preprocessing.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re"
      ]
    },
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              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Location</th>\n",
              "      <th>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
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              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "      <th>New_Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mahindra XUV500 W6 2WD</td>\n",
              "      <td>Kochi</td>\n",
              "      <td>2017</td>\n",
              "      <td>41290</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>16.0 kmpl</td>\n",
              "      <td>2179 CC</td>\n",
              "      <td>140 bhp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMW 5 Series 520d Luxury Line</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>2013</td>\n",
              "      <td>65000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>First</td>\n",
              "      <td>22.48 kmpl</td>\n",
              "      <td>1995 CC</td>\n",
              "      <td>190 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.87 Lakh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Toyota Corolla Altis 1.8 G</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>2016</td>\n",
              "      <td>16471</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>14.28 kmpl</td>\n",
              "      <td>1798 CC</td>\n",
              "      <td>138.03 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>20.48 Lakh</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hyundai i20 Asta 1.4 CRDi</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>54339</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>22.54 kmpl</td>\n",
              "      <td>1396 CC</td>\n",
              "      <td>88.73 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Mahindra KUV 100 mFALCON G80 K2</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>2016</td>\n",
              "      <td>24025</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.15 kmpl</td>\n",
              "      <td>1198 CC</td>\n",
              "      <td>82 bhp</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            "text/plain": [
              "                              Name   Location  Year  Kilometers_Driven  \\\n",
              "0           Mahindra XUV500 W6 2WD      Kochi  2017              41290   \n",
              "1    BMW 5 Series 520d Luxury Line      Delhi  2013              65000   \n",
              "2       Toyota Corolla Altis 1.8 G  Bangalore  2016              16471   \n",
              "3        Hyundai i20 Asta 1.4 CRDi       Pune  2015              54339   \n",
              "4  Mahindra KUV 100 mFALCON G80 K2  Hyderabad  2016              24025   \n",
              "\n",
              "  Fuel_Type Transmission Owner_Type     Mileage   Engine       Power  Seats  \\\n",
              "0    Diesel       Manual     Second   16.0 kmpl  2179 CC     140 bhp    7.0   \n",
              "1    Diesel    Automatic      First  22.48 kmpl  1995 CC     190 bhp    5.0   \n",
              "2    Petrol       Manual     Second  14.28 kmpl  1798 CC  138.03 bhp    5.0   \n",
              "3    Diesel       Manual     Second  22.54 kmpl  1396 CC   88.73 bhp    5.0   \n",
              "4    Petrol       Manual      First  18.15 kmpl  1198 CC      82 bhp    6.0   \n",
              "\n",
              "    New_Price  \n",
              "0         NaN  \n",
              "1  67.87 Lakh  \n",
              "2  20.48 Lakh  \n",
              "3         NaN  \n",
              "4         NaN  "
            ]
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      "source": [
        "df = pd.read_csv('/content/test_data.csv')\n",
        "df.head()"
      ]
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "Name                    0\n",
              "Location                0\n",
              "Year                    0\n",
              "Kilometers_Driven       0\n",
              "Fuel_Type               0\n",
              "Transmission            0\n",
              "Owner_Type              0\n",
              "Mileage                 2\n",
              "Engine                 14\n",
              "Power                  14\n",
              "Seats                  17\n",
              "New_Price            2177\n",
              "dtype: int64"
            ]
          },
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      "source": [
        "df.isnull().sum()"
      ]
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      "outputs": [
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          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2520 entries, 0 to 2519\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Name               2520 non-null   object \n",
            " 1   Location           2520 non-null   object \n",
            " 2   Year               2520 non-null   int64  \n",
            " 3   Kilometers_Driven  2520 non-null   int64  \n",
            " 4   Fuel_Type          2520 non-null   object \n",
            " 5   Transmission       2520 non-null   object \n",
            " 6   Owner_Type         2520 non-null   object \n",
            " 7   Mileage            2518 non-null   object \n",
            " 8   Engine             2506 non-null   object \n",
            " 9   Power              2506 non-null   object \n",
            " 10  Seats              2503 non-null   float64\n",
            " 11  New_Price          343 non-null    object \n",
            "dtypes: float64(1), int64(2), object(9)\n",
            "memory usage: 236.4+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
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              "      <th>Name</th>\n",
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              "      <th>0</th>\n",
              "      <td>mahindra xuv500 w6 2wd</td>\n",
              "      <td>Kochi</td>\n",
              "      <td>2017</td>\n",
              "      <td>41290</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
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              "      <th>1</th>\n",
              "      <td>bmw 5 series 520d luxury line</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>2013</td>\n",
              "      <td>65000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>First</td>\n",
              "      <td>22.48 kmpl</td>\n",
              "      <td>1995 CC</td>\n",
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              "      <td>67.87 Lakh</td>\n",
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              "      <td>5</td>\n",
              "      <td>series 520d luxury line</td>\n",
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              "      <th>2</th>\n",
              "      <td>toyota corolla altis 1.8 g</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>2016</td>\n",
              "      <td>16471</td>\n",
              "      <td>Petrol</td>\n",
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              "      <td>Second</td>\n",
              "      <td>14.28 kmpl</td>\n",
              "      <td>1798 CC</td>\n",
              "      <td>138.03 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>20.48 Lakh</td>\n",
              "      <td>toyota</td>\n",
              "      <td>corolla</td>\n",
              "      <td>altis 1.8 g</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hyundai i20 asta 1.4 crdi</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>54339</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>22.54 kmpl</td>\n",
              "      <td>1396 CC</td>\n",
              "      <td>88.73 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>i20</td>\n",
              "      <td>asta 1.4 crdi</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>mahindra kuv 100 mfalcon g80 k2</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>2016</td>\n",
              "      <td>24025</td>\n",
              "      <td>Petrol</td>\n",
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              "      <td>First</td>\n",
              "      <td>18.15 kmpl</td>\n",
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              "      <td>NaN</td>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9095e4f5-7b39-418b-9873-0e158b72c778 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-9095e4f5-7b39-418b-9873-0e158b72c778');\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   Location  Year  Kilometers_Driven  \\\n",
              "0           mahindra xuv500 w6 2wd      Kochi  2017              41290   \n",
              "1    bmw 5 series 520d luxury line      Delhi  2013              65000   \n",
              "2       toyota corolla altis 1.8 g  Bangalore  2016              16471   \n",
              "3        hyundai i20 asta 1.4 crdi       Pune  2015              54339   \n",
              "4  mahindra kuv 100 mfalcon g80 k2  Hyderabad  2016              24025   \n",
              "\n",
              "  Fuel_Type Transmission Owner_Type     Mileage   Engine       Power  Seats  \\\n",
              "0    Diesel       Manual     Second   16.0 kmpl  2179 CC     140 bhp    7.0   \n",
              "1    Diesel    Automatic      First  22.48 kmpl  1995 CC     190 bhp    5.0   \n",
              "2    Petrol       Manual     Second  14.28 kmpl  1798 CC  138.03 bhp    5.0   \n",
              "3    Diesel       Manual     Second  22.54 kmpl  1396 CC   88.73 bhp    5.0   \n",
              "4    Petrol       Manual      First  18.15 kmpl  1198 CC      82 bhp    6.0   \n",
              "\n",
              "    New_Price     Brand    Model                  Variant  \n",
              "0         NaN  mahindra   xuv500                   w6 2wd  \n",
              "1  67.87 Lakh       bmw        5  series 520d luxury line  \n",
              "2  20.48 Lakh    toyota  corolla              altis 1.8 g  \n",
              "3         NaN   hyundai      i20            asta 1.4 crdi  \n",
              "4         NaN  mahindra      kuv       100 mfalcon g80 k2  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Brand']=df['Name'].apply(lambda x:' '.join(x.split()[:1]))\n",
        "df['Model']=df['Name'].apply(lambda x:' '.join(x.split()[1:2]))\n",
        "df['Variant']=df['Name'].apply(lambda x:' '.join(x.split()[2:]))\n",
        "df['Name']=df['Name'].apply(lambda x:' '.join(x.lower() for x in x.split()))\n",
        "df['Brand']=df['Brand'].apply(lambda x:' '.join(x.lower() for x in x.split()))\n",
        "df['Model']=df['Model'].apply(lambda x:' '.join(x.lower() for x in x.split()))\n",
        "df['Variant']=df['Variant'].apply(lambda x:' '.join(x.lower() for x in x.split()))\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhSeuO-U-U0-"
      },
      "source": [
        "### Preprocessing the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJmLNUzw865z",
        "outputId": "7f877430-7c51-454d-9752-52cf7dcd8a70"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def split_it(x):\n",
        "  try:\n",
        "    return re.findall('[0-9.]+', x)[0]\n",
        "  except:\n",
        "    return np.nan\n",
        "\n",
        "df['Mileage'] = df['Mileage'].apply(lambda x: split_it(x))\n",
        "# df['Mileage'].isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wOC-75E-YtX",
        "outputId": "95f70b2d-b3b9-455f-bc96-77280c4d2921"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def spliter(x):\n",
        "  try:\n",
        "    return x.split(' ')[0]\n",
        "  except:\n",
        "    return np.nan\n",
        "df['Engine'] = df['Engine'].apply(lambda x: spliter(x))\n",
        "df['Engine'].isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PzWahmfI_T3Q"
      },
      "outputs": [],
      "source": [
        "df['Power'] = df['Power'].apply(lambda x: split_it(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QCjqeEG5_Xy4"
      },
      "outputs": [],
      "source": [
        "# df['Engine'] = df['Engine'].apply(lambda x: spliter(x))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oywSxdL5-7mK",
        "outputId": "4bc64d1c-7bcf-4fb7-e4d9-6cf091f2900f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def spliter(x):\n",
        "  try:\n",
        "    return x.split(' ')[0]\n",
        "  except:\n",
        "    return np.nan\n",
        "df['Engine'] = df['Engine'].apply(lambda x: spliter(x))\n",
        "df['Engine'].isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jE8PZ-0I87ZN"
      },
      "source": [
        "### Filling the null values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DUk7R74VEBZB"
      },
      "outputs": [],
      "source": [
        "df[['Mileage','Engine','Power']] = df[['Mileage','Engine','Power']].astype('f') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lUHJljV4FJtU"
      },
      "outputs": [],
      "source": [
        "# df.info()\n",
        "brands = list(df.Brand.unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TFAfmxwU8E1U",
        "outputId": "a4f6700b-cca4-4e91-d3fa-a1f08e511d9c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-28-76ba382319b6>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.Engine[(df.Engine.isnull()) & (df['Brand']==i)]= df.Engine[(df.Engine.notnull()) & (df['Brand']==i)].mean()\n",
            "<ipython-input-28-76ba382319b6>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.Power[(df.Power.isnull()) & (df['Brand']==i)]= df.Power[(df.Power.notnull()) & (df['Brand']==i)].mean()\n",
            "<ipython-input-28-76ba382319b6>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.Mileage[(df.Mileage.isnull()) & (df['Brand']==i)]= df.Mileage[(df.Mileage.notnull()) & (df['Brand']==i)].mean()\n",
            "<ipython-input-28-76ba382319b6>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.Seats[df.Seats.isnull()]=int(df.Seats[df.Seats.notnull()].mean())\n"
          ]
        }
      ],
      "source": [
        "for i in brands:\n",
        "    df.Engine[(df.Engine.isnull()) & (df['Brand']==i)]= df.Engine[(df.Engine.notnull()) & (df['Brand']==i)].mean()\n",
        "    df.Power[(df.Power.isnull()) & (df['Brand']==i)]= df.Power[(df.Power.notnull()) & (df['Brand']==i)].mean()\n",
        "    df.Mileage[(df.Mileage.isnull()) & (df['Brand']==i)]= df.Mileage[(df.Mileage.notnull()) & (df['Brand']==i)].mean()\n",
        "\n",
        "df.Seats[df.Seats.isnull()]=int(df.Seats[df.Seats.notnull()].mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-fdaggIG1eX",
        "outputId": "141bcb77-4146-415a-fc41-2164c9821b8d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Name                    0\n",
              "Location                0\n",
              "Year                    0\n",
              "Kilometers_Driven       0\n",
              "Fuel_Type               0\n",
              "Transmission            0\n",
              "Owner_Type              0\n",
              "Mileage                 0\n",
              "Engine                  0\n",
              "Power                   0\n",
              "Seats                   0\n",
              "New_Price            2177\n",
              "Brand                   0\n",
              "Model                   0\n",
              "Variant                 0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izbHmipn8MeM",
        "outputId": "acf58af3-077a-4934-f524-029b66d67b70"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Brand\n",
              "audi             15.240000\n",
              "bentley           8.600000\n",
              "bmw              17.008257\n",
              "chevrolet        18.362955\n",
              "datsun           21.773750\n",
              "fiat             15.881818\n",
              "force            17.000000\n",
              "ford             18.428539\n",
              "honda            18.435926\n",
              "hyundai          19.000000\n",
              "isuzu            12.400000\n",
              "jaguar           14.955714\n",
              "jeep             16.900000\n",
              "lamborghini       6.400000\n",
              "land             10.316072\n",
              "mahindra         14.858981\n",
              "maruti           21.646524\n",
              "mercedes-benz    13.529098\n",
              "mini             17.110001\n",
              "mitsubishi       12.590000\n",
              "nissan           19.212051\n",
              "porsche          14.678333\n",
              "renault          21.245693\n",
              "skoda            16.974606\n",
              "tata             19.703466\n",
              "toyota           14.539637\n",
              "volkswagen       18.163662\n",
              "volvo            15.566250\n",
              "Name: Mileage, dtype: float32"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.groupby(by='Brand')['Mileage'].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yKys3jf2JCY6"
      },
      "outputs": [],
      "source": [
        "# df['New_Price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wl_JUuDT8MhO"
      },
      "outputs": [],
      "source": [
        "df['New_Price1']= df['New_Price'].apply(lambda x: float(str(x).replace(' Lakh','')) if (str(x).find('Lakh'))!=-1 else (float(str(x).replace(' Cr',''))*100 if (str(x).find('Cr'))!=-1 else 'Nan'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yD7OeUdvJWF9",
        "outputId": "fdfc0ab8-784e-45ef-912c-e6f44dd9d83b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-35-65e3ddfe0196>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.New_Price1[df.New_Price1=='Nan']=np.nan\n"
          ]
        }
      ],
      "source": [
        "df.New_Price1[df.New_Price1=='Nan']=np.nan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PbkJQFyj8MlI"
      },
      "outputs": [],
      "source": [
        "df['New_Price1']=pd.to_numeric(df['New_Price1'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLK0vy688MoU",
        "outputId": "ac60366e-12b2-4c72-b57e-619c0bfe8889"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Name                    0\n",
              "Location                0\n",
              "Year                    0\n",
              "Kilometers_Driven       0\n",
              "Fuel_Type               0\n",
              "Transmission            0\n",
              "Owner_Type              0\n",
              "Mileage                 0\n",
              "Engine                  0\n",
              "Power                   0\n",
              "Seats                   0\n",
              "New_Price            2177\n",
              "Brand                   0\n",
              "Model                   0\n",
              "Variant                 0\n",
              "New_Price1           2177\n",
              "dtype: int64"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DlAt6ytUJ__R"
      },
      "outputs": [],
      "source": [
        "data=df.copy()\n",
        "brand_list=df.Model.unique().tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cegnPxSlKVdm"
      },
      "outputs": [],
      "source": [
        "model_brand=[]\n",
        "for i in range(0,data.shape[0]):\n",
        "    if (data.loc[i,'Model'],data.loc[i,'Brand']) not in model_brand:\n",
        "        model_brand.append((data.loc[i,'Model'],data.loc[i,'Brand']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPs2DL92KbOK",
        "outputId": "a28286f9-f6a9-487c-cd2e-00b1784e216a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('xuv500', 'mahindra'),\n",
              " ('5', 'bmw'),\n",
              " ('corolla', 'toyota'),\n",
              " ('i20', 'hyundai'),\n",
              " ('kuv', 'mahindra'),\n",
              " ('alto', 'maruti'),\n",
              " ('punto', 'fiat'),\n",
              " ('wagon', 'maruti'),\n",
              " ('pajero', 'mitsubishi'),\n",
              " ('brio', 'honda'),\n",
              " ('creta', 'hyundai'),\n",
              " ('amaze', 'honda'),\n",
              " ('city', 'honda'),\n",
              " ('santro', 'hyundai'),\n",
              " ('new', 'mercedes-benz'),\n",
              " ('duster', 'renault'),\n",
              " ('polo', 'volkswagen'),\n",
              " ('s', 'mercedes-benz'),\n",
              " ('xj', 'jaguar'),\n",
              " ('swift', 'maruti'),\n",
              " ('3', 'bmw'),\n",
              " ('verna', 'hyundai'),\n",
              " ('fortuner', 'toyota'),\n",
              " ('beat', 'chevrolet'),\n",
              " ('grand', 'hyundai'),\n",
              " ('figo', 'ford'),\n",
              " ('teana', 'nissan'),\n",
              " ('a4', 'audi'),\n",
              " ('jazz', 'honda'),\n",
              " ('terrano', 'nissan'),\n",
              " ('rover', 'land'),\n",
              " ('compass', 'jeep'),\n",
              " ('scorpio', 'mahindra'),\n",
              " ('go', 'datsun'),\n",
              " ('indigo', 'tata'),\n",
              " ('innova', 'toyota'),\n",
              " ('x1', 'bmw'),\n",
              " ('jetta', 'volkswagen'),\n",
              " ('ssangyong', 'mahindra'),\n",
              " ('vento', 'volkswagen'),\n",
              " ('rapid', 'skoda'),\n",
              " ('s', 'maruti'),\n",
              " ('ertiga', 'maruti'),\n",
              " ('i10', 'hyundai'),\n",
              " ('cla', 'mercedes-benz'),\n",
              " ('a6', 'audi'),\n",
              " ('gla', 'mercedes-benz'),\n",
              " ('aveo', 'chevrolet'),\n",
              " ('elantra', 'hyundai'),\n",
              " ('baleno', 'maruti'),\n",
              " ('cls-class', 'mercedes-benz'),\n",
              " ('dzire', 'maruti'),\n",
              " ('sonata', 'hyundai'),\n",
              " ('eeco', 'maruti'),\n",
              " ('nexon', 'tata'),\n",
              " ('eon', 'hyundai'),\n",
              " ('indica', 'tata'),\n",
              " ('fiesta', 'ford'),\n",
              " ('civic', 'honda'),\n",
              " ('xcent', 'hyundai'),\n",
              " ('kwid', 'renault'),\n",
              " ('q5', 'audi'),\n",
              " ('sx4', 'maruti'),\n",
              " ('e-class', 'mercedes-benz'),\n",
              " ('santa', 'hyundai'),\n",
              " ('d-max', 'isuzu'),\n",
              " ('ecosport', 'ford'),\n",
              " ('x6', 'bmw'),\n",
              " ('ikon', 'ford'),\n",
              " ('cr-v', 'honda'),\n",
              " ('spark', 'chevrolet'),\n",
              " ('s-cross', 'maruti'),\n",
              " ('ritz', 'maruti'),\n",
              " ('hexa', 'tata'),\n",
              " ('nano', 'tata'),\n",
              " ('safari', 'tata'),\n",
              " ('accord', 'honda'),\n",
              " ('superb', 'skoda'),\n",
              " ('crosspolo', 'volkswagen'),\n",
              " ('ignis', 'maruti'),\n",
              " ('laura', 'skoda'),\n",
              " ('fluence', 'renault'),\n",
              " ('ameo', 'volkswagen'),\n",
              " ('glc', 'mercedes-benz'),\n",
              " ('mobilio', 'honda'),\n",
              " ('captiva', 'chevrolet'),\n",
              " ('quanto', 'mahindra'),\n",
              " ('m-class', 'mercedes-benz'),\n",
              " ('vitara', 'maruti'),\n",
              " ('s60', 'volvo'),\n",
              " ('celerio', 'maruti'),\n",
              " ('esteem', 'maruti'),\n",
              " ('omni', 'maruti'),\n",
              " ('800', 'maruti'),\n",
              " ('etios', 'toyota'),\n",
              " ('xylo', 'mahindra'),\n",
              " ('captur', 'renault'),\n",
              " ('sunny', 'nissan'),\n",
              " ('ciaz', 'maruti'),\n",
              " ('thar', 'mahindra'),\n",
              " ('x-trail', 'nissan'),\n",
              " ('zest', 'tata'),\n",
              " ('endeavour', 'ford'),\n",
              " ('octavia', 'skoda'),\n",
              " ('cruze', 'chevrolet'),\n",
              " ('xf', 'jaguar'),\n",
              " ('linea', 'fiat'),\n",
              " ('scala', 'renault'),\n",
              " ('accent', 'hyundai'),\n",
              " ('koleos', 'renault'),\n",
              " ('optra', 'chevrolet'),\n",
              " ('grande', 'fiat'),\n",
              " ('estilo', 'maruti'),\n",
              " ('micra', 'nissan'),\n",
              " ('camry', 'toyota'),\n",
              " ('xc60', 'volvo'),\n",
              " ('s-class', 'mercedes-benz'),\n",
              " ('zen', 'maruti'),\n",
              " ('lancer', 'mitsubishi'),\n",
              " ('q7', 'audi'),\n",
              " ('grand', 'maruti'),\n",
              " ('a-star', 'maruti'),\n",
              " ('passat', 'volkswagen'),\n",
              " ('x3', 'bmw'),\n",
              " ('r-class', 'mercedes-benz'),\n",
              " ('q3', 'audi'),\n",
              " ('rs5', 'audi'),\n",
              " ('b', 'mercedes-benz'),\n",
              " ('classic', 'ford'),\n",
              " ('x5', 'bmw'),\n",
              " ('redi-go', 'datsun'),\n",
              " ('yeti', 'skoda'),\n",
              " ('br-v', 'honda'),\n",
              " ('enjoy', 'chevrolet'),\n",
              " ('gl-class', 'mercedes-benz'),\n",
              " ('panamera', 'porsche'),\n",
              " ('7', 'bmw'),\n",
              " ('a', 'mercedes-benz'),\n",
              " ('manza', 'tata'),\n",
              " ('6', 'bmw'),\n",
              " ('new', 'tata'),\n",
              " ('cayenne', 'porsche'),\n",
              " ('tiago', 'tata'),\n",
              " ('redi', 'datsun'),\n",
              " ('e', 'mahindra'),\n",
              " ('elite', 'hyundai'),\n",
              " ('mustang', 'ford'),\n",
              " ('avventura', 'fiat'),\n",
              " ('fusion', 'ford'),\n",
              " ('xc90', 'volvo'),\n",
              " ('lodgy', 'renault'),\n",
              " ('gle', 'mercedes-benz'),\n",
              " ('tigor', 'tata'),\n",
              " ('fabia', 'skoda'),\n",
              " ('siena', 'fiat'),\n",
              " ('1', 'bmw'),\n",
              " ('wrv', 'honda'),\n",
              " ('slk-class', 'mercedes-benz'),\n",
              " ('1000', 'maruti'),\n",
              " ('sl-class', 'mercedes-benz'),\n",
              " ('cooper', 'mini'),\n",
              " ('a3', 'audi'),\n",
              " ('v40', 'volvo'),\n",
              " ('sumo', 'tata'),\n",
              " ('z4', 'bmw'),\n",
              " ('prius', 'toyota'),\n",
              " ('tuv', 'mahindra'),\n",
              " ('aspire', 'ford'),\n",
              " ('one', 'force'),\n",
              " ('logan', 'mahindra'),\n",
              " ('versa', 'maruti'),\n",
              " ('getz', 'hyundai'),\n",
              " ('bolero', 'mahindra'),\n",
              " ('sail', 'chevrolet'),\n",
              " ('freestyle', 'ford'),\n",
              " ('brv', 'honda'),\n",
              " ('cedia', 'mitsubishi'),\n",
              " ('pulse', 'renault'),\n",
              " ('bolt', 'tata'),\n",
              " ('wr-v', 'honda'),\n",
              " ('continental', 'bentley'),\n",
              " ('qualis', 'toyota'),\n",
              " ('verito', 'mahindra'),\n",
              " ('gallardo', 'lamborghini'),\n",
              " ('c-class', 'mercedes-benz'),\n",
              " ('f', 'jaguar'),\n",
              " ('nuvosport', 'mahindra'),\n",
              " ('jeep', 'mahindra')]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_brand"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hh2M8sYiKcwZ",
        "outputId": "17f4b7d1-0ff1-459e-bb15-c00d2507b843"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-45-b2fd3b7bd2df>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data.New_Price1[(data.New_Price1.isnull()) & (data['Model']==i[0]) & (data['Brand']==i[1])]= data.New_Price1[(data.New_Price1.notnull()) & (data['Model']==i[0])& (data['Brand']==i[1])].mean()\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Name                    0\n",
              "Location                0\n",
              "Year                    0\n",
              "Kilometers_Driven       0\n",
              "Fuel_Type               0\n",
              "Transmission            0\n",
              "Owner_Type              0\n",
              "Mileage                 0\n",
              "Engine                  0\n",
              "Power                   0\n",
              "Seats                   0\n",
              "New_Price            2177\n",
              "Brand                   0\n",
              "Model                   0\n",
              "Variant                 0\n",
              "New_Price1            744\n",
              "dtype: int64"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for i in model_brand:\n",
        "    data.New_Price1[(data.New_Price1.isnull()) & (data['Model']==i[0]) & (data['Brand']==i[1])]= data.New_Price1[(data.New_Price1.notnull()) & (data['Model']==i[0])& (data['Brand']==i[1])].mean()\n",
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_JGcfHpKhfX",
        "outputId": "b5e93539-927c-4258-d996-e4f870ae9d59"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-47-d5a4279e983f>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data.New_Price1[(data.New_Price1.isnull()) & (data['Brand']==i)] = data.New_Price1[(data.New_Price1.notnull()) & (data['Brand']==i)].mean()\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Name                    0\n",
              "Location                0\n",
              "Year                    0\n",
              "Kilometers_Driven       0\n",
              "Fuel_Type               0\n",
              "Transmission            0\n",
              "Owner_Type              0\n",
              "Mileage                 0\n",
              "Engine                  0\n",
              "Power                   0\n",
              "Seats                   0\n",
              "New_Price            2177\n",
              "Brand                   0\n",
              "Model                   0\n",
              "Variant                 0\n",
              "New_Price1            744\n",
              "dtype: int64"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for i in brand_list:\n",
        "    data.New_Price1[(data.New_Price1.isnull()) & (data['Brand']==i)] = data.New_Price1[(data.New_Price1.notnull()) & (data['Brand']==i)].mean()\n",
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWf7nlTmKtmk",
        "outputId": "bb33ce5b-0fc0-4c46-859e-d604dd524921"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-48-d5a4279e983f>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data.New_Price1[(data.New_Price1.isnull()) & (data['Brand']==i)] = data.New_Price1[(data.New_Price1.notnull()) & (data['Brand']==i)].mean()\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Name                    0\n",
              "Location                0\n",
              "Year                    0\n",
              "Kilometers_Driven       0\n",
              "Fuel_Type               0\n",
              "Transmission            0\n",
              "Owner_Type              0\n",
              "Mileage                 0\n",
              "Engine                  0\n",
              "Power                   0\n",
              "Seats                   0\n",
              "New_Price            2177\n",
              "Brand                   0\n",
              "Model                   0\n",
              "Variant                 0\n",
              "New_Price1            744\n",
              "dtype: int64"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "for i in brand_list:\n",
        "    data.New_Price1[(data.New_Price1.isnull()) & (data['Brand']==i)] = data.New_Price1[(data.New_Price1.notnull()) & (data['Brand']==i)].mean()\n",
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xCcc8hqLLgZk"
      },
      "outputs": [],
      "source": [
        "data.drop(dr,axis=0,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-Zm6JgFLXeV",
        "outputId": "6bb2450a-036c-440b-afdb-4855ac2c3b09"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Name                    0\n",
              "Location                0\n",
              "Year                    0\n",
              "Kilometers_Driven       0\n",
              "Fuel_Type               0\n",
              "Transmission            0\n",
              "Owner_Type              0\n",
              "Mileage                 0\n",
              "Engine                  0\n",
              "Power                   0\n",
              "Seats                   0\n",
              "New_Price            2177\n",
              "Brand                   0\n",
              "Model                   0\n",
              "Variant                 0\n",
              "New_Price1            744\n",
              "dtype: int64"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "ATIEUq_vMBPU",
        "outputId": "86c8e42e-28bd-49a9-f688-e3994d9f9598"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-27927e63-fcf0-4298-aa7a-a1192febc4d6\">\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>Location</th>\n",
              "      <th>Year</th>\n",
              "      <th>Kilometers_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner_Type</th>\n",
              "      <th>Mileage</th>\n",
              "      <th>Engine</th>\n",
              "      <th>Power</th>\n",
              "      <th>Seats</th>\n",
              "      <th>New_Price</th>\n",
              "      <th>Brand</th>\n",
              "      <th>Model</th>\n",
              "      <th>Variant</th>\n",
              "      <th>New_Price1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>mahindra xuv500 w6 2wd</td>\n",
              "      <td>Kochi</td>\n",
              "      <td>2017</td>\n",
              "      <td>41290</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>2179.0</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mahindra</td>\n",
              "      <td>xuv500</td>\n",
              "      <td>w6 2wd</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>bmw 5 series 520d luxury line</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>2013</td>\n",
              "      <td>65000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>First</td>\n",
              "      <td>22.480000</td>\n",
              "      <td>1995.0</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>67.87 Lakh</td>\n",
              "      <td>bmw</td>\n",
              "      <td>5</td>\n",
              "      <td>series 520d luxury line</td>\n",
              "      <td>67.870000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>toyota corolla altis 1.8 g</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>2016</td>\n",
              "      <td>16471</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>14.280000</td>\n",
              "      <td>1798.0</td>\n",
              "      <td>138.029999</td>\n",
              "      <td>5.0</td>\n",
              "      <td>20.48 Lakh</td>\n",
              "      <td>toyota</td>\n",
              "      <td>corolla</td>\n",
              "      <td>altis 1.8 g</td>\n",
              "      <td>20.480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hyundai i20 asta 1.4 crdi</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>54339</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>22.540001</td>\n",
              "      <td>1396.0</td>\n",
              "      <td>88.730003</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>i20</td>\n",
              "      <td>asta 1.4 crdi</td>\n",
              "      <td>10.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>mahindra kuv 100 mfalcon g80 k2</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>2016</td>\n",
              "      <td>24025</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.150000</td>\n",
              "      <td>1198.0</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mahindra</td>\n",
              "      <td>kuv</td>\n",
              "      <td>100 mfalcon g80 k2</td>\n",
              "      <td>7.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2515</th>\n",
              "      <td>maruti swift vdi</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>2014</td>\n",
              "      <td>27365</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>28.400000</td>\n",
              "      <td>1248.0</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.88 Lakh</td>\n",
              "      <td>maruti</td>\n",
              "      <td>swift</td>\n",
              "      <td>vdi</td>\n",
              "      <td>7.880000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2516</th>\n",
              "      <td>hyundai xcent 1.1 crdi s</td>\n",
              "      <td>Jaipur</td>\n",
              "      <td>2015</td>\n",
              "      <td>100000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>24.400000</td>\n",
              "      <td>1120.0</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>xcent</td>\n",
              "      <td>1.1 crdi s</td>\n",
              "      <td>7.723333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2517</th>\n",
              "      <td>mahindra xylo d4 bsiv</td>\n",
              "      <td>Jaipur</td>\n",
              "      <td>2012</td>\n",
              "      <td>55000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>2498.0</td>\n",
              "      <td>112.000000</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mahindra</td>\n",
              "      <td>xylo</td>\n",
              "      <td>d4 bsiv</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2518</th>\n",
              "      <td>maruti wagon r vxi</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>2013</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.900000</td>\n",
              "      <td>998.0</td>\n",
              "      <td>67.099998</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>maruti</td>\n",
              "      <td>wagon</td>\n",
              "      <td>r vxi</td>\n",
              "      <td>5.615333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2519</th>\n",
              "      <td>chevrolet beat diesel</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>2011</td>\n",
              "      <td>47000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>25.440001</td>\n",
              "      <td>936.0</td>\n",
              "      <td>57.599998</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>beat</td>\n",
              "      <td>diesel</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2520 rows × 16 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27927e63-fcf0-4298-aa7a-a1192febc4d6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  "
            ],
            "text/plain": [
              "                                 Name   Location  Year  Kilometers_Driven  \\\n",
              "0              mahindra xuv500 w6 2wd      Kochi  2017              41290   \n",
              "1       bmw 5 series 520d luxury line      Delhi  2013              65000   \n",
              "2          toyota corolla altis 1.8 g  Bangalore  2016              16471   \n",
              "3           hyundai i20 asta 1.4 crdi       Pune  2015              54339   \n",
              "4     mahindra kuv 100 mfalcon g80 k2  Hyderabad  2016              24025   \n",
              "...                               ...        ...   ...                ...   \n",
              "2515                 maruti swift vdi      Delhi  2014              27365   \n",
              "2516         hyundai xcent 1.1 crdi s     Jaipur  2015             100000   \n",
              "2517            mahindra xylo d4 bsiv     Jaipur  2012              55000   \n",
              "2518               maruti wagon r vxi    Kolkata  2013              46000   \n",
              "2519            chevrolet beat diesel  Hyderabad  2011              47000   \n",
              "\n",
              "     Fuel_Type Transmission Owner_Type    Mileage  Engine       Power  Seats  \\\n",
              "0       Diesel       Manual     Second  16.000000  2179.0  140.000000    7.0   \n",
              "1       Diesel    Automatic      First  22.480000  1995.0  190.000000    5.0   \n",
              "2       Petrol       Manual     Second  14.280000  1798.0  138.029999    5.0   \n",
              "3       Diesel       Manual     Second  22.540001  1396.0   88.730003    5.0   \n",
              "4       Petrol       Manual      First  18.150000  1198.0   82.000000    6.0   \n",
              "...        ...          ...        ...        ...     ...         ...    ...   \n",
              "2515    Diesel       Manual      First  28.400000  1248.0   74.000000    5.0   \n",
              "2516    Diesel       Manual      First  24.400000  1120.0   71.000000    5.0   \n",
              "2517    Diesel       Manual     Second  14.000000  2498.0  112.000000    8.0   \n",
              "2518    Petrol       Manual      First  18.900000   998.0   67.099998    5.0   \n",
              "2519    Diesel       Manual      First  25.440001   936.0   57.599998    5.0   \n",
              "\n",
              "       New_Price      Brand    Model                  Variant  New_Price1  \n",
              "0            NaN   mahindra   xuv500                   w6 2wd         NaN  \n",
              "1     67.87 Lakh        bmw        5  series 520d luxury line   67.870000  \n",
              "2     20.48 Lakh     toyota  corolla              altis 1.8 g   20.480000  \n",
              "3            NaN    hyundai      i20            asta 1.4 crdi   10.250000  \n",
              "4            NaN   mahindra      kuv       100 mfalcon g80 k2    7.920000  \n",
              "...          ...        ...      ...                      ...         ...  \n",
              "2515   7.88 Lakh     maruti    swift                      vdi    7.880000  \n",
              "2516         NaN    hyundai    xcent               1.1 crdi s    7.723333  \n",
              "2517         NaN   mahindra     xylo                  d4 bsiv         NaN  \n",
              "2518         NaN     maruti    wagon                    r vxi    5.615333  \n",
              "2519         NaN  chevrolet     beat                   diesel         NaN  \n",
              "\n",
              "[2520 rows x 16 columns]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "rQ0B36D_Mwnm",
        "outputId": "8ebbd4fb-1061-41f3-8438-aac6341ca9f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Int64Index([   0,    6,   16,   18,   22,   24,   31,   33,   34,   44,\n",
              "            ...\n",
              "            2480, 2485, 2487, 2491, 2497, 2505, 2507, 2509, 2517, 2519],\n",
              "           dtype='int64', length=744)"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "indexer = data[data.New_Price1.isnull()==True].index\n",
        "indexer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMLLoGCwNLoj",
        "outputId": "8717ad5f-cec2-480d-d5ae-d26362826ccb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Name                    0\n",
              "Location                0\n",
              "Year                    0\n",
              "Kilometers_Driven       0\n",
              "Fuel_Type               0\n",
              "Transmission            0\n",
              "Owner_Type              0\n",
              "Mileage                 0\n",
              "Engine                  0\n",
              "Power                   0\n",
              "Seats                   0\n",
              "New_Price            1433\n",
              "Brand                   0\n",
              "Model                   0\n",
              "Variant                 0\n",
              "New_Price1              0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.drop(indexer,axis=0,inplace=True)\n",
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e4RbuWYHNaj1"
      },
      "outputs": [],
      "source": [
        "data.to_csv('test_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "id": "5ltrOB6fNfIg",
        "outputId": "52db07d4-5b07-4ee7-92d6-e2c004a69ed5"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Location</th>\n",
              "      <th>Year</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>bmw 5 series 520d luxury line</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>2013</td>\n",
              "      <td>65000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>First</td>\n",
              "      <td>22.480000</td>\n",
              "      <td>1995.0</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>67.87 Lakh</td>\n",
              "      <td>bmw</td>\n",
              "      <td>5</td>\n",
              "      <td>series 520d luxury line</td>\n",
              "      <td>67.870000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>toyota corolla altis 1.8 g</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>2016</td>\n",
              "      <td>16471</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>14.280000</td>\n",
              "      <td>1798.0</td>\n",
              "      <td>138.029999</td>\n",
              "      <td>5.0</td>\n",
              "      <td>20.48 Lakh</td>\n",
              "      <td>toyota</td>\n",
              "      <td>corolla</td>\n",
              "      <td>altis 1.8 g</td>\n",
              "      <td>20.480000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hyundai i20 asta 1.4 crdi</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>54339</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>22.540001</td>\n",
              "      <td>1396.0</td>\n",
              "      <td>88.730003</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>i20</td>\n",
              "      <td>asta 1.4 crdi</td>\n",
              "      <td>10.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>mahindra kuv 100 mfalcon g80 k2</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>2016</td>\n",
              "      <td>24025</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.150000</td>\n",
              "      <td>1198.0</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mahindra</td>\n",
              "      <td>kuv</td>\n",
              "      <td>100 mfalcon g80 k2</td>\n",
              "      <td>7.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>maruti alto 800 2016-2019 lxi</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>2013</td>\n",
              "      <td>24027</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>24.700001</td>\n",
              "      <td>796.0</td>\n",
              "      <td>47.299999</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>maruti</td>\n",
              "      <td>alto</td>\n",
              "      <td>800 2016-2019 lxi</td>\n",
              "      <td>4.264000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2513</th>\n",
              "      <td>toyota innova 2.5 v diesel 7-seater</td>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2011</td>\n",
              "      <td>45004</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>12.800000</td>\n",
              "      <td>2494.0</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>toyota</td>\n",
              "      <td>innova</td>\n",
              "      <td>2.5 v diesel 7-seater</td>\n",
              "      <td>23.871429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2514</th>\n",
              "      <td>honda amaze vx i-dtec</td>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2015</td>\n",
              "      <td>70602</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>25.799999</td>\n",
              "      <td>1498.0</td>\n",
              "      <td>98.599998</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>honda</td>\n",
              "      <td>amaze</td>\n",
              "      <td>vx i-dtec</td>\n",
              "      <td>8.508000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2515</th>\n",
              "      <td>maruti swift vdi</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>2014</td>\n",
              "      <td>27365</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>28.400000</td>\n",
              "      <td>1248.0</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.88 Lakh</td>\n",
              "      <td>maruti</td>\n",
              "      <td>swift</td>\n",
              "      <td>vdi</td>\n",
              "      <td>7.880000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2516</th>\n",
              "      <td>hyundai xcent 1.1 crdi s</td>\n",
              "      <td>Jaipur</td>\n",
              "      <td>2015</td>\n",
              "      <td>100000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>24.400000</td>\n",
              "      <td>1120.0</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hyundai</td>\n",
              "      <td>xcent</td>\n",
              "      <td>1.1 crdi s</td>\n",
              "      <td>7.723333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2518</th>\n",
              "      <td>maruti wagon r vxi</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>2013</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.900000</td>\n",
              "      <td>998.0</td>\n",
              "      <td>67.099998</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>maruti</td>\n",
              "      <td>wagon</td>\n",
              "      <td>r vxi</td>\n",
              "      <td>5.615333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1776 rows × 16 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ec6e714-ee86-4fc1-812e-ddd39bb67fee')\"\n",
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              "    .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-3ec6e714-ee86-4fc1-812e-ddd39bb67fee 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-3ec6e714-ee86-4fc1-812e-ddd39bb67fee');\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    Location  Year  \\\n",
              "1           bmw 5 series 520d luxury line       Delhi  2013   \n",
              "2              toyota corolla altis 1.8 g   Bangalore  2016   \n",
              "3               hyundai i20 asta 1.4 crdi        Pune  2015   \n",
              "4         mahindra kuv 100 mfalcon g80 k2   Hyderabad  2016   \n",
              "5           maruti alto 800 2016-2019 lxi     Kolkata  2013   \n",
              "...                                   ...         ...   ...   \n",
              "2513  toyota innova 2.5 v diesel 7-seater  Coimbatore  2011   \n",
              "2514                honda amaze vx i-dtec  Coimbatore  2015   \n",
              "2515                     maruti swift vdi       Delhi  2014   \n",
              "2516             hyundai xcent 1.1 crdi s      Jaipur  2015   \n",
              "2518                   maruti wagon r vxi     Kolkata  2013   \n",
              "\n",
              "      Kilometers_Driven Fuel_Type Transmission Owner_Type    Mileage  Engine  \\\n",
              "1                 65000    Diesel    Automatic      First  22.480000  1995.0   \n",
              "2                 16471    Petrol       Manual     Second  14.280000  1798.0   \n",
              "3                 54339    Diesel       Manual     Second  22.540001  1396.0   \n",
              "4                 24025    Petrol       Manual      First  18.150000  1198.0   \n",
              "5                 24027    Petrol       Manual      First  24.700001   796.0   \n",
              "...                 ...       ...          ...        ...        ...     ...   \n",
              "2513              45004    Diesel       Manual      First  12.800000  2494.0   \n",
              "2514              70602    Diesel       Manual      First  25.799999  1498.0   \n",
              "2515              27365    Diesel       Manual      First  28.400000  1248.0   \n",
              "2516             100000    Diesel       Manual      First  24.400000  1120.0   \n",
              "2518              46000    Petrol       Manual      First  18.900000   998.0   \n",
              "\n",
              "           Power  Seats   New_Price     Brand    Model  \\\n",
              "1     190.000000    5.0  67.87 Lakh       bmw        5   \n",
              "2     138.029999    5.0  20.48 Lakh    toyota  corolla   \n",
              "3      88.730003    5.0         NaN   hyundai      i20   \n",
              "4      82.000000    6.0         NaN  mahindra      kuv   \n",
              "5      47.299999    5.0         NaN    maruti     alto   \n",
              "...          ...    ...         ...       ...      ...   \n",
              "2513  102.000000    7.0         NaN    toyota   innova   \n",
              "2514   98.599998    5.0         NaN     honda    amaze   \n",
              "2515   74.000000    5.0   7.88 Lakh    maruti    swift   \n",
              "2516   71.000000    5.0         NaN   hyundai    xcent   \n",
              "2518   67.099998    5.0         NaN    maruti    wagon   \n",
              "\n",
              "                      Variant  New_Price1  \n",
              "1     series 520d luxury line   67.870000  \n",
              "2                 altis 1.8 g   20.480000  \n",
              "3               asta 1.4 crdi   10.250000  \n",
              "4          100 mfalcon g80 k2    7.920000  \n",
              "5           800 2016-2019 lxi    4.264000  \n",
              "...                       ...         ...  \n",
              "2513    2.5 v diesel 7-seater   23.871429  \n",
              "2514                vx i-dtec    8.508000  \n",
              "2515                      vdi    7.880000  \n",
              "2516               1.1 crdi s    7.723333  \n",
              "2518                    r vxi    5.615333  \n",
              "\n",
              "[1776 rows x 16 columns]"
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XFZJIZ8QNhkr"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}