CarPricePrediction.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "%matplotlib inline\n",
        "mpl.style.use('ggplot')"
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      "source": [
        "cpp=pd.read_csv('quikr_car.csv')"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>company</th>\n",
              "      <th>year</th>\n",
              "      <th>Price</th>\n",
              "      <th>kms_driven</th>\n",
              "      <th>fuel_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hyundai Santro Xing XO eRLX Euro III</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2007</td>\n",
              "      <td>80,000</td>\n",
              "      <td>45,000 kms</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mahindra Jeep CL550 MDI</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2006</td>\n",
              "      <td>4,25,000</td>\n",
              "      <td>40 kms</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Maruti Suzuki Alto 800 Vxi</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>2018</td>\n",
              "      <td>Ask For Price</td>\n",
              "      <td>22,000 kms</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hyundai Grand i10 Magna 1.2 Kappa VTVT</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2014</td>\n",
              "      <td>3,25,000</td>\n",
              "      <td>28,000 kms</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ford EcoSport Titanium 1.5L TDCi</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2014</td>\n",
              "      <td>5,75,000</td>\n",
              "      <td>36,000 kms</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                                     name   company  ...  kms_driven fuel_type\n",
              "0    Hyundai Santro Xing XO eRLX Euro III   Hyundai  ...  45,000 kms    Petrol\n",
              "1                 Mahindra Jeep CL550 MDI  Mahindra  ...      40 kms    Diesel\n",
              "2              Maruti Suzuki Alto 800 Vxi    Maruti  ...  22,000 kms    Petrol\n",
              "3  Hyundai Grand i10 Magna 1.2 Kappa VTVT   Hyundai  ...  28,000 kms    Petrol\n",
              "4        Ford EcoSport Titanium 1.5L TDCi      Ford  ...  36,000 kms    Diesel\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
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        "cpp.head()"
      ]
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      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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        "id": "iILZmD6gL5Qx",
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(892, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 4
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      "source": [
        "cpp.shape"
      ]
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    {
      "cell_type": "code",
      "execution_count": 5,
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        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 892 entries, 0 to 891\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   name        892 non-null    object\n",
            " 1   company     892 non-null    object\n",
            " 2   year        892 non-null    object\n",
            " 3   Price       892 non-null    object\n",
            " 4   kms_driven  840 non-null    object\n",
            " 5   fuel_type   837 non-null    object\n",
            "dtypes: object(6)\n",
            "memory usage: 41.9+ KB\n"
          ]
        }
      ],
      "source": [
        "cpp.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dgAojfGML5Qy"
      },
      "source": [
        "##### Creating backup copy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "A18wDoIyL5Q1"
      },
      "outputs": [],
      "source": [
        "backup=cpp.copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cyZRhv7CL5Q2"
      },
      "source": [
        "## Quality\n",
        "\n",
        "- names are pretty inconsistent\n",
        "- names have company names attached to it\n",
        "- some names are spam like 'Maruti Ertiga showroom condition with' and 'Well mentained Tata Sumo'\n",
        "- company: many of the names are not of any company like 'Used', 'URJENT', and so on.\n",
        "- year has many non-year values\n",
        "- year is in object. Change to integer\n",
        "- Price has Ask for Price\n",
        "- Price has commas in its prices and is in object\n",
        "- kms_driven has object values with kms at last.\n",
        "- It has nan values and two rows have 'Petrol' in them\n",
        "- fuel_type has nan values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_jW3u2vxL5Q3"
      },
      "source": [
        "## Cleaning Data "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9bnwmiRuL5Q4"
      },
      "source": [
        "#### year has many non-year values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "4Q2gitKvL5Q4"
      },
      "outputs": [],
      "source": [
        "cpp=cpp[cpp['year'].str.isnumeric()]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BRmynRkHL5Q5"
      },
      "source": [
        "#### year is in object. Change to integer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "g-AXlnrdL5Q5"
      },
      "outputs": [],
      "source": [
        "cpp['year']=cpp['year'].astype(int)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iP21QT1YL5Q6"
      },
      "source": [
        "#### Price has Ask for Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "8AL_lVMYL5Q6"
      },
      "outputs": [],
      "source": [
        "cpp=cpp[cpp['Price']!='Ask For Price']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYN2m52jL5Q6"
      },
      "source": [
        "#### Price has commas in its prices and is in object"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "fZhPrcFqL5Q7"
      },
      "outputs": [],
      "source": [
        "cpp['Price']=cpp['Price'].str.replace(',','').astype(int)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BK5akWPJL5Q8"
      },
      "source": [
        "####  kms_driven has object values with kms at last."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "3DcxBXwlL5Q8"
      },
      "outputs": [],
      "source": [
        "cpp['kms_driven']=cpp['kms_driven'].str.split().str.get(0).str.replace(',','')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9s5gxGy2L5Q9"
      },
      "source": [
        "#### It has nan values and two rows have 'Petrol' in them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "POkjDRqcL5Q9"
      },
      "outputs": [],
      "source": [
        "cpp=cpp[cpp['kms_driven'].str.isnumeric()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "g5iNlRp8L5Q-"
      },
      "outputs": [],
      "source": [
        "cpp['kms_driven']=cpp['kms_driven'].astype(int)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aLvzoMPRL5Q-"
      },
      "source": [
        "#### fuel_type has nan values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "UtyPJWJJL5Q_"
      },
      "outputs": [],
      "source": [
        "cpp=cpp[~cpp['fuel_type'].isna()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ep4ossu2L5Q_",
        "outputId": "9aa295d9-15b2-41cf-c500-41ceba7a58eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(816, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "cpp.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nm-30ztRL5RA"
      },
      "source": [
        "### name and company had spammed data...but with the previous cleaning, those rows got removed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QCYKbfOhL5RA"
      },
      "source": [
        "#### Company does not need any cleaning now. Changing car names. Keeping only the first three words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "hTEcLYnIL5RA"
      },
      "outputs": [],
      "source": [
        "cpp['name']=cpp['name'].str.split().str.slice(start=0,stop=3).str.join(' ')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIZ9hCGUL5RB"
      },
      "source": [
        "#### Resetting the index of the final cleaned data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ea9RbgAML5RB"
      },
      "outputs": [],
      "source": [
        "cpp=cpp.reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnm2IbRTL5RB"
      },
      "source": [
        "## Cleaned Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "qIDmTFfWL5RC",
        "outputId": "e3df922b-1044-4126-c361-9f002d8c8203"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hyundai Santro Xing</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2007</td>\n",
              "      <td>80000</td>\n",
              "      <td>45000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mahindra Jeep CL550</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2006</td>\n",
              "      <td>425000</td>\n",
              "      <td>40</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hyundai Grand i10</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2014</td>\n",
              "      <td>325000</td>\n",
              "      <td>28000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ford EcoSport Titanium</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2014</td>\n",
              "      <td>575000</td>\n",
              "      <td>36000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ford Figo</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2012</td>\n",
              "      <td>175000</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>811</th>\n",
              "      <td>Maruti Suzuki Ritz</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>2011</td>\n",
              "      <td>270000</td>\n",
              "      <td>50000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>812</th>\n",
              "      <td>Tata Indica V2</td>\n",
              "      <td>Tata</td>\n",
              "      <td>2009</td>\n",
              "      <td>110000</td>\n",
              "      <td>30000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>Toyota Corolla Altis</td>\n",
              "      <td>Toyota</td>\n",
              "      <td>2009</td>\n",
              "      <td>300000</td>\n",
              "      <td>132000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>814</th>\n",
              "      <td>Tata Zest XM</td>\n",
              "      <td>Tata</td>\n",
              "      <td>2018</td>\n",
              "      <td>260000</td>\n",
              "      <td>27000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>815</th>\n",
              "      <td>Mahindra Quanto C8</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2013</td>\n",
              "      <td>390000</td>\n",
              "      <td>40000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>816 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc31610d-2162-4570-8989-9d8ac7c82715')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fc31610d-2162-4570-8989-9d8ac7c82715 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-fc31610d-2162-4570-8989-9d8ac7c82715');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                       name   company  year   Price  kms_driven fuel_type\n",
              "0       Hyundai Santro Xing   Hyundai  2007   80000       45000    Petrol\n",
              "1       Mahindra Jeep CL550  Mahindra  2006  425000          40    Diesel\n",
              "2         Hyundai Grand i10   Hyundai  2014  325000       28000    Petrol\n",
              "3    Ford EcoSport Titanium      Ford  2014  575000       36000    Diesel\n",
              "4                 Ford Figo      Ford  2012  175000       41000    Diesel\n",
              "..                      ...       ...   ...     ...         ...       ...\n",
              "811      Maruti Suzuki Ritz    Maruti  2011  270000       50000    Petrol\n",
              "812          Tata Indica V2      Tata  2009  110000       30000    Diesel\n",
              "813    Toyota Corolla Altis    Toyota  2009  300000      132000    Petrol\n",
              "814            Tata Zest XM      Tata  2018  260000       27000    Diesel\n",
              "815      Mahindra Quanto C8  Mahindra  2013  390000       40000    Diesel\n",
              "\n",
              "[816 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "cpp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "ttDIvJ5JL5RC"
      },
      "outputs": [],
      "source": [
        "cpp.to_csv('Cleaned_CPP_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LkKBbsrSL5RC",
        "outputId": "8f0035c3-dfd0-4826-cd3b-c9ec18cc3d25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 815 entries, 0 to 815\n",
            "Data columns (total 6 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   name        815 non-null    object\n",
            " 1   company     815 non-null    object\n",
            " 2   year        815 non-null    int64 \n",
            " 3   Price       815 non-null    int64 \n",
            " 4   kms_driven  815 non-null    int64 \n",
            " 5   fuel_type   815 non-null    object\n",
            "dtypes: int64(3), object(3)\n",
            "memory usage: 76.9+ KB\n"
          ]
        }
      ],
      "source": [
        "cpp.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "R__B2f2VL5RD",
        "outputId": "af8657b9-7e77-4e3c-c1c6-2e4a6bab486d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ceb2fac0-bd63-4f66-b35b-cbac407b978e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>company</th>\n",
              "      <th>year</th>\n",
              "      <th>Price</th>\n",
              "      <th>kms_driven</th>\n",
              "      <th>fuel_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>815</td>\n",
              "      <td>815</td>\n",
              "      <td>815.000000</td>\n",
              "      <td>8.150000e+02</td>\n",
              "      <td>815.000000</td>\n",
              "      <td>815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>254</td>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Maruti Suzuki Swift</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>51</td>\n",
              "      <td>221</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2012.442945</td>\n",
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              "      <td>46277.096933</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.005079</td>\n",
              "      <td>3.815888e+05</td>\n",
              "      <td>34318.459638</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1995.000000</td>\n",
              "      <td>3.000000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>1.750000e+05</td>\n",
              "      <td>27000.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2013.000000</td>\n",
              "      <td>2.999990e+05</td>\n",
              "      <td>41000.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>4.900000e+05</td>\n",
              "      <td>56879.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>3.100000e+06</td>\n",
              "      <td>400000.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ceb2fac0-bd63-4f66-b35b-cbac407b978e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ceb2fac0-bd63-4f66-b35b-cbac407b978e 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-ceb2fac0-bd63-4f66-b35b-cbac407b978e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                       name company  ...     kms_driven  fuel_type\n",
              "count                   815     815  ...     815.000000        815\n",
              "unique                  254      25  ...            NaN          3\n",
              "top     Maruti Suzuki Swift  Maruti  ...            NaN     Petrol\n",
              "freq                     51     221  ...            NaN        428\n",
              "mean                    NaN     NaN  ...   46277.096933        NaN\n",
              "std                     NaN     NaN  ...   34318.459638        NaN\n",
              "min                     NaN     NaN  ...       0.000000        NaN\n",
              "25%                     NaN     NaN  ...   27000.000000        NaN\n",
              "50%                     NaN     NaN  ...   41000.000000        NaN\n",
              "75%                     NaN     NaN  ...   56879.000000        NaN\n",
              "max                     NaN     NaN  ...  400000.000000        NaN\n",
              "\n",
              "[11 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "cpp.describe(include='all')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gkr1JBHWL5RE"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "oRZmUpmqL5RF"
      },
      "outputs": [],
      "source": [
        "cpp=cpp[cpp['Price']<6000000]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKXsWZTZL5RG"
      },
      "source": [
        "### Checking relationship of Company with Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBXVjWAaL5RG",
        "outputId": "4abc5ffb-03de-4791-e833-13aeebb40bef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Hyundai', 'Mahindra', 'Ford', 'Maruti', 'Skoda', 'Audi', 'Toyota',\n",
              "       'Renault', 'Honda', 'Datsun', 'Mitsubishi', 'Tata', 'Volkswagen',\n",
              "       'Chevrolet', 'Mini', 'BMW', 'Nissan', 'Hindustan', 'Fiat', 'Force',\n",
              "       'Mercedes', 'Land', 'Jaguar', 'Jeep', 'Volvo'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "cpp['company'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "FQyQkLsQL5RH"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "G-aqKyc8L5RH",
        "outputId": "6653f8aa-9932-4673-d073-000e79822cfa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.subplots(figsize=(15,7))\n",
        "ax=sns.boxplot(x='company',y='Price',data=cpp)\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHiAqGSXL5RI"
      },
      "source": [
        "### Checking relationship of Year with Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "id": "rWOKvSbBL5RI",
        "outputId": "e5aad56e-5c1a-4b4b-a953-e44d01576c3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 9.3% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 6.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 10.6% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:1296: UserWarning: 5.5% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.subplots(figsize=(20,10))\n",
        "ax=sns.swarmplot(x='year',y='Price',data=cpp)\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=40,ha='right')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0M6KocfXL5RJ"
      },
      "source": [
        "### Checking relationship of kms_driven with Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "vofVO4OGL5RJ",
        "outputId": "a59c8730-46c2-45ce-e8b8-6165e1cd5efa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fa182fb9d90>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 756x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.relplot(x='kms_driven',y='Price',data=cpp,height=7,aspect=1.5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LOrXg_5tL5RJ"
      },
      "source": [
        "### Checking relationship of Fuel Type with Price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "RMscFKoaL5RK",
        "outputId": "99657e11-ae5a-444e-9d00-14611b14d1bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa183366e90>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.subplots(figsize=(14,7))\n",
        "sns.boxplot(x='fuel_type',y='Price',data=cpp)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XE1NUH5hL5RK"
      },
      "source": [
        "### Relationship of Price with FuelType, Year and Company mixed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "ajkgM59TL5RK",
        "outputId": "88af99cb-80c6-4363-b1e9-687d1ededde3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fa1831166d0>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1075x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "ax=sns.relplot(x='company',y='Price',data=cpp,hue='fuel_type',size='year',height=7,aspect=2)\n",
        "ax.set_xticklabels(rotation=40,ha='right')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DSHuw2y8L5RL"
      },
      "source": [
        "### Extracting Training Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "lVJzhEgbL5RL"
      },
      "outputs": [],
      "source": [
        "X=cpp[['name','company','year','kms_driven','fuel_type']]\n",
        "y=cpp['Price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "P_jRiTE7L5RM",
        "outputId": "7f9a87b3-2e6b-43c8-d929-4c67aae78a25"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e196723e-e013-4121-80fb-03d596f7f21e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>company</th>\n",
              "      <th>year</th>\n",
              "      <th>kms_driven</th>\n",
              "      <th>fuel_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Hyundai Santro Xing</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2007</td>\n",
              "      <td>45000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mahindra Jeep CL550</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2006</td>\n",
              "      <td>40</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Hyundai Grand i10</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>2014</td>\n",
              "      <td>28000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ford EcoSport Titanium</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2014</td>\n",
              "      <td>36000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Ford Figo</td>\n",
              "      <td>Ford</td>\n",
              "      <td>2012</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>811</th>\n",
              "      <td>Maruti Suzuki Ritz</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>2011</td>\n",
              "      <td>50000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>812</th>\n",
              "      <td>Tata Indica V2</td>\n",
              "      <td>Tata</td>\n",
              "      <td>2009</td>\n",
              "      <td>30000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>813</th>\n",
              "      <td>Toyota Corolla Altis</td>\n",
              "      <td>Toyota</td>\n",
              "      <td>2009</td>\n",
              "      <td>132000</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>814</th>\n",
              "      <td>Tata Zest XM</td>\n",
              "      <td>Tata</td>\n",
              "      <td>2018</td>\n",
              "      <td>27000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>815</th>\n",
              "      <td>Mahindra Quanto C8</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>2013</td>\n",
              "      <td>40000</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>815 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e196723e-e013-4121-80fb-03d596f7f21e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e196723e-e013-4121-80fb-03d596f7f21e 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-e196723e-e013-4121-80fb-03d596f7f21e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                       name   company  year  kms_driven fuel_type\n",
              "0       Hyundai Santro Xing   Hyundai  2007       45000    Petrol\n",
              "1       Mahindra Jeep CL550  Mahindra  2006          40    Diesel\n",
              "2         Hyundai Grand i10   Hyundai  2014       28000    Petrol\n",
              "3    Ford EcoSport Titanium      Ford  2014       36000    Diesel\n",
              "4                 Ford Figo      Ford  2012       41000    Diesel\n",
              "..                      ...       ...   ...         ...       ...\n",
              "811      Maruti Suzuki Ritz    Maruti  2011       50000    Petrol\n",
              "812          Tata Indica V2      Tata  2009       30000    Diesel\n",
              "813    Toyota Corolla Altis    Toyota  2009      132000    Petrol\n",
              "814            Tata Zest XM      Tata  2018       27000    Diesel\n",
              "815      Mahindra Quanto C8  Mahindra  2013       40000    Diesel\n",
              "\n",
              "[815 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZQ4HZJUPHsP",
        "outputId": "5cb3a4e2-82e4-4f05-a2a2-6f10f70d7524"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(815, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_OAsrB3wPM_-",
        "outputId": "7faa695e-c28a-498c-b782-4eadf1847800"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       80000\n",
              "1      425000\n",
              "2      325000\n",
              "3      575000\n",
              "4      175000\n",
              "        ...  \n",
              "811    270000\n",
              "812    110000\n",
              "813    300000\n",
              "814    260000\n",
              "815    390000\n",
              "Name: Price, Length: 815, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOI0xgLxL5RM",
        "outputId": "e4f2b2bd-b44f-46fa-8392-5f6b75aa3a2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(815,)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RcCSBtecL5RN"
      },
      "source": [
        "### Applying Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "MF2xDyJdL5RN"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "tljj1zxAL5RN"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "6Aug5ortL5RO"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjN8hgpJL5RO"
      },
      "source": [
        "#### Creating an OneHotEncoder object to contain all the possible categories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JyDKIAaAL5RO",
        "outputId": "734b356d-5a9a-4dae-d84d-52cf1fee6057"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneHotEncoder()"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "one=OneHotEncoder()\n",
        "one.fit(X[['name','company','fuel_type']])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WrFOfx-hL5RP"
      },
      "source": [
        "#### Creating a column transformer to transform categorical columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "7OIP_o8EL5RP"
      },
      "outputs": [],
      "source": [
        "column_trans=make_column_transformer((OneHotEncoder(categories=one.categories_),['name','company','fuel_type']),\n",
        "                                    remainder='passthrough')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m3H8QXH8L5RP"
      },
      "source": [
        "#### Linear Regression Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "rBUPW4yFL5RQ"
      },
      "outputs": [],
      "source": [
        "linreg=LinearRegression()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Ci1Cn1IL5RQ"
      },
      "source": [
        "#### Making a pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "Kz3xDgWkL5RQ"
      },
      "outputs": [],
      "source": [
        "pipe=make_pipeline(column_trans,linreg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wR8_MmAnL5RQ"
      },
      "source": [
        "#### Fitting the  model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vsQT-tzsL5RR",
        "outputId": "7cf59abe-8bcd-46eb-e5e4-9fb974582dd1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('onehotencoder',\n",
              "                                                  OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
              "       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
              "       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
              "       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n",
              "                                                                            array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n",
              "       'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n",
              "       'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n",
              "       'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n",
              "      dtype=object),\n",
              "                                                                            array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n",
              "                                                  ['name', 'company',\n",
              "                                                   'fuel_type'])])),\n",
              "                ('linearregression', LinearRegression())])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "pipe.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "eyY4Y_VUL5RR"
      },
      "outputs": [],
      "source": [
        "y_pred=pipe.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QzGIfBoSL5RR"
      },
      "source": [
        "#### Checking R2 Score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2XL07XIvL5RS",
        "outputId": "68db670b-2c8e-4235-a774-470b00d5adf0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5477544080549159"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "r2_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OH1cWPd3L5RS"
      },
      "source": [
        "#### Finding the model with a random state of TrainTestSplit where the model was found to give almost 0.92 as r2_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "jwMHCu7CL5RS"
      },
      "outputs": [],
      "source": [
        "scores=[]\n",
        "for i in range(1000):\n",
        "    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=i)\n",
        "    linreg=LinearRegression()\n",
        "    pipe=make_pipeline(column_trans,linreg)\n",
        "    pipe.fit(X_train,y_train)\n",
        "    y_pred=pipe.predict(X_test)\n",
        "    scores.append(r2_score(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "haE9MwpeL5RT",
        "outputId": "26759710-d25a-4911-9fce-cfee73730e32"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "655"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "np.argmax(scores)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDjJhZseL5RT",
        "outputId": "8dbe7441-d92b-4c3d-87f4-db70bfc55c0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.920087093218515"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "scores[np.argmax(scores)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwWBeqCGL5RU",
        "outputId": "30d49419-0782-4c8c-dfc7-2fda68880820"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([400642.51767152])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "pipe.predict(pd.DataFrame(columns=X_test.columns,data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nIGor0HL5RU"
      },
      "source": [
        "#### The best model is found at a certain random state "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wn7avHUHL5RU",
        "outputId": "0c8611ce-6464-471b-8688-f574e783ca20"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.920087093218515"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=np.argmax(scores))\n",
        "linreg=LinearRegression()\n",
        "pipe=make_pipeline(column_trans,linreg)\n",
        "pipe.fit(X_train,y_train)\n",
        "y_pred=pipe.predict(X_test)\n",
        "r2_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "neCS-pgFL5RV"
      },
      "outputs": [],
      "source": [
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "BbsHm1OKL5RV"
      },
      "outputs": [],
      "source": [
        "pickle.dump(pipe,open('LinearRegressionModel.pkl','wb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uetGLntAL5RV",
        "outputId": "4dcf3a99-23a2-46fe-c25a-a2133d05b2ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([416107.38610967])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "pipe.predict(pd.DataFrame(columns=['name','company','year','kms_driven','fuel_type'],data=np.array(['Maruti Suzuki Swift','Maruti',2019,100,'Petrol']).reshape(1,5)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgWaDs9pL5RW",
        "outputId": "0a2c93cf-7b23-46bf-92e7-d87a93b6d857"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n",
              "       'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n",
              "       'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n",
              "       'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat Diesel',\n",
              "       'Chevrolet Beat LS', 'Chevrolet Beat LT', 'Chevrolet Beat PS',\n",
              "       'Chevrolet Cruze LTZ', 'Chevrolet Enjoy', 'Chevrolet Enjoy 1.4',\n",
              "       'Chevrolet Sail 1.2', 'Chevrolet Sail UVA', 'Chevrolet Spark',\n",
              "       'Chevrolet Spark 1.0', 'Chevrolet Spark LS', 'Chevrolet Spark LT',\n",
              "       'Chevrolet Tavera LS', 'Chevrolet Tavera Neo', 'Datsun GO T',\n",
              "       'Datsun Go Plus', 'Datsun Redi GO', 'Fiat Linea Emotion',\n",
              "       'Fiat Petra ELX', 'Fiat Punto Emotion', 'Force Motors Force',\n",
              "       'Force Motors One', 'Ford EcoSport', 'Ford EcoSport Ambiente',\n",
              "       'Ford EcoSport Titanium', 'Ford EcoSport Trend',\n",
              "       'Ford Endeavor 4x4', 'Ford Fiesta', 'Ford Fiesta SXi', 'Ford Figo',\n",
              "       'Ford Figo Diesel', 'Ford Figo Duratorq', 'Ford Figo Petrol',\n",
              "       'Ford Fusion 1.4', 'Ford Ikon 1.3', 'Ford Ikon 1.6',\n",
              "       'Hindustan Motors Ambassador', 'Honda Accord', 'Honda Amaze',\n",
              "       'Honda Amaze 1.2', 'Honda Amaze 1.5', 'Honda Brio', 'Honda Brio V',\n",
              "       'Honda Brio VX', 'Honda City', 'Honda City 1.5', 'Honda City SV',\n",
              "       'Honda City VX', 'Honda City ZX', 'Honda Jazz S', 'Honda Jazz VX',\n",
              "       'Honda Mobilio', 'Honda Mobilio S', 'Honda WR V', 'Hyundai Accent',\n",
              "       'Hyundai Accent Executive', 'Hyundai Accent GLE',\n",
              "       'Hyundai Accent GLX', 'Hyundai Creta', 'Hyundai Creta 1.6',\n",
              "       'Hyundai Elantra 1.8', 'Hyundai Elantra SX', 'Hyundai Elite i20',\n",
              "       'Hyundai Eon', 'Hyundai Eon D', 'Hyundai Eon Era',\n",
              "       'Hyundai Eon Magna', 'Hyundai Eon Sportz', 'Hyundai Fluidic Verna',\n",
              "       'Hyundai Getz', 'Hyundai Getz GLE', 'Hyundai Getz Prime',\n",
              "       'Hyundai Grand i10', 'Hyundai Santro', 'Hyundai Santro AE',\n",
              "       'Hyundai Santro Xing', 'Hyundai Sonata Transform', 'Hyundai Verna',\n",
              "       'Hyundai Verna 1.4', 'Hyundai Verna 1.6', 'Hyundai Verna Fluidic',\n",
              "       'Hyundai Verna Transform', 'Hyundai Verna VGT',\n",
              "       'Hyundai Xcent Base', 'Hyundai Xcent SX', 'Hyundai i10',\n",
              "       'Hyundai i10 Era', 'Hyundai i10 Magna', 'Hyundai i10 Sportz',\n",
              "       'Hyundai i20', 'Hyundai i20 Active', 'Hyundai i20 Asta',\n",
              "       'Hyundai i20 Magna', 'Hyundai i20 Select', 'Hyundai i20 Sportz',\n",
              "       'Jaguar XE XE', 'Jaguar XF 2.2', 'Jeep Wrangler Unlimited',\n",
              "       'Land Rover Freelander', 'Mahindra Bolero DI',\n",
              "       'Mahindra Bolero Power', 'Mahindra Bolero SLE',\n",
              "       'Mahindra Jeep CL550', 'Mahindra Jeep MM', 'Mahindra KUV100',\n",
              "       'Mahindra KUV100 K8', 'Mahindra Logan', 'Mahindra Logan Diesel',\n",
              "       'Mahindra Quanto C4', 'Mahindra Quanto C8', 'Mahindra Scorpio',\n",
              "       'Mahindra Scorpio 2.6', 'Mahindra Scorpio LX',\n",
              "       'Mahindra Scorpio S10', 'Mahindra Scorpio S4',\n",
              "       'Mahindra Scorpio SLE', 'Mahindra Scorpio SLX',\n",
              "       'Mahindra Scorpio VLX', 'Mahindra Scorpio Vlx',\n",
              "       'Mahindra Scorpio W', 'Mahindra TUV300 T4', 'Mahindra TUV300 T8',\n",
              "       'Mahindra Thar CRDe', 'Mahindra XUV500', 'Mahindra XUV500 W10',\n",
              "       'Mahindra XUV500 W6', 'Mahindra XUV500 W8', 'Mahindra Xylo D2',\n",
              "       'Mahindra Xylo E4', 'Mahindra Xylo E8', 'Maruti Suzuki 800',\n",
              "       'Maruti Suzuki A', 'Maruti Suzuki Alto', 'Maruti Suzuki Baleno',\n",
              "       'Maruti Suzuki Celerio', 'Maruti Suzuki Ciaz',\n",
              "       'Maruti Suzuki Dzire', 'Maruti Suzuki Eeco',\n",
              "       'Maruti Suzuki Ertiga', 'Maruti Suzuki Esteem',\n",
              "       'Maruti Suzuki Estilo', 'Maruti Suzuki Maruti',\n",
              "       'Maruti Suzuki Omni', 'Maruti Suzuki Ritz', 'Maruti Suzuki S',\n",
              "       'Maruti Suzuki SX4', 'Maruti Suzuki Stingray',\n",
              "       'Maruti Suzuki Swift', 'Maruti Suzuki Versa',\n",
              "       'Maruti Suzuki Vitara', 'Maruti Suzuki Wagon', 'Maruti Suzuki Zen',\n",
              "       'Mercedes Benz A', 'Mercedes Benz B', 'Mercedes Benz C',\n",
              "       'Mercedes Benz GLA', 'Mini Cooper S', 'Mitsubishi Lancer 1.8',\n",
              "       'Mitsubishi Pajero Sport', 'Nissan Micra XL', 'Nissan Micra XV',\n",
              "       'Nissan Sunny', 'Nissan Sunny XL', 'Nissan Terrano XL',\n",
              "       'Nissan X Trail', 'Renault Duster', 'Renault Duster 110',\n",
              "       'Renault Duster 110PS', 'Renault Duster 85', 'Renault Duster 85PS',\n",
              "       'Renault Duster RxL', 'Renault Kwid', 'Renault Kwid 1.0',\n",
              "       'Renault Kwid RXT', 'Renault Lodgy 85', 'Renault Scala RxL',\n",
              "       'Skoda Fabia', 'Skoda Fabia 1.2L', 'Skoda Fabia Classic',\n",
              "       'Skoda Laura', 'Skoda Octavia Classic', 'Skoda Rapid Elegance',\n",
              "       'Skoda Superb 1.8', 'Skoda Yeti Ambition', 'Tata Aria Pleasure',\n",
              "       'Tata Bolt XM', 'Tata Indica', 'Tata Indica V2', 'Tata Indica eV2',\n",
              "       'Tata Indigo CS', 'Tata Indigo LS', 'Tata Indigo LX',\n",
              "       'Tata Indigo Marina', 'Tata Indigo eCS', 'Tata Manza',\n",
              "       'Tata Manza Aqua', 'Tata Manza Aura', 'Tata Manza ELAN',\n",
              "       'Tata Nano', 'Tata Nano Cx', 'Tata Nano GenX', 'Tata Nano LX',\n",
              "       'Tata Nano Lx', 'Tata Sumo Gold', 'Tata Sumo Grande',\n",
              "       'Tata Sumo Victa', 'Tata Tiago Revotorq', 'Tata Tiago Revotron',\n",
              "       'Tata Tigor Revotron', 'Tata Venture EX', 'Tata Vista Quadrajet',\n",
              "       'Tata Zest Quadrajet', 'Tata Zest XE', 'Tata Zest XM',\n",
              "       'Toyota Corolla', 'Toyota Corolla Altis', 'Toyota Corolla H2',\n",
              "       'Toyota Etios', 'Toyota Etios G', 'Toyota Etios GD',\n",
              "       'Toyota Etios Liva', 'Toyota Fortuner', 'Toyota Fortuner 3.0',\n",
              "       'Toyota Innova 2.0', 'Toyota Innova 2.5', 'Toyota Qualis',\n",
              "       'Volkswagen Jetta Comfortline', 'Volkswagen Jetta Highline',\n",
              "       'Volkswagen Passat Diesel', 'Volkswagen Polo',\n",
              "       'Volkswagen Polo Comfortline', 'Volkswagen Polo Highline',\n",
              "       'Volkswagen Polo Highline1.2L', 'Volkswagen Polo Trendline',\n",
              "       'Volkswagen Vento Comfortline', 'Volkswagen Vento Highline',\n",
              "       'Volkswagen Vento Konekt', 'Volvo S80 Summum'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "pipe.steps[0][1].transformers[0][1].categories[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RVA9bEa1L5RW"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "colab": {
      "name": "Quikr Analysis.ipynb",
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}