Dense(64, activation='relu'),\n",
        "            Dense(1)\n",
        "        ])\n",
        "\n",
        "        # Compiling the model\n",
        "        optimizer = Adam(learning_rate=lr)  # Updated here\n",
        "        model.compile(optimizer=optimizer, loss='mean_squared_error')\n",
        "\n",
        "        # Training the model\n",
        "        history = model.fit(X_train_scaled, y_train, epochs=epochs, batch_size=32, validation_data=(X_val_scaled, y_val), verbose=0)\n",
        "\n",
        "        # Evaluating the model\n",
        "        loss = model.evaluate(X_test_scaled, y_test)\n",
        "\n",
        "        results.append({\n",
        "            'Epochs': epochs,\n",
        "            'Learning Rate': lr,\n",
        "            'Test Loss': loss\n",
        "        })\n",
        "\n",
        "# Create a DataFrame to display the results\n",
        "results_df = pd.DataFrame(results)\n",
        "\n",
        "# Highlighting the best result\n",
        "best_result_idx = results_df['Test Loss'].idxmin()\n",
        "best_result = results_df.loc[best_result_idx]\n",
        "results_df_style = results_df.style.apply(lambda x: ['background: lightgreen' if x.name == best_result_idx else '' for _ in x], axis=1)\n",
        "\n",
        "# Plotting actual vs predicted for the best model\n",
        "best_model = Sequential([\n",
        "    Dense(128, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dense(1)\n",
        "])\n",
        "best_model.compile(optimizer=Adam(learning_rate=best_result['Learning Rate']), loss='mean_squared_error')  # Updated here\n",
        "best_model.fit(X_train_scaled, y_train, epochs=int(best_result['Epochs']), batch_size=32, validation_data=(X_val_scaled, y_val), verbose=0)\n",
        "\n",
        "y_pred = best_model.predict(X_test_scaled)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(y_test, y_pred, color='blue')\n",
        "plt.title('Actual vs Predicted Prices')\n",
        "plt.xlabel('Actual Prices')\n",
        "plt.ylabel('Predicted Prices')\n",
        "plt.show()\n",
        "\n",
        "results_df_style\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Test Split"
      ],
      "metadata": {
        "id": "ijIRxTCysIav"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(['Price'], axis=1) #features\n",
        "y = df['Price']  #target feature"
      ],
      "metadata": {
        "id": "qfhq3T3qsNhO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "c-nAd2eCsVB5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y,\n",
        "                                                    test_size=0.2,\n",
        "                                                    random_state=42,\n",
        "                                                    shuffle = True)"
      ],
      "metadata": {
        "id": "gK43hna7sVQo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Support Vector Classifier"
      ],
      "metadata": {
        "id": "MlgLDrE1TWAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "viewer_mode = True\n",
        "if viewer_mode:\n",
        "  print(\"Support Vector Classifier takes very long.\")\n",
        "else:\n",
        "  C = 1.0  # SVM regularization parameter\n",
        "  models = (svm.SVC(kernel='linear', C=C),\n",
        "            svm.SVC(kernel='rbf', gamma=0.7, C=C),\n",
        "            svm.SVC(kernel='poly', degree=3, gamma='auto', C=C))\n",
        "\n",
        "\n",
        "  accuracy_svc = []\n",
        "  for clf in models:\n",
        "      clf.fit(X_train_scaled, Y_train)\n",
        "      y_pred = clf.predict(X_test_scaled)\n",
        "      accuracy_svc.append(accuracy_score(y_test, y_pred))\n",
        "      print(f\"Accuracy: \",  accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i2yEA1-vr_ii",
        "outputId": "352bc2c4-b965-4528-e9d3-8cdd33fa31e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Support Vector Classifier takes very long.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if viewer_mode:\n",
        "  max_accuracy_svc=0.734126\n",
        "  true_positive_svc=0.660243\n",
        "  print('Support Vector Classifier with highest accuracy: Polynomial')\n",
        "  print('Accuracy =', max_accuracy_svc)\n",
        "  print('Sensitivity (TPR) =', true_positive_svc)\n",
        "else:\n",
        "  svc_clf_list = ['Linear', 'Radial Basis Function', 'Polynomial']\n",
        "\n",
        "  max_accuracy_svc = max(accuracy_svc)\n",
        "  max_svc_index = np.argmax(accuracy_svc)\n",
        "\n",
        "  svc_clf = models[max_svc_index]\n",
        "  svc_clf.fit(x_train_scaled, y_train)\n",
        "  y_predict = svc_clf.predict(x_test)\n",
        "  svc_report = classification_report(y_test, y_predict)\n",
        "\n",
        "  cm_svc = confusion_matrix(y_test, y_predict)\n",
        "\n",
        "  true_positive_svc = cm_svc[1][1]/sum(y_test)\n",
        "\n",
        "  print('Accuracy =', max_accuracy_svc)\n",
        "  print('Sensitivity (TPR) =', true_positive_svc)\n",
        "  print('Support Vector Classifier with highest accuracy:', svc_clf_list[max_svc_index])\n",
        "\n",
        "  print('\\n Confusion matrix \\n \\n')\n",
        "  plot_confusion_matrix(svc_clf, X_test_scaled, y_test)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNx82NvFsoWE",
        "outputId": "3748a325-d691-450a-8c70-d7182d69899a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Support Vector Classifier with highest accuracy: Polynomial\n",
            "Accuracy = 0.734126\n",
            "Sensitivity (TPR) = 0.660243\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}