Car_selling_price_prediction.ipynb

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      "name": "Car selling price prediction.ipynb",
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      "source": [
        "# Importing necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars = pd.read_csv(\"/content/Car details.csv\")"
      ],
      "metadata": {
        "id": "1arfjKw_HAvo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cars.head(10)"
      ],
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        "outputId": "d60c4e90-296e-4524-c083-af2f0030b65c"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "                                   name  year  selling_price  km_driven  \\\n",
              "0                Maruti Swift Dzire VDI  2014         450000     145500   \n",
              "1          Skoda Rapid 1.5 TDI Ambition  2014         370000     120000   \n",
              "2              Honda City 2017-2020 EXi  2006         158000     140000   \n",
              "3             Hyundai i20 Sportz Diesel  2010         225000     127000   \n",
              "4                Maruti Swift VXI BSIII  2007         130000     120000   \n",
              "5         Hyundai Xcent 1.2 VTVT E Plus  2017         440000      45000   \n",
              "6          Maruti Wagon R LXI DUO BSIII  2007          96000     175000   \n",
              "7                    Maruti 800 DX BSII  2001          45000       5000   \n",
              "8                      Toyota Etios VXD  2011         350000      90000   \n",
              "9  Ford Figo Diesel Celebration Edition  2013         200000     169000   \n",
              "\n",
              "     fuel seller_type transmission         owner     mileage   engine  \\\n",
              "0  Diesel  Individual       Manual   First Owner   23.4 kmpl  1248 CC   \n",
              "1  Diesel  Individual       Manual  Second Owner  21.14 kmpl  1498 CC   \n",
              "2  Petrol  Individual       Manual   Third Owner   17.7 kmpl  1497 CC   \n",
              "3  Diesel  Individual       Manual   First Owner   23.0 kmpl  1396 CC   \n",
              "4  Petrol  Individual       Manual   First Owner   16.1 kmpl  1298 CC   \n",
              "5  Petrol  Individual       Manual   First Owner  20.14 kmpl  1197 CC   \n",
              "6     LPG  Individual       Manual   First Owner  17.3 km/kg  1061 CC   \n",
              "7  Petrol  Individual       Manual  Second Owner   16.1 kmpl   796 CC   \n",
              "8  Diesel  Individual       Manual   First Owner  23.59 kmpl  1364 CC   \n",
              "9  Diesel  Individual       Manual   First Owner   20.0 kmpl  1399 CC   \n",
              "\n",
              "    max_power                    torque  seats  \n",
              "0      74 bhp            190Nm@ 2000rpm    5.0  \n",
              "1  103.52 bhp       250Nm@ 1500-2500rpm    5.0  \n",
              "2      78 bhp     12.7@ 2,700(kgm@ rpm)    5.0  \n",
              "3      90 bhp  22.4 kgm at 1750-2750rpm    5.0  \n",
              "4    88.2 bhp     11.5@ 4,500(kgm@ rpm)    5.0  \n",
              "5   81.86 bhp         113.75nm@ 4000rpm    5.0  \n",
              "6    57.5 bhp      7.8@ 4,500(kgm@ rpm)    5.0  \n",
              "7      37 bhp             59Nm@ 2500rpm    4.0  \n",
              "8    67.1 bhp       170Nm@ 1800-2400rpm    5.0  \n",
              "9    68.1 bhp            160Nm@ 2000rpm    5.0  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>year</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>km_driven</th>\n",
              "      <th>fuel</th>\n",
              "      <th>seller_type</th>\n",
              "      <th>transmission</th>\n",
              "      <th>owner</th>\n",
              "      <th>mileage</th>\n",
              "      <th>engine</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maruti Swift Dzire VDI</td>\n",
              "      <td>2014</td>\n",
              "      <td>450000</td>\n",
              "      <td>145500</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>23.4 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>74 bhp</td>\n",
              "      <td>190Nm@ 2000rpm</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Skoda Rapid 1.5 TDI Ambition</td>\n",
              "      <td>2014</td>\n",
              "      <td>370000</td>\n",
              "      <td>120000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second Owner</td>\n",
              "      <td>21.14 kmpl</td>\n",
              "      <td>1498 CC</td>\n",
              "      <td>103.52 bhp</td>\n",
              "      <td>250Nm@ 1500-2500rpm</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Honda City 2017-2020 EXi</td>\n",
              "      <td>2006</td>\n",
              "      <td>158000</td>\n",
              "      <td>140000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Third Owner</td>\n",
              "      <td>17.7 kmpl</td>\n",
              "      <td>1497 CC</td>\n",
              "      <td>78 bhp</td>\n",
              "      <td>12.7@ 2,700(kgm@ rpm)</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hyundai i20 Sportz Diesel</td>\n",
              "      <td>2010</td>\n",
              "      <td>225000</td>\n",
              "      <td>127000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>23.0 kmpl</td>\n",
              "      <td>1396 CC</td>\n",
              "      <td>90 bhp</td>\n",
              "      <td>22.4 kgm at 1750-2750rpm</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Maruti Swift VXI BSIII</td>\n",
              "      <td>2007</td>\n",
              "      <td>130000</td>\n",
              "      <td>120000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>16.1 kmpl</td>\n",
              "      <td>1298 CC</td>\n",
              "      <td>88.2 bhp</td>\n",
              "      <td>11.5@ 4,500(kgm@ rpm)</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hyundai Xcent 1.2 VTVT E Plus</td>\n",
              "      <td>2017</td>\n",
              "      <td>440000</td>\n",
              "      <td>45000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>20.14 kmpl</td>\n",
              "      <td>1197 CC</td>\n",
              "      <td>81.86 bhp</td>\n",
              "      <td>113.75nm@ 4000rpm</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Maruti Wagon R LXI DUO BSIII</td>\n",
              "      <td>2007</td>\n",
              "      <td>96000</td>\n",
              "      <td>175000</td>\n",
              "      <td>LPG</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>17.3 km/kg</td>\n",
              "      <td>1061 CC</td>\n",
              "      <td>57.5 bhp</td>\n",
              "      <td>7.8@ 4,500(kgm@ rpm)</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Maruti 800 DX BSII</td>\n",
              "      <td>2001</td>\n",
              "      <td>45000</td>\n",
              "      <td>5000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second Owner</td>\n",
              "      <td>16.1 kmpl</td>\n",
              "      <td>796 CC</td>\n",
              "      <td>37 bhp</td>\n",
              "      <td>59Nm@ 2500rpm</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Toyota Etios VXD</td>\n",
              "      <td>2011</td>\n",
              "      <td>350000</td>\n",
              "      <td>90000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>23.59 kmpl</td>\n",
              "      <td>1364 CC</td>\n",
              "      <td>67.1 bhp</td>\n",
              "      <td>170Nm@ 1800-2400rpm</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Ford Figo Diesel Celebration Edition</td>\n",
              "      <td>2013</td>\n",
              "      <td>200000</td>\n",
              "      <td>169000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>20.0 kmpl</td>\n",
              "      <td>1399 CC</td>\n",
              "      <td>68.1 bhp</td>\n",
              "      <td>160Nm@ 2000rpm</td>\n",
              "      <td>5.0</td>\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UCVm2EBHLPk",
        "outputId": "ef60fc79-9796-44ec-be52-1b0d9ce726ed"
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          "data": {
            "text/plain": [
              "(8128, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1k0SdRJDHUFF",
        "outputId": "bedd91d8-6b47-4bab-8be9-e997e8461ff3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name               0\n",
              "year               0\n",
              "selling_price      0\n",
              "km_driven          0\n",
              "fuel               0\n",
              "seller_type        0\n",
              "transmission       0\n",
              "owner              0\n",
              "mileage          221\n",
              "engine           221\n",
              "max_power        215\n",
              "torque           222\n",
              "seats            221\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars = cars.dropna(how = 'any')\n",
        "\n",
        "cars.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3cmZGjTZHXwN",
        "outputId": "f92bb1c3-a648-4fc3-b936-17570402eaa1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7906, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re"
      ],
      "metadata": {
        "id": "uppOVTGKHajd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res = '190Nm@ 2,000rpm'.replace(\".\", \"\")\n",
        "res = res.replace(\",\", \"\")\n",
        "a =  [int(s) for s in re.findall(r'\\d+', res)]\n",
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFNbfHiHHda9",
        "outputId": "b551de28-68a1-4deb-d8d7-e83cf927d717"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[190, 2000]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torque_list = cars['torque'].to_list()\n",
        "# torque_list[:2]\n",
        "torque_rpm = []\n",
        "def extractingRPM(x):\n",
        "  for item in x:\n",
        "    res = item.replace(\".\", \"\")\n",
        "    res = res.replace(\",\", \"\")\n",
        "    temp = [int(s) for s in re.findall(r'\\d+', res)]\n",
        "    torque_rpm.append(max(temp))\n",
        "\n",
        "extractingRPM(torque_list)\n",
        "print(torque_list[:2])\n",
        "print(torque_rpm[:2])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5HTkTZuGH1jI",
        "outputId": "fc288f50-b67a-4149-830a-595665d0165e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['190Nm@ 2000rpm', '250Nm@ 1500-2500rpm']\n",
            "[2000, 2500]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars['torque_rpm'] = torque_rpm\n",
        "\n",
        "cars.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "5Pz5IxoFIAfJ",
        "outputId": "298df839-12dd-4528-b6bb-df552aefcee9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           name  year  selling_price  km_driven    fuel  \\\n",
              "0        Maruti Swift Dzire VDI  2014         450000     145500  Diesel   \n",
              "1  Skoda Rapid 1.5 TDI Ambition  2014         370000     120000  Diesel   \n",
              "\n",
              "  seller_type transmission         owner     mileage   engine   max_power  \\\n",
              "0  Individual       Manual   First Owner   23.4 kmpl  1248 CC      74 bhp   \n",
              "1  Individual       Manual  Second Owner  21.14 kmpl  1498 CC  103.52 bhp   \n",
              "\n",
              "                torque  seats  torque_rpm  \n",
              "0       190Nm@ 2000rpm    5.0        2000  \n",
              "1  250Nm@ 1500-2500rpm    5.0        2500  "
            ],
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              "                                                     [key], {});\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mil_list = cars['mileage'].to_list()\n",
        "# torque_list[:2]\n",
        "mil_kmpl = []"
      ],
      "metadata": {
        "id": "sMWtuboaIDLE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mil_list = cars['mileage'].to_list()\n",
        "# torque_list[:2]\n",
        "mil_kmpl = []\n",
        "def extractingmil(x):\n",
        "  for item in x:\n",
        "    temp = []\n",
        "    try:\n",
        "      for s in item.split(\" \"):\n",
        "        temp.append(float(s))\n",
        "    except:\n",
        "      pass\n",
        "    mil_kmpl.append(max(temp))\n",
        "\n",
        "extractingmil(mil_list)\n",
        "print(mil_list[:2])\n",
        "print(mil_kmpl[:2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPd3cRvOIIIB",
        "outputId": "17d45fbb-567b-4838-c9ac-3b350eff279e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['23.4 kmpl', '21.14 kmpl']\n",
            "[23.4, 21.14]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars['mil_kmpl'] = mil_kmpl\n",
        "\n",
        "cars.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "va-MTKnLIN7V",
        "outputId": "4649b30e-1563-4752-ffb4-3d424f51750f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           name  year  selling_price  km_driven    fuel  \\\n",
              "0        Maruti Swift Dzire VDI  2014         450000     145500  Diesel   \n",
              "1  Skoda Rapid 1.5 TDI Ambition  2014         370000     120000  Diesel   \n",
              "\n",
              "  seller_type transmission         owner     mileage   engine   max_power  \\\n",
              "0  Individual       Manual   First Owner   23.4 kmpl  1248 CC      74 bhp   \n",
              "1  Individual       Manual  Second Owner  21.14 kmpl  1498 CC  103.52 bhp   \n",
              "\n",
              "                torque  seats  torque_rpm  mil_kmpl  \n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "engine_list = cars['engine'].to_list()\n",
        "# torque_list[:2]\n",
        "engine_cc = []\n",
        "def extractingEngine(x):\n",
        "  for item in x:\n",
        "    temp = []\n",
        "    try:\n",
        "      for s in item.split(\" \"):\n",
        "        temp.append(float(s))\n",
        "    except:\n",
        "      pass\n",
        "    engine_cc.append(max(temp))\n",
        "\n",
        "extractingEngine(engine_list)\n",
        "print(engine_list[:2])\n",
        "print(engine_cc[:2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F1v5z1wPIR0G",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['1248 CC', '1498 CC']\n",
            "[1248.0, 1498.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars['engine_cc'] = engine_cc\n",
        "\n",
        "cars.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           name  year  selling_price  km_driven    fuel  \\\n",
              "0        Maruti Swift Dzire VDI  2014         450000     145500  Diesel   \n",
              "1  Skoda Rapid 1.5 TDI Ambition  2014         370000     120000  Diesel   \n",
              "\n",
              "  seller_type transmission         owner     mileage   engine   max_power  \\\n",
              "0  Individual       Manual   First Owner   23.4 kmpl  1248 CC      74 bhp   \n",
              "1  Individual       Manual  Second Owner  21.14 kmpl  1498 CC  103.52 bhp   \n",
              "\n",
              "                torque  seats  torque_rpm  mil_kmpl  engine_cc  \n",
              "0       190Nm@ 2000rpm    5.0        2000     23.40     1248.0  \n",
              "1  250Nm@ 1500-2500rpm    5.0        2500     21.14     1498.0  "
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              "      <th>0</th>\n",
              "      <td>Maruti Swift Dzire VDI</td>\n",
              "      <td>2014</td>\n",
              "      <td>450000</td>\n",
              "      <td>145500</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>23.4 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>74 bhp</td>\n",
              "      <td>190Nm@ 2000rpm</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2000</td>\n",
              "      <td>23.40</td>\n",
              "      <td>1248.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Skoda Rapid 1.5 TDI Ambition</td>\n",
              "      <td>2014</td>\n",
              "      <td>370000</td>\n",
              "      <td>120000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Individual</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second Owner</td>\n",
              "      <td>21.14 kmpl</td>\n",
              "      <td>1498 CC</td>\n",
              "      <td>103.52 bhp</td>\n",
              "      <td>250Nm@ 1500-2500rpm</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2500</td>\n",
              "      <td>21.14</td>\n",
              "      <td>1498.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-74fd29cc-6285-4eb8-bd76-787d5d70a957')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-74fd29cc-6285-4eb8-bd76-787d5d70a957 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-74fd29cc-6285-4eb8-bd76-787d5d70a957');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "power_list = cars['max_power'].to_list()\n",
        "# torque_list[:2]\n",
        "max_power = []\n",
        "def extractingPower(x):\n",
        "  for item in x:\n",
        "    temp = []\n",
        "    try:\n",
        "      for s in item.split(\" \"):\n",
        "        temp.append(float(s))\n",
        "    except:\n",
        "      pass\n",
        "    max_power.append(max(temp))\n",
        "\n",
        "extractingPower(power_list)\n",
        "print(power_list[:2])\n",
        "print(max_power[:2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcMoAe2OIa5U",
        "outputId": "4e6d80b0-2a68-44ac-ff15-84b49c059487"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['74 bhp', '103.52 bhp']\n",
            "[74.0, 103.52]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars['max_power_new'] = max_power\n"
      ],
      "metadata": {
        "id": "tM-9drG5IdR3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cars_new = cars.drop(['mileage', 'engine', 'max_power', 'torque'], axis = 1)"
      ],
      "metadata": {
        "id": "mQB92hPpIgYk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "sns.heatmap(cars_new.corr(),annot=True,cmap='viridis',linewidths=.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "sAneayvYIkLA",
        "outputId": "8e5c520c-2cd4-4c5e-df9b-16be7d6a5b98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0173cf72d0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can see there is high correlation of selling price(which we will be predicting) with max_power output of the car\n",
        "# The values of correlation are pretty averaged out meaning we wont be excluding any clumn as each column is able to provide us with new\n",
        "# information that is not provided by any other column."
      ],
      "metadata": {
        "id": "-vE4uObSIoFU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Our journey for finding categorical data.\n",
        "\n",
        "cars_new['fuel'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZvcF35qIxpU",
        "outputId": "e2ef7631-28db-49d7-cd0c-888255c5f1c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Diesel    4299\n",
              "Petrol    3520\n",
              "CNG         52\n",
              "LPG         35\n",
              "Name: fuel, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars_new['seller_type'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5S8aXWLZI3k_",
        "outputId": "fa5b5cc6-29f5-43a4-ba4c-daf4edbbc246"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Individual          6563\n",
              "Dealer              1107\n",
              "Trustmark Dealer     236\n",
              "Name: seller_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cars_new['owner'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2ALgBihJGKh",
        "outputId": "93f8b7d6-ccc0-4485-f71a-4e92c0a91f15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "First Owner             5215\n",
              "Second Owner            2016\n",
              "Third Owner              510\n",
              "Fourth & Above Owner     160\n",
              "Test Drive Car             5\n",
              "Name: owner, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Converting categorical data to integer format.\n",
        "\n",
        "def ref1(x):\n",
        "  if x == 'Manual':\n",
        "    return 1\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "cars_new['transmission'] = cars_new['transmission'].map(ref1)"
      ],
      "metadata": {
        "id": "6pxzjznaJMat"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ref2(x):\n",
        "  if x == 'Individual':\n",
        "    return 1\n",
        "  elif x == 'Dealer':\n",
        "    return 0\n",
        "  else:\n",
        "    return -1\n",
        "\n",
        "cars_new['seller_type'] = cars_new['seller_type'].map(ref2)"
      ],
      "metadata": {
        "id": "XOCPcksnJPy-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def ref3(x):\n",
        "  if x == 'Petrol':\n",
        "    return 1\n",
        "  elif x == 'Diesel':\n",
        "    return 0\n",
        "  else:\n",
        "    return -1\n",
        "\n",
        "cars_new['fuel'] = cars_new['fuel'].map(ref3)"
      ],
      "metadata": {
        "id": "LnvATGaZJYkU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "owners = pd.get_dummies(cars_new['owner'])\n",
        "\n",
        "X = pd.concat([cars_new, owners], axis=1)"
      ],
      "metadata": {
        "id": "X9U5cWubJb5U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "rlOal19qJgwc",
        "outputId": "e0504b96-c104-48aa-8452-7ca34226a4d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           name  year  selling_price  km_driven  fuel  \\\n",
              "0        Maruti Swift Dzire VDI  2014         450000     145500     0   \n",
              "1  Skoda Rapid 1.5 TDI Ambition  2014         370000     120000     0   \n",
              "2      Honda City 2017-2020 EXi  2006         158000     140000     1   \n",
              "3     Hyundai i20 Sportz Diesel  2010         225000     127000     0   \n",
              "4        Maruti Swift VXI BSIII  2007         130000     120000     1   \n",
              "\n",
              "   seller_type  transmission         owner  seats  torque_rpm  mil_kmpl  \\\n",
              "0            1             1   First Owner    5.0        2000     23.40   \n",
              "1            1             1  Second Owner    5.0        2500     21.14   \n",
              "2            1             1   Third Owner    5.0        2700     17.70   \n",
              "3            1             1   First Owner    5.0        2750     23.00   \n",
              "4            1             1   First Owner    5.0        4500     16.10   \n",
              "\n",
              "   engine_cc  max_power_new  First Owner  Fourth & Above Owner  Second Owner  \\\n",
              "0     1248.0          74.00            1                     0             0   \n",
              "1     1498.0         103.52            0                     0             1   \n",
              "2     1497.0          78.00            0                     0             0   \n",
              "3     1396.0          90.00            1                     0             0   \n",
              "4     1298.0          88.20            1                     0             0   \n",
              "\n",
              "   Test Drive Car  Third Owner  \n",
              "0               0            0  \n",
              "1               0            0  \n",
              "2               0            1  \n",
              "3               0            0  \n",
              "4               0            0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ce4e6d8-3a2d-4940-9d0c-34d5b8cf6f81\">\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>year</th>\n",
              "      <th>selling_price</th>\n",
              "      <th>km_driven</th>\n",
              "      <th>fuel</th>\n",
              "      <th>seller_type</th>\n",
              "      <th>transmission</th>\n",
              "      <th>owner</th>\n",
              "      <th>seats</th>\n",
              "      <th>torque_rpm</th>\n",
              "      <th>mil_kmpl</th>\n",
              "      <th>engine_cc</th>\n",
              "      <th>max_power_new</th>\n",
              "      <th>First Owner</th>\n",
              "      <th>Fourth &amp; Above Owner</th>\n",
              "      <th>Second Owner</th>\n",
              "      <th>Test Drive Car</th>\n",
              "      <th>Third Owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maruti Swift Dzire VDI</td>\n",
              "      <td>2014</td>\n",
              "      <td>450000</td>\n",
              "      <td>145500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2000</td>\n",
              "      <td>23.40</td>\n",
              "      <td>1248.0</td>\n",
              "      <td>74.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Skoda Rapid 1.5 TDI Ambition</td>\n",
              "      <td>2014</td>\n",
              "      <td>370000</td>\n",
              "      <td>120000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Second Owner</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2500</td>\n",
              "      <td>21.14</td>\n",
              "      <td>1498.0</td>\n",
              "      <td>103.52</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>2</th>\n",
              "      <td>Honda City 2017-2020 EXi</td>\n",
              "      <td>2006</td>\n",
              "      <td>158000</td>\n",
              "      <td>140000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Third Owner</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2700</td>\n",
              "      <td>17.70</td>\n",
              "      <td>1497.0</td>\n",
              "      <td>78.00</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>3</th>\n",
              "      <td>Hyundai i20 Sportz Diesel</td>\n",
              "      <td>2010</td>\n",
              "      <td>225000</td>\n",
              "      <td>127000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2750</td>\n",
              "      <td>23.00</td>\n",
              "      <td>1396.0</td>\n",
              "      <td>90.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Maruti Swift VXI BSIII</td>\n",
              "      <td>2007</td>\n",
              "      <td>130000</td>\n",
              "      <td>120000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>First Owner</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4500</td>\n",
              "      <td>16.10</td>\n",
              "      <td>1298.0</td>\n",
              "      <td>88.20</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",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ce4e6d8-3a2d-4940-9d0c-34d5b8cf6f81')\"\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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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          document.querySelector('#df-3ce4e6d8-3a2d-4940-9d0c-34d5b8cf6f81 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-3ce4e6d8-3a2d-4940-9d0c-34d5b8cf6f81');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = X['selling_price']\n",
        "X = X.drop(['selling_price', 'name', 'owner'], axis = 1)"
      ],
      "metadata": {
        "id": "1krkfeecJqHM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.decomposition import PCA"
      ],
      "metadata": {
        "id": "Lq7VtyXCJtud"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X[:3000], y[:3000], test_size=0.2)"
      ],
      "metadata": {
        "id": "-XRck_omJwl1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_model = RandomForestRegressor(n_estimators=300, random_state = 42, n_jobs = -1)"
      ],
      "metadata": {
        "id": "yYjatfi9JzYD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Fit\n",
        "random_model.fit(Xtrain, ytrain)\n",
        "\n",
        "y_pred = random_model.predict(Xtest)\n",
        "\n",
        "#Checking the accuracy\n",
        "random_model_accuracy = round(random_model.score(Xtrain, ytrain)*100,2)\n",
        "print(round(random_model_accuracy, 2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKSxoa2bJ8M7",
        "outputId": "249fb6f3-826b-42db-e450-1fe585003ca4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "99.22 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = LinearRegression()\n",
        "\n",
        "reg.fit(Xtrain, ytrain)"
      ],
      "metadata": {
        "id": "8eAy5YvDKDaF",
        "outputId": "b91bff0e-face-40d1-c5ef-13dae45cb591",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    }
  ]
}