car_final.ipynb

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      "name": "car_final.ipynb",
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      "source": [
        "# Predicting used car prices\n",
        "\n",
        "In this colab, I'll work with the [Kaggle](https://www.kaggle.com/avikasliwal/used-cars-price-prediction) dataset about used cars and their prices. The colab first includes exploration of the dataset followed by prediction of prices."
      ],
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      "cell_type": "markdown",
      "source": [
        "## Import libraries\n",
        "\n",
        "I'll import  `numpy` to work with arrays and `pandas` to read in the dataset files, `matplotlib` & `seaborn` for plotting and `sklearn` for various machine learning models."
      ],
      "metadata": {
        "id": "kx7snVeZAOJK"
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      "source": [
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Read dataset\n",
        "\n",
        "I'll read the dataset and get information about it."
      ],
      "metadata": {
        "id": "pvl9ssnL7qgB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car=pd.read_csv(\"https://raw.githubusercontent.com/iamdkcoder/car-price-prediction/master/dataset.csv\")\n",
        "car.head(5)"
      ],
      "metadata": {
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        },
        "id": "VwvZ45tq7q_C",
        "outputId": "11fa9692-86c7-45d2-cfc1-1840427400c7"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "   Unnamed: 0                              Name    Location  Year  \\\n",
              "0           0            Maruti Wagon R LXI CNG      Mumbai  2010   \n",
              "1           1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015   \n",
              "2           2                      Honda Jazz V     Chennai  2011   \n",
              "3           3                 Maruti Ertiga VDI     Chennai  2012   \n",
              "4           4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013   \n",
              "\n",
              "   Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage   Engine  \\\n",
              "0              72000       CNG       Manual      First  26.6 km/kg   998 CC   \n",
              "1              41000    Diesel       Manual      First  19.67 kmpl  1582 CC   \n",
              "2              46000    Petrol       Manual      First   18.2 kmpl  1199 CC   \n",
              "3              87000    Diesel       Manual      First  20.77 kmpl  1248 CC   \n",
              "4              40670    Diesel    Automatic     Second   15.2 kmpl  1968 CC   \n",
              "\n",
              "       Power  Seats  New_Price  Price  \n",
              "0  58.16 bhp    5.0        NaN   1.75  \n",
              "1  126.2 bhp    5.0        NaN  12.50  \n",
              "2   88.7 bhp    5.0  8.61 Lakh   4.50  \n",
              "3  88.76 bhp    7.0        NaN   6.00  \n",
              "4  140.8 bhp    5.0        NaN  17.74  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</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>Price</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Maruti Wagon R LXI CNG</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>2010</td>\n",
              "      <td>72000</td>\n",
              "      <td>CNG</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>26.6 km/kg</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>58.16 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>19.67 kmpl</td>\n",
              "      <td>1582 CC</td>\n",
              "      <td>126.2 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Honda Jazz V</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.2 kmpl</td>\n",
              "      <td>1199 CC</td>\n",
              "      <td>88.7 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.61 Lakh</td>\n",
              "      <td>4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Maruti Ertiga VDI</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.77 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>88.76 bhp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.2 kmpl</td>\n",
              "      <td>1968 CC</td>\n",
              "      <td>140.8 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.74</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#backup car dataset"
      ],
      "metadata": {
        "id": "G_9xWk4Y703s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "backup_Car= car.copy()"
      ],
      "metadata": {
        "id": "nfYANGPkCAGe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "backup_Car.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E0_dRbbRMPDi",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6019 entries, 0 to 6018\n",
            "Data columns (total 14 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Unnamed: 0         6019 non-null   int64  \n",
            " 1   Name               6019 non-null   object \n",
            " 2   Location           6019 non-null   object \n",
            " 3   Year               6019 non-null   int64  \n",
            " 4   Kilometers_Driven  6019 non-null   int64  \n",
            " 5   Fuel_Type          6019 non-null   object \n",
            " 6   Transmission       6019 non-null   object \n",
            " 7   Owner_Type         6019 non-null   object \n",
            " 8   Mileage            6017 non-null   object \n",
            " 9   Engine             5983 non-null   object \n",
            " 10  Power              5983 non-null   object \n",
            " 11  Seats              5977 non-null   float64\n",
            " 12  New_Price          824 non-null    object \n",
            " 13  Price              6019 non-null   float64\n",
            "dtypes: float64(2), int64(3), object(9)\n",
            "memory usage: 658.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis\n",
        "\n",
        "Let's explore the various columns and draw information about how useful each column is. \n"
      ],
      "metadata": {
        "id": "lyJFZmjyCTwI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import missingno as msg\n",
        "msg.matrix(car)"
      ],
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          "height": 406
        },
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      "execution_count": null,
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          "execution_count": 64
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          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car['Owner_Type'].value_counts().nlargest(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-wnMgNbY6W0I",
        "outputId": "8ac95def-5652-4413-d323-c89c169d33da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "First             4929\n",
              "Second             968\n",
              "Third              113\n",
              "Fourth & Above       9\n",
              "Name: Owner_Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the above results showcase that first hand cars are sold mostly"
      ],
      "metadata": {
        "id": "o1T0u_v2-oXD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car['Kilometers_Driven'].value_counts().nlargest(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Kjgxie26y8S",
        "outputId": "ac31aaa7-9fa8-4d18-e133-a185d704a28f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000    82\n",
              "45000    70\n",
              "65000    68\n",
              "50000    61\n",
              "55000    60\n",
              "70000    60\n",
              "30000    54\n",
              "52000    54\n",
              "80000    50\n",
              "75000    50\n",
              "Name: Kilometers_Driven, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above result showcases the top 10 Car Mileage in Kilometer. This also implies that older cars are being offered for sales than the newer cars.\n",
        "\n"
      ],
      "metadata": {
        "id": "n5zvPNeN7cOW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Seaborn visualization library\n",
        "import seaborn as sns\n",
        "# Create the default pairplot\n",
        "sns.pairplot(car)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 831
        },
        "id": "VF49gIVyv0wf",
        "outputId": "2b55bb7b-7550-4e0c-8a25-22f48d09713a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f7e0491f610>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car.info()"
      ],
      "metadata": {
        "id": "Kfk-VhzBCEd2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Index\n",
        "\n",
        "The first column is the index for each data point and hence we can simply remove it."
      ],
      "metadata": {
        "id": "Grr9_4I8xtaL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car= car.iloc[:,1:]"
      ],
      "metadata": {
        "id": "_PF-DLYUxsK6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#lets check what happend\n",
        "car"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "FskZPLWNyAqM",
        "outputId": "833afaf0-3749-4b1b-b65e-4ac44f22adf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Name    Location  Year  Kilometers_Driven  \\\n",
              "0               Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
              "1     Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
              "2                         Honda Jazz V     Chennai  2011              46000   \n",
              "3                    Maruti Ertiga VDI     Chennai  2012              87000   \n",
              "4      Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
              "...                                ...         ...   ...                ...   \n",
              "6014                  Maruti Swift VDI       Delhi  2014              27365   \n",
              "6015          Hyundai Xcent 1.1 CRDi S      Jaipur  2015             100000   \n",
              "6016             Mahindra Xylo D4 BSIV      Jaipur  2012              55000   \n",
              "6017                Maruti Wagon R VXI     Kolkata  2013              46000   \n",
              "6018             Chevrolet Beat Diesel   Hyderabad  2011              47000   \n",
              "\n",
              "     Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
              "0          CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
              "1       Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
              "2       Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
              "3       Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
              "4       Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
              "...        ...          ...        ...         ...      ...        ...    ...   \n",
              "6014    Diesel       Manual      First   28.4 kmpl  1248 CC     74 bhp    5.0   \n",
              "6015    Diesel       Manual      First   24.4 kmpl  1120 CC     71 bhp    5.0   \n",
              "6016    Diesel       Manual     Second   14.0 kmpl  2498 CC    112 bhp    8.0   \n",
              "6017    Petrol       Manual      First   18.9 kmpl   998 CC   67.1 bhp    5.0   \n",
              "6018    Diesel       Manual      First  25.44 kmpl   936 CC   57.6 bhp    5.0   \n",
              "\n",
              "      New_Price  Price  \n",
              "0           NaN   1.75  \n",
              "1           NaN  12.50  \n",
              "2     8.61 Lakh   4.50  \n",
              "3           NaN   6.00  \n",
              "4           NaN  17.74  \n",
              "...         ...    ...  \n",
              "6014  7.88 Lakh   4.75  \n",
              "6015        NaN   4.00  \n",
              "6016        NaN   2.90  \n",
              "6017        NaN   2.65  \n",
              "6018        NaN   2.50  \n",
              "\n",
              "[6019 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c0c2aed9-7712-428a-9b90-a6a00cb1c4a8\">\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>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maruti Wagon R LXI CNG</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>2010</td>\n",
              "      <td>72000</td>\n",
              "      <td>CNG</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>26.6 km/kg</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>58.16 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>19.67 kmpl</td>\n",
              "      <td>1582 CC</td>\n",
              "      <td>126.2 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Honda Jazz V</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.2 kmpl</td>\n",
              "      <td>1199 CC</td>\n",
              "      <td>88.7 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.61 Lakh</td>\n",
              "      <td>4.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Maruti Ertiga VDI</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.77 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>88.76 bhp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.2 kmpl</td>\n",
              "      <td>1968 CC</td>\n",
              "      <td>140.8 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.74</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6014</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>28.4 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>74 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.88 Lakh</td>\n",
              "      <td>4.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6015</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>24.4 kmpl</td>\n",
              "      <td>1120 CC</td>\n",
              "      <td>71 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6016</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>14.0 kmpl</td>\n",
              "      <td>2498 CC</td>\n",
              "      <td>112 bhp</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6017</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>18.9 kmpl</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>67.1 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6018</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>25.44 kmpl</td>\n",
              "      <td>936 CC</td>\n",
              "      <td>57.6 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6019 rows × 13 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Name\n",
        "\n",
        "Let's explore the various cars in the dataset."
      ],
      "metadata": {
        "id": "ccW8aKHQyJ_P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car['Name'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JgIYjDfDmdcj",
        "outputId": "5b0b50a9-105e-494a-b583-4bd8178f96d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mahindra XUV500 W8 2WD           49\n",
              "Maruti Swift VDI                 45\n",
              "Honda City 1.5 S MT              34\n",
              "Maruti Swift Dzire VDI           34\n",
              "Maruti Swift VDI BSIV            31\n",
              "                                 ..\n",
              "Ford Fiesta Titanium 1.5 TDCi     1\n",
              "Mahindra Scorpio S10 AT 4WD       1\n",
              "Hyundai i20 1.2 Era               1\n",
              "Toyota Camry W4 (AT)              1\n",
              "Mahindra Xylo D4 BSIV             1\n",
              "Name: Name, Length: 1876, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As it appears, there are several cars in the dataset, some of them with a count higher than 1.\n",
        "Sometimes the resale value of a car also depends on manufacturer of car and hence, I'll extract the manufacturer and model from this column and add it to the dataset."
      ],
      "metadata": {
        "id": "uEaf7-Z_yY05"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car['Manufacturer']=car['Name'].str.split(\" \").str.slice(0,1).str.join(' ')\n",
        "car['Model']= car['Name'].str.split(\" \").str.slice(0,3).str.join(' ')"
      ],
      "metadata": {
        "id": "HQ7LUQWhyjVr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834
        },
        "id": "9DiXVa5S-_MU",
        "outputId": "aa26f7b2-9cb3-4d86-b8d6-bc65c7ceec35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Name    Location  Year  Kilometers_Driven  \\\n",
              "0               Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
              "1     Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
              "2                         Honda Jazz V     Chennai  2011              46000   \n",
              "3                    Maruti Ertiga VDI     Chennai  2012              87000   \n",
              "4      Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
              "...                                ...         ...   ...                ...   \n",
              "6014                  Maruti Swift VDI       Delhi  2014              27365   \n",
              "6015          Hyundai Xcent 1.1 CRDi S      Jaipur  2015             100000   \n",
              "6016             Mahindra Xylo D4 BSIV      Jaipur  2012              55000   \n",
              "6017                Maruti Wagon R VXI     Kolkata  2013              46000   \n",
              "6018             Chevrolet Beat Diesel   Hyderabad  2011              47000   \n",
              "\n",
              "     Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
              "0          CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
              "1       Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
              "2       Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
              "3       Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
              "4       Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
              "...        ...          ...        ...         ...      ...        ...    ...   \n",
              "6014    Diesel       Manual      First   28.4 kmpl  1248 CC     74 bhp    5.0   \n",
              "6015    Diesel       Manual      First   24.4 kmpl  1120 CC     71 bhp    5.0   \n",
              "6016    Diesel       Manual     Second   14.0 kmpl  2498 CC    112 bhp    8.0   \n",
              "6017    Petrol       Manual      First   18.9 kmpl   998 CC   67.1 bhp    5.0   \n",
              "6018    Diesel       Manual      First  25.44 kmpl   936 CC   57.6 bhp    5.0   \n",
              "\n",
              "      New_Price  Price Manufacturer                  Model  \n",
              "0           NaN   1.75       Maruti         Maruti Wagon R  \n",
              "1           NaN  12.50      Hyundai      Hyundai Creta 1.6  \n",
              "2     8.61 Lakh   4.50        Honda           Honda Jazz V  \n",
              "3           NaN   6.00       Maruti      Maruti Ertiga VDI  \n",
              "4           NaN  17.74         Audi            Audi A4 New  \n",
              "...         ...    ...          ...                    ...  \n",
              "6014  7.88 Lakh   4.75       Maruti       Maruti Swift VDI  \n",
              "6015        NaN   4.00      Hyundai      Hyundai Xcent 1.1  \n",
              "6016        NaN   2.90     Mahindra       Mahindra Xylo D4  \n",
              "6017        NaN   2.65       Maruti         Maruti Wagon R  \n",
              "6018        NaN   2.50    Chevrolet  Chevrolet Beat Diesel  \n",
              "\n",
              "[6019 rows x 15 columns]"
            ],
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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",
              "      <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>Price</th>\n",
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              "      <th>Model</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maruti Wagon R LXI CNG</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>2010</td>\n",
              "      <td>72000</td>\n",
              "      <td>CNG</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>26.6 km/kg</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>58.16 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.75</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Wagon R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
              "      <td>Pune</td>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>19.67 kmpl</td>\n",
              "      <td>1582 CC</td>\n",
              "      <td>126.2 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.50</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>Hyundai Creta 1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Honda Jazz V</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.2 kmpl</td>\n",
              "      <td>1199 CC</td>\n",
              "      <td>88.7 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.61 Lakh</td>\n",
              "      <td>4.50</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Honda Jazz V</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Maruti Ertiga VDI</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.77 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>88.76 bhp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Ertiga VDI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
              "      <td>Coimbatore</td>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.2 kmpl</td>\n",
              "      <td>1968 CC</td>\n",
              "      <td>140.8 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.74</td>\n",
              "      <td>Audi</td>\n",
              "      <td>Audi A4 New</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6014</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>28.4 kmpl</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>74 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.88 Lakh</td>\n",
              "      <td>4.75</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Swift VDI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6015</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>24.4 kmpl</td>\n",
              "      <td>1120 CC</td>\n",
              "      <td>71 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>Hyundai Xcent 1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6016</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>14.0 kmpl</td>\n",
              "      <td>2498 CC</td>\n",
              "      <td>112 bhp</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.90</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>Mahindra Xylo D4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6017</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>18.9 kmpl</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>67.1 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.65</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Wagon R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6018</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>25.44 kmpl</td>\n",
              "      <td>936 CC</td>\n",
              "      <td>57.6 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.50</td>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Chevrolet Beat Diesel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6019 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-041535eb-538f-4079-a5ef-eb7ac75a4685')\"\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-041535eb-538f-4079-a5ef-eb7ac75a4685 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-041535eb-538f-4079-a5ef-eb7ac75a4685');\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": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's also confirm that there are no null values and identify all unique values."
      ],
      "metadata": {
        "id": "W0X5V6D40DIB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (12, 8))\n",
        "plot = sns.countplot(x = 'Manufacturer', data = car)\n",
        "plt.xticks(rotation = 90)\n",
        "for p in plot.patches:\n",
        "    plot.annotate(p.get_height(), \n",
        "                        (p.get_x() + p.get_width() / 2.0, \n",
        "                         p.get_height()), \n",
        "                        ha = 'center', \n",
        "                        va = 'center', \n",
        "                        xytext = (0, 5),\n",
        "                        textcoords = 'offset points')\n",
        "\n",
        "plt.title(\"Count of cars based on manufacturers\")\n",
        "plt.xlabel(\"Manufacturer\")\n",
        "plt.ylabel(\"Count of cars\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "V7kveICZ0CQH",
        "outputId": "6a659267-46b8-4a63-dbfa-be8135eb5d66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count of cars')"
            ]
          },
          "metadata": {},
          "execution_count": 194
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Maximum cars in the dataset are by the manufacturer **Maruti** and there are no null values.\n",
        "\n",
        "I'll also drop the `Name` column."
      ],
      "metadata": {
        "id": "apnimvCV0pK5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car.drop('Name',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "n1wYMfEy0rSK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Location\n",
        "\n",
        "Location should not be a determinant for the price of a car and I'll safely remove it."
      ],
      "metadata": {
        "id": "j-U7NXA0054V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car.drop('Location',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "zbbV185o034m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car[\"Fuel_Type\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Fh394oYmgzr",
        "outputId": "d99fef66-d1de-44bc-fed2-9dd38759b628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Diesel      3205\n",
              "Petrol      2746\n",
              "CNG           56\n",
              "LPG           10\n",
              "Electric       2\n",
              "Name: Fuel_Type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize = (10, 8))\n",
        "plot = sns.countplot(x = 'Fuel_Type', data = car)\n",
        "plt.xticks(rotation = 90)\n",
        "for p in plot.patches:\n",
        "    plot.annotate(p.get_height(), \n",
        "                        (p.get_x() + p.get_width() / 2.0, \n",
        "                         p.get_height()), \n",
        "                        ha = 'center', \n",
        "                        va = 'center', \n",
        "                        xytext = (0, 5),\n",
        "                        textcoords = 'offset points')\n",
        "plt.title(\"Count of cars based on Fuel_Type\")\n",
        "plt.xlabel(\"Fuel_Type\")\n",
        "plt.ylabel(\"Count of cars\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "MpdFICdXopvF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "b2b51a43-9130-4854-97da-dc0b8d6a92c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Diesel and petrol cars are sold mostly than CNG , LPG and electric\n"
      ],
      "metadata": {
        "id": "3OAr_WNI_Rsh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Kilometers_Driven\n",
        "\n",
        "`Kilometers_Driven` is a numerical column with a certain range of values."
      ],
      "metadata": {
        "id": "vY3Ei7nr1cbz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car['Kilometers_Driven']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OfD3aE15GejO",
        "outputId": "c861b58a-eb93-490f-a17c-394237594ff0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        72000\n",
              "1        41000\n",
              "2        46000\n",
              "3        87000\n",
              "4        40670\n",
              "         ...  \n",
              "6014     27365\n",
              "6015    100000\n",
              "6016     55000\n",
              "6017     46000\n",
              "6018     47000\n",
              "Name: Kilometers_Driven, Length: 6019, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data range is really varied and the high values might affect prediction, thus, it is really important that scaling be applied to this column for sure."
      ],
      "metadata": {
        "id": "oCEkxm-H3TkJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mileage\n",
        "\n",
        "This column defines the mileage of the car. We need to extract the numerical value out of each string and save it.\n",
        "\n",
        "but before that we can will se  mileage units"
      ],
      "metadata": {
        "id": "3xrAbDfM3V2B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmkg = 0\n",
        "kmpl = 0\n",
        "for i in car.Mileage:\n",
        "    if str(i).endswith(\"km/kg\"):\n",
        "        kmkg+=1\n",
        "    elif str(i).endswith(\"kmpl\"):\n",
        "        kmpl+=1\n",
        "print('The number of rows with Km/Kg : {} '.format(kmkg))\n",
        "print('The number of rows with Km/pl : {} '.format(kmpl))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "laPzRyPL_gji",
        "outputId": "73720fbf-ff5d-4c0e-b3a2-299526356a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of rows with Km/Kg : 66 \n",
            "The number of rows with Km/pl : 5951 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Soooooo , not many in the list to make that great a difference \n",
        "Still gonna do it though....\n",
        "\n",
        "But first , let us remove the missing values\n"
      ],
      "metadata": {
        "id": "NScO8PrV__yv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import missingno as msg\n",
        "msg.matrix(car)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "GoSufsnsANOO",
        "outputId": "545118bf-0c9e-4a36-e45d-f8a697ccd807"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7dfcebcc90>"
            ]
          },
          "metadata": {},
          "execution_count": 201
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": 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BrwC+0pr3AOxFZT0fCfxA0hnAicA7gS0TMB89ktYDTqUy//9i+0FJswC0sivfpJopLdme8qjtyd3PMQHziIiIaddf1izBvgiQtApwOPApSUsC2P4Jdc18OXCupNf2B8zb4/IdingKS9A8IqbFalTTkq/1goCSdpf0LUlnS1qsBafG957QFzDfmqopvKXtXwxg/BHTpGWE9P5+1ZZffhv4EPV9OETSIravAbYEtgIeBeYDrgZea/uywYx+OEyh7viDwBuoWvHPAbD9sKSJ7d+7Aw8Da7Sf3f1vRERETLu+c/pVJH1e0vGSPijphYMeX8Sg2P4Z8HFq1enOkpZq238KfJYKnJ8t6Y1J3IgYWxI0j4gp6suuXbY1NpwLeAkwu6Q1JF0MfAR4FrAMcEar6/Zo7zX6AuZfBT5q+6iZ/OtETLPeEsr27y8AB0maxfZDwOnA5lStwsMkLWr7XttfB9a0vRLwcdt/GdgvMCQ6+5CVJT3L9hlUlvlkYHNJq7bHTZI0TtJzgH8Dtw9s0BEREUOmczzeDPgesBawFJVhe4ykDQc4vIiZopsc1n7urXY8FNga2AD4TF/g/DPAP4GTJM2WRvQRY0eC5hExRX0nxqcCKwFnABOAu4ADgNuo2s7vBL4MLEQF0Ptfoxcw/3AC5k/O/zqZyonWjNeWTvYC5qdQjW8FLALQ7vse1WRyFSrjfPF2373tZR7pf90YmbbU9fvAugC2fwm8FVgM2FPSu9tDFwBWBhYGrpz5I42IiBheklaiVo3uQSUJLA+8kbpOWE3SvIMcX8SM1rk+WEfS3G21Yy9w/jUq43wDYEdJy7TtP6NWpL7e9oNZ/RgxdkwY9AAi4qlL0jzANlTTz/MBU0GrZYAb28x577FzAdcCD/dlmO9A1Xve0vbRM/lXGJP63r/XAcsBcwPX2T4pJ1ozXqc5z6HAq4CNgUtt39eyzR+2PQk4uU1iHAocK2kT2ze218jnNAKSFu81WO24GvgHNUH3dagLkNZn4WzgVEnnArMBzwT2sn36TBx2RETE0Oqcm74OuAY4yfbN7e5tqWP0V23/W9JcnQSCiKHQtwL141Sfry9I2q/v+uBQSQsCnwful3SE7YttnzfI8UfEyCRoHhFTJGktqhTLzcDpLUAI8Id26z1uIeBtwHbATrZv69y3ILAm8IkEzJ+8TsD8A1SG/vVUBv8zJG0A7JiyHzOepOdS2VP72v5N27YYsFX72z4POMP2Sa2e9p5UNnqMkKTDgcmSduhdcLeSTw9L2gf4mqS32z67rQb4aWu+9DNgBWBv4EzbV7Tn/kezpYiIiJh2nUSAFwFz2r4BQNJZVImWNWxf1o7JL5N0jO0HBzTciFHREmNms/1AJ2C+EJXAsSpVqlEtcH6vpIntmvk7VOLZFu3+j3eupSNiDEl5loj4L+1k4LPAF6iT43+07f013FYB9gH2BfaxfVjb3gsc3ga8qy1Vi2kg6S1UyZs9gdWBF1LNZdYENus1PYzRM4WyN/MCzwdul/RCSZtSjXzWpUqyHEMF1aGagy7Zu4iMaSfpCGAz4NRuhlprvgpwMdUEdKXHn6Jxts+nmrLOTdWYn6fz3ATMIyIiplFfb6PZOnfdAjxb0nySfkCVaVyzBcznpOqcvx6Yc6YOOGKUte/ARlSyzDPbtjOBz9t+GFgP+D1VdmUHSfN0AuMLAd+iguYHJmAeMXYlaB4R/8X2v6hahb+igubvbdsf7Z1Et6DtK6mSLdvb3rttH9fLRrE9uZt5Hk+sc5GyGnAFcKLtG2zfD3wI+Ctwck6+RldbctnL8F8MwPZlwAXA8VQJkH2pMiyvs/0C4Cbqc6Itx7xnEGMfBpK+Tl18vNX2zyW9S9IHWy1zAGz/mZqo2ErSS1rGj9s+5+fAO6jJjL17zUEjIiJi2vSVCXw7sHPnuPp1YBKVULMMsKrtS1tgfb12+5HtNOOOMa19B+akavh/WtLZ1LXvMb1VkFTPowupa7Rd22TSYlSi06K2j7b9pwH9ChExClKeJeJprnti3GX7TEkPU9mbh0i62/Z3bfeCVJMkHUQt0byjvVZKIYyeFYG7bd8Ej2U2dJe/rgEs1svuj5Hrq1G4P/B8SUfZPpsKiu9MlSn6u+1z2+MWB24FrhrQsIeGpH2pTJwNbZ/X+iO8H1gb+Ftb+n0ClWl+ErAJFSC/GhjXJvPG2T5H0juBHwOvBn46hf9dRERE/A99ZQK/Qh17e8l2/6JWQW4HPADM1Y69ywM7AXvaPqE9f4rXGBFjhe2vS5qPWn19P3WuejFAr4a5pPcAx1Lnp1tQE0oLUYkcETHGJWge8TTWl0nyCuA5wFzA/9n+fQtCjaeChkdIogXOJ7dA40PAQ73XS8B85HqfRefi4gpgDUnPoLJrl+HxgPl8VBmK+SV92/ZdAxr2mNfe917A/BQqg+RLtGB4+zy+2PecxajvxALAWTN1wENG0pHAB4FHgLUlnd8mit4l6TXAW4CPU8u9rwF2Be6mMnsO7H12bZ80zvZPJC2drJ6IiIiRUzXaPog67h7fyxy3/ZCkE4A7gM8BZwJzUP2OdrR9SHt+EmlizOr7+52LipvNA7xF0qW2b2wB817g/P3Au4HlgIep78y1gxl9RIwmZfI3Ilomyf5UxsiiwA3A2ba3bPe/FdiFqqu9te3TBjTUofK/MnAkrQ8cAEymsnteb/s6SROoLNxdgR1snzzTBjzEJB1A1Sp/H3C57QckzQJMsP1A53E7A6+gapm/zfalAxnwEGgB83WBDYAFqSXfPwI+afv6zuPmo0pEvY+qnToJWATYxPa3+l6zOxGYC/aIiIgRkHQglT3+Xts3t22i4geT28/jqJVddwL/7jwux98Ys/pWoC4AzAKMp5VgAb4KfLnTDHdCp/9O/v4jhkwyzSOe5tqSyoOB3YEfUie+21ENTW62vWvLODe1HPNkSS8B/pYllyPXF9x7I/Baqj78NS2b/yRJrwU+BvykPe5VVLB2T2D3BMynXSv98dLe0sq2bV7qwvAbti9q215CZVA9V9JVVGPc8cDLqJPnlW3/ZWaPf1hI+hyVYb5qq2E+gZocOgyYLGn7XuC8raQ4glrtsjaVxbM98DaqydJjuvukXLBERET8b5I+AZxr+4rOtonAa4B/dQPm7RjbO3ddvB2nL+h7PeX4G2NVX8D8S8D8wBm2vwfs3lpP7dq7v62OnCBpY+CPtv9I+45ExHBI0Dwi1gZ+ARxn+1Z4LDj7F+CM3oNa2YOJwOy2/zqIgQ6TTsB8UypQeBu1vPUZkr4N7GT7E5Lup0pTXAX8G7gR2Nn2V9vzk83wJLUMqZOBeyVt0HnfxlHB8MslPZdqYrUHVSLnH1Qzn/ttbytpK2C87btn/m8wHFqA/A/Aq23/HsD2I5JOoi40Dm+P+5Ttf7R/j7f9qO3vA9+XdB1wmKSv2b5wIL9IRETEGCZpaSop4Ly+u8YBtwCLS3pOK0XhzvNeCGwn6fBusB3+c/I6YizpK9l4KlWy8atUTx0AbHcD5xMl/Rh4J7A5tSI734GIIZPyLBFPI93Z8/bznMBFwG9tf6htO4sqgfDOVj/7TcCzbZ/U91oJ1o5AX4b54sBvgEOAE6lM5tWA/YDfAWvbvl/S84EXU40n77b9f+35+QymUbvQ+5fteyW9iFox8aikbagT47uo1RbfsL2npNmpbOY5gbfnRHh0TK00USuJsz4VOD8T6AbOu9+d17X732/7jP7XiYiIiCcm6Zm2b5f0BuDelimLpK2BA4FtqMSae9r2WagA4ZbAZr3HRwyLVorx41RpwAtbzfL/OG+V9Bmq59FtVNnAtWxfMpABR8QMlUzziKcBSQvYvq0ze74c8Bfb90m6FniOpFmB04CleLzh5EJUHeGbJc1h+/7eayZYOzKdoN/bqSaqv6SaxdzUHnKEpBuB7wC7UXXL/wb8rfs6Wf46Mp0Jh12BHahJit9Q2f4XUM09b+rUKp+DavxzxX+/WozU1CYf2oVJb4Kul3H+Sds39D1nEjAbNZkR02gKF39T7a8QERHDp7ffbwHzBYHzgfMkbWv7UtsHS1qBao6+iKTvUA0O3w58HtgtAfMYNm1SaEXq+/DbXq1y2+4mK9neW9JvgHmBP/QSPCJi+Iwb9AAiYsaStAywUysDgqQPAt+lsskBLgFWBn4PLAOsZvtSSeOp5WZvBy7rBsxj+khaFDgOOBdYArin7yE/BY4F1mvZ6P8lAa7p9kOq5M3xLWv5Udu/s/2jXsC8ZaXvCywJHJr3fOaw/TBwEvBhqm75Ia1sDvBYXfrdgHP6V8DEE2sNqyxpnKQJkubM33ZExNNLJ4ljeeBBYA2qZ8g+kpZtD9ua6imyE3WdcEHbtoftA9rzNZOHHjEjzQ68nCrL+Ej3Dj/eAHelNul0nu0fJGAeMdwSNI8YfncD7wB2lnQUcBSwP3ApVG02KoC4JHAKcIukFwBbUQ1CD291hGMUSJoNuInKcr4QeB5tAqPVesb2Q1Rm+TOAZJNPJ0lTOtZdAnyA+n6cALy2e+En6ZPAkVQm+jtsXz0ThhpNC5yfTC0LX4vaP/Xuu5eq+b82TPXzjSloJboeaRMPJwK/Bq6RdJikNw94eBERMRO1RJrzgRVtn0X1cHkjsK+kpW3/2/Y2wBuofi/vpVaj7t+ePy6TrjFkHqFKriwr6Vn9d0paCdiE6oUUEU8DqWkeMcR6Sy9bmZVLgIWBE21v0u6faHtSq9t8HPCW9tR7qayTI21/qT029bOnk6T1gLfa3qIt/3sPsCdVKutNtq9rj5sV2B1Yh6qjfd1gRjz2dev4S3oxcAfwUKtpPp4qR/QNYD7qJPg3wELAZsBzgANtXzOQwQeq5sPPt31V+7m/rEj2S9Oo9bK4BLidav42K7XvXwz4oO3vDXB4ERExg/T1Bnkm8HXqePDV3opSSasDp1LB9J1t/+GJXitirPlf54+S1qa+A18Fdrd9X9v+LCrxbAlgddu3zazxRsTgJGgeMeRacHYR4BqqweGdwN62j2v3T+gtP2uZhosC/wL+afuytj2BqRHovrft512AXYDlbV/RgrbvoUqAzEI1lPk3Fbz6HLBrb/lrTLu+i8MjqMDgeOAXwF62r55C4Hwj2xe0FQHYfnAggx9S03ORnf3Q9GurKb5CZQ2+l2qEa0n7A58CPmz7yEGOMSIiZoxOMs3q1Eq61ajJ0ov6HtcLnP8M2CW1y2OY9CXUvANYEJjV9hFt29zAju12NvAjKsHpbcDrgDfavnwQY4+ImS9B84inCUkvAeYGvgUY2Nf2se2+WVoDvv8KSiWTZNpJ+tSUgt1tSd93qKyFQ9u2XuB8N+ClVJ3tk4E7bB/UHpPPYDpI+hLwbqqx5NLAm6jJow07kxdLUXU7X0atBrhwUOMdFn2TFi8Frrf9wICH9bTW/tbPAf7P9pZt2/uAbwM72t6/lW5ZyK1pboyOTrAq+/OImGna+f9cwAO2/9xKAf4aeAVwM7CE7funkOjxdqp842+BtW3fPoDhR4yqvoD5N4FXUU3lJwLXAh9rvb3mB94FfIG6fr6TSkDb1vYVAxl8RAxE6oBGDJm+usxzSVqw/Xid7YupeoUCduw1B20B89WpuucTuq+Xi/tp0zKa95e0XGdbr1b5L6lyCNu1Eiy0E7fvUCdll1AnZgfbPqg16ku9yGk0hRrX8wCftb2v7Y2p0jcTgdMkLdU+gyuoOv5/oMpWxHTqBMw3p4Kyr0jDsJlrCu/3RKpXwizt/l7AfOcWMJ8V+CSwelulFNOpTVQ89n3ofC/yXYiIGUrS7tQ55oXAxZK+SGXMvgP4ObA41fhzltbv4rHzJ9tnUwkHJyZgHmOZpFlbaTo6AfNvUPX7Pwa8GDidyiI/VtLytu+0fQywLPAaaoXeuxIwj3j6SdA8Yoj0ZXa+DzgLuFTSZcAXJS1m+xIqcA4VON9D0oeobJJ73dcpPJ48SUdTWeOr2v6DpOcD9L2nx1NlQNZrz5nQTuBOBr5ENf68SNLCKUUx7VoGSa+7/fMlLQ88E/hz7zG2jwL2oVZcnCZpyfYZXAq8zfa1Axj60OibuHsWsDX1933Zk50ASkBx+vUm3CSNV/WtAJgE/B1YTtKnaQFzYL92/5LA26mViHmR/iMAACAASURBVA/P9EEPmd7+XdKcknaUdKikgyS9ctBji4jhJuko4KPAN4FPUPv7nYD9bN8BbEwlcqxHJXOMtz25L3D+/c7KyByXY8yRNA9wPbBR729Y0nupYPgHbP+Majr/IeDzVPLSUZJe1Y7ht9m+0vY/bN8zoF8jIgYo5VkihpCkjYBjqDrNf6aWYL6JCpisYvsmSUsDR1Kz6wK+ZHuvAQ15zJP0ZeDDwEq2L5H0VuAE4E9UFvnVtm+QNAe11PWvttdpzx3fAivjqaye/aj6egvbvnsQv89Y1DdpdCxVe/ARapLiE8DxfUuPPwBsT73Xb7L95/7XjJGTtCrwSupz2Nz2X5/k87qf4wZUf4Wfz7iRDp9eqS1VI9WfU0GTb9v+d9v3/4yaTPqS7R3ac5YAjgIeoib+Hh3Q8IdC5zOYG7iIel9FZXm+CNgbOMr2DQMcZkQMIUmHUAkyawG/afuiBaiVdlsBr7F9Udt2OvAC4GDqmPBot4RFxFjVzoHOoRJF39kLekt6G7Cc7X3aasiDgM2A7wJbUN+Fn1OrVH87kMEPEaUnUYxxyTSPGDKSFqUySQ4APm37QNubUVm1D1N1DWkNTN5DawTUC5hPobRFPAFJBwPbUvXIe0Huq6iu6xOopbE/lbQtVRphd+CNrV4k7QJF7QLlNKq++dcTMH/yumVsJO0FrAwcApwC3EFlkSzZfU6r6X8IcB01oRSjpO2HDqOaS87bC5g/0f6lL2D+CWplxrwzeLhDpWVGTW7lVRag/u63AdaWNE/b928B3Aa8VdIBkg6j+l3MStX0703ixQh1PoNvA/8E1gdWtL0kcAE1kbdwsjdnjG6pufwtx9NJSwj4KHUMvrgXrLJ9G1WmBapUV2/busBfqWD6jp0VkBFj3eLAYsA3bd8j6Vvt2uvXwNFtUvtDwL7Ad21PAs4AbgDeDBzYK6cZ00bSHJIOkfSydj6Uc50YsxIcixg+CwCLAOd2ZtR/QJ0gb2T7aklLS5rX9g22L2m1zjMTPAJt+etGVFb//MChkpaw/Xfbe9leicpAP4fKOD8T2AW4F1ixvUavjEIvcH6c7e16983832rs6ZRkWYFaWrmj7S8COwAfpD6boyQt2/e8w6lJozQ9HEUte/ZzwL+oUiDrt+1TPXHuC5hvTU38fdT292bSsMe8lh34SLsQ/CnwNeBGYFFgf2AdSbO393RVasLo9dQx4wzg1a4eFwmajI5FqObCx1OrjR5qqydeTzXj/j3tXDwXlKOrt6pI0n7UhFGOpfF0cRZwPnXus3Zb4dgzC3AP8G947JhxG9Xw8GaqXNeyRAwHUatN15L0E2AV4Gbb99q+lbo2eBFwfwuYA7wUuJI6Tr/P9kMDGPcwWJ+akPiSpBf1rnMHPaiIkcgJZMTwmZUKGj4IIOlMqjzLGrYvk7QUlRW9ZP8TEzCfNpKOoTJ01rT9IeDrVIDkQEkv6T3O9qm2twFWosokPEIFsbaRtGgnC+g/GsW1f+czeZIkHQCcStVkvgIea/jzc+D9VBmWoyUt032e7X/P5KEOlamdBNs+mZoguhbYQ9VsmCmdOE8hYP5V4CO2j5yhgx8yLUN8VuBcanXRfsBbgLWpUlEHAeu1jPPLqHJQb7S9tu3Pt4D7eKe3xWhZDHgecGWbMNqYyujfpS0Lnxs4TNJzu/v9GLlucFzSV4APUBNHueaJpwXbt1D79muoff66AO38/2tUCZbL22N7pVhup0q5fKCXSBMxlrXzymuATYF3Uk0+t7d9ad9DJwGvkvQKSS+g9ZwCrrB93Uwb8JBxNVHdDVgKODiB8xjLcgIZMUb9j4PObcD9wPqSfgwsQwXML23LlVelAub3zZyRDh+V5aile++hltpjex9qOexLga9JenF7/LiWTf5H21+y/SpgS2pio1fXPCcR02gK79mvqSyqF1Jd7oHHJh7OBzahMk5ObxePMZ36gt1LSnqjpDUkvRCgZTR/jioNte/UAudTCJh/2NWwNabdilR92q/avsB2ryb8esAlPJ5xPq/tRzvZVcBjE00xOq6mSnatKukdVMb5Lu1YAVXzfzlg6QGNb+h0Vh0tTWUZfsL2hZkImvFSBuepowXB3wX8BdhP0s7UuerRwBfh8XOoTuD8VtvfafclRhDD4o1UstLswHslvaJ3h+3rqVJp61G9Xn5BfW92cJp+jljvWGB7XyqhbAkSOI8xLI1AI8agvkDVM4GJtm/u3L8DsA8VlF3D9s8kzUtlGx5ENTb52gCGPlQkLdCWtT7WzLP9+zNUbcirqPIS1+jxpnDdx10A3GP7rYP6HcaqvvdxAeDOduH3ZuBE4HbgM7bP6DxHVNbtl4G1bf9tAEMfSqoaqntTk/HPoi7Uz7C9Y7v/3cAeVEbPLrZ/OIXX+ChVY34L20fPpKEPnfYdOBtY1/aZ3bJbquas36cybz8PnJLVLNNPU2maJ2keqlbqhtQKsO1sH9jueynVsPtW6rPK5zBKVH0tPgDMRjV/u0ApPzdD9R2T16dqaV8z4GE97bVrhNOowOHPgPV7560Rw6h/X6+qYf4HqjTLt4AfArvZvqTzmDdRTevvA062fe3MHfXw6Tv33JkqVXolsLXta7uxjIinuswiR4xBnYD5RlTpicsknSZp1Ta7+2WqfvZswHaSjgaObNv37wXMM9M7Mp3snMcuPFrAdlz79948nnF+qKQXt4D5uO7jgN8Ac7aLmniS+i7O96UymV/dTsB+TgVL5gM+J2nN3vPa9+Zcqm5zAuajRNJawKFUDfLVgecCFwOfbhNI2D6NKtUyF/WdeG7faywEvBX4UALm0+3vwEPUqqJeHfnePucq4HoqA/erVJmoHAumQ29/JGlOSbtJ+pKkbSTN4Wrm/A3gj9RE3rMkvUTSFlTW+RzAe/o+o5h+/6BqNs9H7Y9S6mwG6jsmH0clbXxA0myDHVm0jPP3UD0uXgGsls8lhlknULuXpI1sn237X7a/Ta04XQPYTdLyneecZ3tn219MwHzkuquNusdc23tRcYhknMeYlEzziDFK0luBU6gazv+mGm5Mopr4nNKpn7oWtVT/F8CFtk9tz0/W1QzQN7Peyzj/E7Ct7as6j1uMqvV8rO0PD2SwY1Df+3sKsDxwFHC87Zs6j1sdOAK4Cdjd9pmDGO+wU5V8OoaaoPtoZ+XFz6mmxJvY/mPn8RsC42yfMIXXemzlRjyxqWU3t/s+S02cfsT2EZ3tbwQ+TWWZ/xD4ge2tZsZ4h5mkuYCLgPHAvFTj7WuBVW3fLenV1GTeelTfkb8BlwObuurIT0j5kJHpW3nXPT5sSNX0fxR4v+3zBjjMpwVJ3wZeC2xB1fG/ccBDiqYlZ3yX6ruzDfB92w8MdlQRM4akJagVd/dSWeWndO7bEDiBOgfa1fYfBjPK4dI3eboysDBwC3Bj7/pX0i7A5vxnxnniEfGUl6B5xBjRv4ypZao9H+g1b1sE+BV1Qf5pKnD+iKSJVJLtw53n5gA1A/VduO8AfIoKkrzNremkpJcBG9nepf2cZWrTQNKBVLmh9YHLbd8naRbbD3cyP9egyn1Mok7OfjzIMQ+jFiz8I/Aj21u3bWdRNZp7vRReD8zfX5Ilf/Mj1wuytozBtwMLUaWeTmz3v4gqh7M+9R34OVXvf3vgX1Tw9tfAX2xvNIBfYczrBrol7QG8mgpG3QpsQE1g3wus0ALn81KfwfOoTOhbWqZVAuYj1HeRPhGY1/atnfs3BT5LJRZs3wucZ98z+iRtQNXKfj/w6/a3PT/1vXgA+KvtfwxyjE93LXD+HSrZ4BPAN6c28Rox1kl6C1U2cC4qeebkzn0bUqu9fgVs42qMHiPUd917EvAmaiXd3MDvqQSxQ9v9nwE+AlxGHZevmvKrRjx1TBj0AOKpLxcXg9eXSbUY1ehzBeCmFjiZxfZNklakst32a4892X1N3iDLlGe0TimWybb3kzQn8PdewLw95i9UuYpMYkyjNkH0euAA279t255HlWOZH7hN0idt/7AtFdyLasgXo+8R4J9Udi2SzgSW4vGA+bOprJJLJc1q+6HeE3NcGZkWKHxE0tzUkvvZgXmAOSStA2zWsnd2obKZd6RqSd7bfn43MCd1Dnh9e80c56dBe78eafv2janM8u91sqmOogK1+wEXSVqhs/+/rfM64xIwH5m+gPlXgFcBy0o6FzjH9mG2j2vLv3cGDpC0ne1f5m99+kma2Hd++Uyq4fP/AeNawOpQal+zIHCCpO27kxoxc9m+XdJ61HFjlgTMp1/O3wevf9Vdb99k+1xVLe19gF0l0Quc2z6xJR18BbhzMCMfHp2A+WHAa4APUnXkF6YagR4i6Urbv7C9tyRTE9p7Stqwm9gX8VSU+onxP7UDUS4uBqwTMN+Eqsl8KbAalVVOy66dtZU2WJEKqn+VyviJ6dCrt9b775PlTo1a27vaPmZqr5MT7mn2EJW9sLikF0v6GLXUbxlgEaok0a5t//V9YEWnhvl0mdLfbQscPkhlkawj6WIqYP72FjAfT2VBvwG4phswj5FrqyjmoJq6PQCsCyxH1TJ/D/BdSfPY/j9Xf4XlqPrmqwFvpuqZHwg8Gzi6vWaO809A0qskfRD+4/3ahOpfsQN13O2dNz0EnNy2zw1coGoK+h+y7x+5TsD8ZGoi6NfUBOn8wF6SDmqPOxbYk5pcOlbS6wYy4CEgabykZdu+f1Lb9qk2gfcAdfzdDTiJaj75a+p4/AVgI2CxgQx8yHSPxyM4N72N6uty5KgP7Gmm7et7wcKlJS0v6RWDHtfTTedY8FlJi9ieJGmWdt9PgZ2oBI8vSnpX53nHAItlBczokLQoVZ7rS8AvbP+TKpG5BHWueVFvf2V7H2BXYOcEzGMsSNA8/qfOgegXkg6Y1pOzmD59J8ZvAQ4GfkJdjBvYsXMR/1AncP46qrZqPq/poKoLvFlbPj/NDUumFBBJcGraaMrN8R6kavS/m2qmui3V4HZFYBXgBmDhTubJ/TNhqEOrb6XL0pJWUTVQ6q1W2wn4JRWcPQy4rq2I+QhVGuQIp6b8qGn7oe2Bu4GNXU2rvk6VaNmVumj5lqT5AGz/rWXXXgK8BfgeFUB/q9Pw6gmpzEb1SFi07+6zgM9Q+5h3SpqtTWqMaxeCJ1Pl0pagvhsxiiStT53vbEotv9+HKhEyLzChfW7YPp46f7oDuHlAwx0Gb6DKHXwBQNL3qL4tc7saOB8FvBKYDHzM9qa2f0fVFv77YIY8fDrH47Woffp/NOB7Es9/qD0ncYARavv43jXyN6j+Ur8Gzpd0hKQlBzrApxlJqwGfA74jaeGWTNYNnO9C9ffaQ9IHOk+9Z6YPdoxTNT3fWFXuqevZVPLSn2w/oCpD+hfgR8AnbN8PbCxpOQDbX7Z9zUwdfMQIpTxLTFHfstctqeU1P6MmWrKcbybpnBg/A3g58A1gF9v3Sjqdaua2f1tydkw3cC7pOS0LNEagXWx/ispqvl/Sd9py/JQxmEn69kNLAfcB99v+V5vQ+Bl1cf6PXpkWKpvwLuDGTkZDPq/p0NkPbUotZZ2t3Y6WdEjLKv84FSDflQqWT6Im7nazvX97fr47o2NWqsTHN2zfIOkIajns6tSE0YupkiFHStrS9p3w2D5tVuoiZjtXiah4Au1v9kFJb7N9q6TZgXfYPt329ZJOoMqz7Ebtdz7px0t0PSzpVOrzOndwv8XQehkVCL+wXaS/FPg2lem8ve0HJb3K9u9tHy7p2+6USYtpdg2VsbmZpHdTtYLfTis5ZHu7tgrGbk0mJS1ElYe6k1YOKqZfC3h/nqobvESbrHvCY2z3MVnpMnKdDPPjqGSNHanJ07mo5uiLSfpoVjnONOdSE9hbA6dLWtf2P9s18UO2vy/pSuBZwJaSTrd9d85JR6TXTH47Scf3zjGpEoB3Ac+W9HJqEuknwOa271c1on8vlX0eMaYkaB5T1AlUvYFq6PY9qkZkAuYzWTvInEPVifym7XsBbP9a0u5UkOoASZNtH9sC5+OoYG/q7Y1Au6h4UNLzgQuopWbjJPWaqyb4N4O197i3HzqSKi8xD/AnSZ9pf/+ndD8HSS+mTuaWBj6az2j69GWYL0XVZ96Hat7zYqrExyKSPm/7YirT9j1U/dp/Ade17dkPjaK2b/oecLukZamSK9tR2T2W9O227d1UvfmtO8/7MfATT6HXRUxd+y70ajEfC7xN0vy2j7Z9o6qGeS+IRX/gnDqG/1ft1XjypvLezQXM1S7Ie8frc4At27aNgeUlXW/7lgTMR659B25qWZqXAS+hmrv9qd3fqyN8f+c5q1CrANYAVnathIwR6D/vbPuXnYGjJO1me7dpCZi3LPW7bJ8/Y0c+vCStRK3s+hjVDP1hSS+gVgLfSIKDM8SUjgXt+3AQtcJ6Gypw/m7bN7cEmiWphIGTgN/bvnumD3x47EmtutsXGC/pWNt32P6zpL9STeifQfVO2ABwS/7btG3/04DGHTFiWZYVUyVpc+B8aod3vVNzalD+QS1tfTnwIlXtSABs/wbYnbpQPFTSVm375GSSjFwLPE1oF9ivpbJH9gPeqydZqqV7v6QFnujx8bgWaOpd2B0EvBU4ADiSWnZ/jqQ3dT8HSdsD+1NZb29zlvxNt85nsDTVZO8nwGG2z7Z9MFU/+x1UI5/XtOd8x/ahtk/rBMyV/dDIaCpL7m3f5Fpi/zxqSext7fswjloe+xtgZap0Ufd5jyRgPm30371ddgf+Cnxa0hYAtm+myrfsQU1SfGlKf/cJmI9cZxJ1087m66msto9RvRV+AmzhWo23MLA2dcy4b2aPd9i0/ct4asL0eqok19sl7dnun9TdX7XP6QRqNcCbbF8+gGEPjc7x+AWqhucAlwC/AtZqx+mp1jjvC5h/gkqGmm2GD3y4LdRuf20B8xdR+6HvAtu0JKZVBjrCIaP/XIG6jqStJK0gacF2vD0QOIhaIf8jSctQ568fp3oqnGn7ugENf8xr18CPAFtQ12R7UCVXFm4P+RC10nR24FvUPmZF4MtU/50Pu2qdR4wpyTSP/+VYKmNza6rJ2w9s3zDYIT099C2f/JukT1FLYjegAobf7U1i2P6NpC8C81GlKmIUtIzyWWz/W9IKwO+owDlPlHHed3GyA1U/+H3UMvJ4An582euc1InXJ22f1radQwWtfiJpVdvnq+rqLQPcDqxq++oBDX3oSFqRyha5kWrsc08vMGL7dNUS/dOARyTtafvC/tdIxv/I9C5O2vfgs1Q9zklUWaJTWkbnXdR35A2S/kmVCVkd+HUvgzDZzSPXW/GiKm0z0bWc+0pVLe1Tge1V5dGObBltR1DH4d2pGs4HDXD4Q0fV2+Ub7dh8lO2D2uqWg6nvxYfbPmoxHq/vv4rtBM1HqLtKqO1HLpS0MhUsPJgq1SLbn23flfFUpu3J1ArJv9m+cUDDHyqSVgdOAc6StLvtP0nahQrUfgjY9kmck25DJRh82PY5M3H4Y5qmvFqud476f5IWAC6iVrr0ylGsDnxB0vt7KzJi+nQC5idRSRuzU/GsYyUdZPuPkg6kJkq3Bv4I3Eodl9/hVjYqpl07l3yk/fhBqlzXHFQt+XGSjnOVa9ycKid7BFUS8HpqBcDKtq8YwNAjpptyLRvw38v+ej9LmkjVsN2KqhV2pO0E/maAvpPauYCHqWXHt7dtiwOHU02vtgC+183+VzU+yeztdJhaELzdNx8VOJ8d2IEKWv1X4Lzvc9wa+CqVcfK1Gf8bDA9Jh1CZzLcCm9j+Y+e+lYC9gFcDq9k+r31nnODI6JP0LWrC7i/AW1pwcDwwuR0n1gFOp+oXvtv2LQMc7lDoHIPnAi6mjgfXA8+lLtSvod7ruyV9nqqnfSfVc+QGYMX/NbEXT6wXJGnZ+2cAi1CTcr1j8kuowPlswJdsH9m2L0qtjjm+c4EZI9AfqGrnQV8DZqGOq1erSkcdTDUiPonqpfB8Kunjbd1jR0ybvqzOeYD5bF/fuf+5VGbnCsAxtneRNIHKKpydCswmmWMUtGPuR6j39gbgOVQ5qBOoZqBHABvY/l7f86Z0Tvph20fNxOGPaX3fg9cDt9i+RtLzqKSCm4ClgDOpBrh3q2r570NlPG/cO27EyPR9BhtRjT23Ba6gyn7sQNU1/6Lti9uKi2cDb6LOny7q7rti5CR9B1gW+CEVFF+RKn/zWeo4cJeqCes7qXKNV1CrMRKjiDErQfPoPxDNTu0AH/Dj3dVnoYK1m/D4DjEH/1HUd1K7LnUC8HJqpvwY4Nuu5p6LUifGr6OySn7gvrI5CZKMTN9nsAJV9mBx6iT4Ftt3qJbEXsRUAudTuTjZ0vbRA/iVxqy2z9kY2J4Kfqxj+5xuAKUFzvegTojfaPtXAxvwEJK0BrCA7WPbz1+j9jnHALu7mrF2A+fvAxa2feDABj1k2vt7CpXRuYlbQzFJP6Syydex/YO27V1U0PB24Gttv9RbRhvTqJPlPxuVzfZearXQD4APTSFwPhE4oD8Qlc9g5Pr29/O61SOXtDaVxfxp2we3SY25gC9SqzHmoUqHHOusOhqxvmuDfahyTy8ArgR2Ai63fV8ncP4a4FJq9ctawGszYTFyUzqXb5NGp/J4WZbPA1dT5aIWpcqufmJKwcEEzEem73twDBUsPJ4qTTGJWtHyMeBu4HWuuv8vpRLNVqeya68cyOCHkGqV10upzOUvdD6b7aiM5/OAPW1fMrhRDi9JH6aOtetRq0+tKs2yP3WetCMVs/jXAIcZMfps5/Y0vgHjO//eF/gxlcFwNPD6zn2ztG2TgE9RwZSBj3/YbtTExINUHbCjqVrmk4ETgRe1xzwP+D7wALARbfIrt1H7DDajmhjeQF383U2ttnhFu39e4CrgOiqwO6FtV+c1tqYyPjcf9O8zVm9UNu37qJr+FwALtu3jOo9ZBTgLePmgxztMN2B+6sLjXGDxzvajqRJDhwALtW0Tup9J25Z90rS/57P3v3/AM6kg1La99xhYpx0TPtl7HlU2pP/1xs/I8Q7zrfP+zw1c3r4H36GWeU+myoA8o/P4l1BBrDuBtQY9/mG7AcdR2Zybdo63veX3S/Y9dlz//ii3Eb3n3fOZU6gyKztRpebupJIH3gXM0R6zGLUC4PdUP4VlBv07DMuNmqxYoneMoPq2TKZW2r2AKgX1t7btHmDDKbzGZ4CHqAm/gf9OY/FGXYf9jQoWLtzZPht1jXAdtRrsvPY9+CvtuiG3UfsMtqSukf8JbNp7/zv3b9fOUU8BXjXo8Q7jjWoCegPtmqyzfd52nL6dmkR61qDHmltuo3kb+AByG+CH/58nxSe1A/7OVAbtDe2Av2rnMbNQWc6TqaBgLkym472nL7BENUu6lsocmattm5uqGzYJOLzz2OcDvwA+MujfZZhuVEbhvdTE0NJt217tb37PzucyTzt5vh94ad9rbN0+rwTMn9x7PtXgHpU9uD5wC9WUeEqB89ln5PieDjceDxJ2jwkbtb/v9/c99qh2UXJQ78Kxf1+W2zS//8tRGcyv7Nv+orYveX/7ef22L/pM+3kOKstt9UH/DsN2oyaDzgR+y+MT1nO3i/bbqcD5MzuPX4IK7mayYnQ/hxdQAZLJ1ATpL6gyOcu2Y8KJwPyDHuew3qiyT1cCr24/f7Ltk65vt3cBc7b75qACiPMMetzDcgMWAG5u34F9gEXb9gPaPmhR6nripVTAajKwWd9rqH1Xthn07zNWb+3v/O9UY+Fustn49t+J1KrHvaikgs3pJBzkNmqfwzOBb7a/83N4fBJ1Yucx27T7vwnMOugxD8utc52wN1WOaLH287jOfR9r7/1k4KMkTpTbEN3GEU9btntlJPYEXgG8z/ZeVKbCglQjn2PVOn+7yoB8jDoh+KlTp3B6LGrbbfl9z3xU7bs/2L63bbvX9jFUIH1LVRMsXMv017B9+Ewd9ZCSNK4t8V6DOhE7zvbl7e4XURNIJ9u+V9Ictu+mAl0fsX1V53XeSWXAbeUsf31Cfctet5K0r6RjJK0iae72PfghNRHxUuA0Sc/y43WGcZr6TLfesYDKFuxt+xZVAuErbUl4b/vm1PLwDwH7SJq98/wYmWdS+549JL2is/0+KmCybGusdCJVIm3fdv+rgVWpkmoxuiYCLwZ+ZftaANv3UPWDd6ayP7+hakKM7Sttb+rHGyHGCPS/d7b/yv+zd+dhlpTl3fi/T8+AIkJQwSQKYvTVGBVp3ABFcTRiXEiIGLfEmKive7QVFFRUcI8xpo0xhvx81ajRuGQxbyJRA9NxiUI0HiQub9xYVYQosigC08/vj6fOzJlmYLrP9Eyd7v58rquuPqdOnZ77VE3Xqbrrrvtpt3snyWlpd4F9Ka2H86VpfVQP697rnGYZde3o9k3yf2qtZ5ZSXpiWMHlCkg1pg9O/OslR3XHRT2qtV3fHRyyDWuulacc+/5TW8ubfSylHpVXzb0rr2V+749BHpo1l8a7h+4ctXmqtD6y1GpR4O0opN+3aAy5017SxEj5Ttx5UezhA7jW11n+rtb601vrc2gYp1j97B3Q9ybd6XltbtOemXaC+T5I/LW1Q6GtKG4Mt3f/zZ6e1aPnZro57tVj4fTpynP/BtGPWF3bz50de+2mSD6TdeTEnT8Rq4gBzjSulHJBWyfO67qD4+LQKwmFfqn2SvK+UsiFpifNa6/NqrV/rLegVrpTy3CTnlVIOXXCC/YtplTpXdsvtPvJF9A9pbULuNfw9w8T6wgMLlq770p9Pq14r3YlKSimnJTk8yaNrreeUUo5MckQp5Sa11stqre/plhvuS/89yVFVD/Pt6vrVDhPm70/b39wj7f/4XyU5oZRyi+7/+T+nVY/8UpLTSyn7OhhbXl1P8nO7CxdHdrP/MC1J9fJSyp7DZWutz0irjP6cixY7rtb6r0keluSBSf5wmDivtX4vybvSbjn+yyQn11pf3100ulNatO+fbgAAIABJREFUwurHaduCZdJ9p94s7SL21d289d1J+0/S2qf9V9qFjg+MvG9dkixIqrAEI98JDxyZ91dpJ+pHpd0B88dpfyu3TRvs87huOd8Jy+vKtPX+3m6cl+PSvoc/Wmv9VtpFjLsmOTUtic4OGD2WL6XsVkq5SXeseXl3sfoFaYPRfzRtfd82yfNLKT+XJLXWn9Vav9C9f1hUUJ0jLE6XLB+kXRQdzhse2++bdpGijK7PkeKzo0sph4y8zzrfAV1BzXDd7tUVy9QkqW1sixemXUg6JslsN3bINaWUm3TL/MVoQRNL063/4Xgiv1hKuVspZc9Syk1rG6fi9Wn7ntcNzw1KKbdOu5h9RZLjqz7+rDKS5mtcrfWCtAOw07uTlBemDVz4D7XWj6SdHN4iyT8Pq5zZYd9K63f3r6WU+46cYG9M8o20NiDpDgDWd6/9JK3q8HoVPKo8l25bFQzdwdZP0noEp5TysSR3T6vo/3JXdfX0tArDrd4/PLiotf6oS4CxHSMHZG9LqxR8Qq314WkDTd427fbWF5dS9hmpOH9p2u3fe277t7IDbt39fGKS55RS/iRtgLG/TatmvneSjJyUPKHWemo3zwniEpVSDi+lvH34vNb6ySSPSRvk+Q9LKcMLpK9Pu7srSW5ZSvmtbsCr96e1Lzqmu/jqeG5MC5JVw8rMS9P6oj6zlHLv2gbzHCbFr0y7++hdSe5VSnlLN1+yfBmUUp6aZK6U8u5SyhHd7Hck2S/JI2utb0zyxmy5YPHgUsov9hDqqrGt/Udtd5eeVWv9Qdp39NVJPla3DGx7Wdp+6Itpx66Mabjf6R7/Ztr4IZ9P8o5Syu8kSa3147XWY9OS5/dMq3K+e5K3Lvx9oxeQnCMs2k3Ter//SbJ14jCtvc0BaecDdXhu1p073CXt3OBuw+8S63x8Zes7UF+b1pbr86WUM0opv9RVlv8o7QLe6UkeneTNXeJcZfkOWrD+/zytJdo5aecDryul3D5t0M8/Tmvn+9lSyhlpxX2PSvJW//9ZjZxkrSE3dFJda/1grfWitKTID9O+hIZulnYr7Olp/QsZUynltklSaz0t7Yvmq2knhvftFrk6yduT3KOU8tHuAOC6UspN0wb+2T3J13sIfdUZOTl5cCnl1l2S5Gdp1ZxHlVK+lTa426NqrWeXdtvfMWkJrf+otV7dW/CrSHeh7r5p7Ww+V0p5aZI3pVUT/mda9fmw4vyqtATuvWqt5/UW9CpwA0nuU5O8O61v55eS3D+tqu2stB7+z0laNdvod8noyT6L051wPz7dXUVDtdaPZ0vi/HWllIO7/dIJaVXlj0pL1D4m7STm3t13xHpVtuPp1l3tKsn3SutbPvR3ae1x/qyUcp9uXQ+TJPulXcj7RJL7l1JuueujX7VOT7t4d1SSU0spb0o7cf/vtGOn1Fr/vdY6m1ZpfufurgzGsKCq8F6llIeXUu5dStlzJAl1s7SLdPt3y+2XVmX++Vrrw2ut/91L8KvEyDHpk9PGmLpJ2oDz+yd5T1fROawe/4tsaVFxdVoRDjuo1npFrfXva62Xl1JOTfKxYZFAWlHT36ddxHjMyNtun1Zsdrckn3UstGPK1negfijte+DjSU5Ka0f34SSHLUic/0uSZ6QVGLADuuP50TuAH5l2wfp30sZQeGr3/Na11hel3SH5nbS7ML6T5PBa61f6iB12ujoBjdVNO3/K1gOX3CMtIXKXbD14xp8kuWLk+T5pB2/HJrlZ359hJU9pyah3JtlrZN5DkpyZVt18WDdv37SBZK5MGxT0A0k+0j1/ad+fYzVNadXMl6UlCPfr5v1C2oWLq7qfe3d/Ly/o5r2477hX6pR2kXbdgue3SPKqtFHXH5920e5J3ev7pCVw/zvJ25Ls0/dnWMlTth7kczhozwOTHDmcl9ar9rS0C0T7pFXafi7t7pj5JC/o+3Os9CnJvt3P4eB5eyY5acEyD0u7xfUTSQ4emf/zSQ4cvrebt77vz7RSp2wZxO3maW0ozklLjsyMLPPcbv4P06qb35rky2kVuEm7yPf1GPxwh7bBDbx2YFq7wO+nVd0+Ja2P9sl9x71apmw9qPZfp91BsSlt8O0vJfmV7rV7JflRtx3ekZZAvCTtgkXvn2MlTyPfx3dPSzy9KN3gtt0x6f90+6BbL/x7STc4+ujvMY21DdYveH5C2rnZXyfZrZt3WFqrwPm0i3ind38Pl45+T5uWvO7XZUGOIe284OvZMgDxi5L8LMmF3d/IA9LlL9L6a/+FfdGyrv+Hpg32+egF3xEv7vb770p3TjZ8ffh3YjKt1kml+So2rEpYcOXwr9LasfxrkrOT/EXpBvpMq1q4qpTyH6WUk5O8J23H+aXa+ngyhlLKbdJOxD9Qa72ilLJPktRaT09rN3FOko2llMNruyX8jWkJxEG6HttJnlvbIK0Gu1o+/5NWnXCrJB8trU/299MStH/evTZI8skkv5/kFbXdFm4bLMFIO4/5kf3QP6VdiPhxklfX1qPwmLTbMP+he+s1aReLbpHWHmT3XRz6alNKKbcupdwsyR5dZexbk5xWSnlJ2nr+UFrl2gtq69n/2CSzafuvpG0vxlRKuX+SL5c2rsWw2v+5aQOAjrZqGVacH57kjaWUe3bzL661nlfbXRfD7/brwlhqa2uzR5LPpFUMfj5tP/NHIy1X/iztRPFDaS2jfjXJ19Kqy3dPu1PmnCTX7vIPsMItuA382aWUt5RSPlRKeUop5Q613VH0siRHp7Wm+6O0C93PKKUc3l/kq0fdUmH+jrRimhPSjjufnvad8JlSyi/VWr+Ytk+aSrvYum+SB1cV5mMppTyjlPKgZKtWHrdJqzCfq62KNmkXKH6UVkzwg7Qk+mbdvGGFrirnMYx+j5ZSPlZKOTbtHOC4tKTh+7r1+/kkz0obZPK6tItLn0pyv1rr2f1Ev7J1d3edkeSppZSbd/N+Pu1OrrfWLWOtvTbJ76YV8d0krWXdfUsb++t/kjzbvmjptrX+OwemFc6cWdsYOsNBVt+YLcWUw7vrhvsdx6Ksbn1n7U07Z0qrXntbkluNzHt3WouVx6ZdpX1MWmLqC2lV5zdJuxXqS0kuSKuCvkffn2UlT2lf7KePPH9sWuX4aPXgsOL8p2m3No2+f/dsfZV3amfGuxqntKT4Nitwuv/zx6bdgv/5bKkCLWm3W/5O2i3id7UNxlr3e6dVhTxjZN4zu/3Lr2ZLpedN0xJP/zCy3B3TbsW8Y5Jf7PuzrOQp7RbW/5tWvXxJ2sWgY7v9/YvSEuWfTfJbaQfK5yY5ZeT9N09yt74/x0qeun3Nv6VVqV2a1pIoaUmQt6RdxDt1wXselpYs/FiS+/T9GVbLlJGKqLTqwY+lq1JLa4fwpm47/dmC990yW6qq9kurtvrR6PeDadHbYPS45m/TqtrOTrur5Sfd9/E9FrzneWntoi5Nsn/fn2ElT9n6zqNfSbuj60lJ9ujm3SXtIulfp7VmGVZD79d9H7izYsz1ntb6b1itfL+R156TZH7k+cfSztkO7p4fknb+cMe+P8dqmTJSYZ52cfT7aW0Zk3YH5LO7/dEHFyw7/DtxPjD+ui9peYnhMdEz0saU2i2tLch+aYUDFyV52sj73t+95+IsOGc27fD6v3n32u+kJcEfPrL8sLL/Dt17ntT3ZzCZduWkWnL1OjrtivhwVOP7pPUDOz4tMfXptAOypN1+f35tI6+/v1vu/kkeWmv98i6PfHU5PFv3gv+FtMqF40opd0+2qjj/cpIzhj3Ou2rma9Ndxe2qIfStXYJSynvTKtV2757fvZSy9/D12vp1/lNateeBaRXn+9XmK7XW99VaP1G7UcBtg8Xr1vMg7XbvD3XzHpg2dsI7k3yqtkrPklax84m0ypHnd8u9PO2W8B9X/WrH1lUwn5B2Yei4tHV/Rdo2eXTaLcb3TLv19a1pLaE+keQhpZRDkzbwYe36FLrLYmzXprW++Wravv5PSynPr+3ultemnQj+VtdLNclWFee/ltY6hzGVUjaUUv4+aQMcllJuWkr5dNodLxfXrkqt1nphWqu6P07y7GHFeeeK2qqufj2tfdeDkzxo+P3A4tUtFc6npPXwPzbJEbXWO6bt+2+V1sv5jiPv+dO0asO7d9uJJSilrCul7J9cb6DC26YlQr5Qa/1p17P/s2n7q/9d252mTyul7FVrvaT7PrjeoPRsX3ds+d9JHpF2fvDa7g6kpBXP/LiU8sJSymlp7Vp+o7ZxdfZI+x7Ya5u/mLHULRXmd0/r0f+GtOOf1HYH5F+nnTf/epL3jvQ4H/b6V90/pm4f9Lm0QYS/nXb8+bQkm2qt/1xrvSTtb+SKtL7mQ99Lu4jxhbRkL2O4gfX/+9052WfSiml+v7tbPrXWa7q33iGtyOM7uzxo6FPfWXvTzpmSHJF2kn6v7vkxaZXMw+e/nNaj88PpelmltWK5VR/xrsYprbLwa0netmD+M9Ku0v512snfcP5D0k5UNmWk+sQ09vr/y7T2Hr/aPb9rt97/KCO95bvXbjayXf4pXZ/I6BE57rq/edpB2L8kOaCb95Ju/Z6b5Le6eaPVhvfqlv9Z2gHZ5gor09jb4aVpJxhHZaRnYbd9Tui2xxlJfjGtivbJaXcazXfTq6Jn9nJuj4PSTgBflpYovy7J87rXbp120vLDXL/i/L62ww6t9wdky0Dbw2qpO6fdWj+fNuDnTRbsj26b1iptPsm7F/y+26f11/6lvj/bSpnS7iZ6bNqgec8cmX9a2u3e6xas/2d3fwuv6Z77/79j63/PtDsfPzL8/h15bbrbF21IaxHyw7Sk1LDq8IHddnpA359jJU9prW1Kttyt8rBuvW/s9vG7pbXOnO++t+85su1+Ny1B+L/7/hwredrWfmjkWOjbSY7q5o2OvzOsOP9x2vmB3s3Ltz1+OW38hNePHBM9d2Tf84dp1f/DsRVuldZi9g9yI+NhmHZo/T+/e+3JaXmkdye5bzfvl5L8n7RBit0BbFpTU+8BmHbShm2VtRcmeWX3/PFpJ+u/2J3w/bA7URl+MR2dlrC6ex/xrsYpyfok/5UuaZ6tb+17VrYkzg8amf/QtET7s/uOfyVP2dIHcsPIvJt3BwZXp1WT7L3gPTdLaxEyn+QrSW7a9+dYiVO3nr+Ulvi+04LXPt6t37/PloGuRhMlB6ZVbx4bt9/v6Ha4ZVpC/LVJbrJwXXfPn9ttjzd3z9el3R472/2d2A8tz7YYTZS8utvPHJrk1LQWaX/QvTZMnP9Pkrdv4/dIHC593T+gW8dvTndL/chr02ljKFyd5NHbeO9tu230uXQJr5HX3Ja/+G2wV7YMKPyzbp9zZre//2yS/zuy7Ohx0r8k+c++41/pU7f+v5TW8uYPt3Hsc2C3T/pst+/5wPD4J13Ff/ddsl/fn2U1TN337LDdza+lJao+lZbA2ivtDr1L0lps/u9uH3RZkpeN/A4FHUtf79vaD/179108HODzLdkySPfo/n7vtDv1vpvktn1/ltUwjRwTvSitmOaBaRe2r0nynO61g9ISt3PdtvlIWpJXi6Kdt/6vTbtIdIu0sS2u7r4Xvpl2l+QlSab7jt9k2tVT7wGYlnFjbjkIW5d2Nf3zST7Yzfu5tMqFuW7n9/5sqTDfL+2W/U+k6+lsGnsb/OqC519O8qfd44Un3aOJ87uNzJcs3LFtcGpaRc6Dh+t95LVD0yoUNqUlE39udL2nnaQfF73axl33e6dV61yXVh3y2uEJyMgyH+/W/0y2XLRTMbL82+Lu3cHvb23jtdH90J8nuSpdJc/I/Dv3/RlW8pTkf6UlXYcn4MP+/Q9Nuyj3G2kX6t6T6yfO39J9N7y478+xkqe0O+6uSWu3MjzeGZ4oDsevuGtaFe2VSR65jd+x38ixlUT50rfB8DvhE0mOTEvQviStiONzacUb38lIb9p0ifO0u8UGcbFoR9b/zdL6wH+y+04Yrtt1C5Z7crfPuSjJr3Xz7plWVfg/MabFjmyDt6UlxV+SdkfdTRe8/sjumOmzSe6UVnjw9iT/0R1HfTjJk0eWtx9a+ja4sf3Q6d3zT6UlZH8zW+5IWpg4v0Xfn2WlTml3c91yZN0Oj4kOS/LFtLsp9h45JhrehbchLWH7nW5fdlAf8a/0aQnr/71pF5WG4+7cI63Q7J1JTkzyv/r+LCZTH1PvAZiWYSO2W/d+La3PVEl361iSV6b1T71FWtXzM9NaHnw/reJ8Ku2E8d1pVw4dFO/Ydnhkd9Jx8sh2+VaSPx5Zpgy/sLrnf9C950NJDlnw+1SSLH0bPLRbn+9buA7TetR+ofs7eWVa4vaPkhzQnaT8TtpFpTuNvMc2WPy63zutN95pSW6XVk3+gySvS5ccH1n239KSVM/LlsS5E8Hl3R6Hduv44d3zbSae0gZk3ZRuoKWFy9kuY637Q7r90DeS/FWSwxa8/g9JvtQ93jttQMnRxPkvpPXalizc8W3wpmwZtG14knjPtATKMDl4cNoF082J84X7ft8FY22DPdMSHZ8YHnN28/dI8qdp7Q5O6vY/Hxk9BkqrcP7XtKS6dgjjb4MXpyVfD9rW/+GMVI8n+b204pqL0o5dv5J256M2aeOv/6Oypd3Zud3Pz3fHo/dP1xIz7Y6Y65J8OiMD4GZk8OHuue/jpW+D7e2H5rvjpf3TLtKdl9bS9HqJc9MObYOLu2Oi2SyoVE4rHvt69/jm2zgm+rnh1PdnWYnTGOv/3Rm5cGEymQwEuuJ1Azb8Tdqgnl9MawfynlLKU5LcJa1Kaq/aBjv5h7QDhN3TKhrOTvtiekBahfRXdv0nWFU+k1bR9rJSyivSBohZl2TzgEm1uWbk+VvTWiQ8Jm1bZXTZXRH0KvOFtJORx5VSTh6uw1LKm9NuNzup1vrtbpmXpVWVn5FWafLnSU6rtX5j+Mtsg8UppaxLawd1UZKn11rPT/s//e9pA/u8tJRy8+HytdYj0/ZXr0vye90AYwZYXV7fTqsgf3zSBrzqvi+SbP7uSK31X9P2UfsNlxv9JbbL0nR/C4/snt4y7UTvs6WUU0spT+7mv6pb9om1Dah3Ylp1zxtLKS+ptX6/1von3TZbv6s/w0rXbYOju6c/qW1ww6naBh6+Z9p39elplYWptZ6d1tf200neX0o5duG+33fBWH43rYLz7Frr92obRHX3WutP07bBVFpV4ZPSBiV+ZynlFaWUZyf5/9IGjX5VrfXanuJfDe6V5LJa6zmj/4dLKU8rpXw4bfD500opd621vjstWfiCtLtRX5x2bnB2H4GvEv+RdsfdVd3j30k7Vvq9tLvuvlRK+ZO0hPkT0wbFPbmU8pAkqbX+MN1gkwaiH9uN7Yc+nVZtvqm2wYUfldba8S1Jfq1bzr5/xz0p7RjzwCS/kuSsUsofl1Ie3b3+siTXlFKeXWu9Mu2Y6H1J3lBKeXGSa2qtP65tcFaWbqnr/4S0Y9LXl1KOL6XcLNly3gBrUfFdsPKVUvZNq8q5d9pgkndKuw3zR2n9y9+Q5PW11itKKXum3f79u2ktXL6c5DO11gt6CH3VKaXslVbF/MK0xOxD026J+nRalflP0vqDJa26YTjo4X/VWjfu8oBXoQXb4Pi0ga2emdam4rQFyz4s7eTlJ0nmaq3v7eYXB8pLU0o5OMmltdaLugTVfCllt7Rbi++X1mf+dd0B2fA9p6fdevmsJH9pnS+fUsoeadUjD0q7kPGRbn5ZkDw5JC2B+Lu11n/qI9bVppRyi7T2Qy9N+/4dpO1n7pN2sfojaXcZfbrW+pzuPfultZa6VZIH+VvYMaWUfdJuv39RklfXWl/ZJcw/nXYy/oJa609G/x5KKQelVVj9qNb6qz2Fvmp0fwcnpm2Dk9MG9ZzvXntX2vHqIbXW/ymlHJWWXPzltP7N56b1tT2nh9BXhe6C2wfTqmuPThvL6E5prT82ZMt6vktaFeKGWuu5fcS6mpVSfi7tu+BFad8Lb0urcv7NtD7CR6Xd8fiptFYI+6TdZfF7tdbv9hHzarKI/dCD0/ZDP+zm7Z9WZPbLSR5Xa/1YH3GvJiPHRCemFSj9Z1pBzR26x3+Vdkz0nVrrU7r33Dpt8OIjk9yl1vqjHkJfFax/2HGS5qtUKeUOabd9n5LWr+pdaQmry2/0jeywUsreSV6R9gW0W9otrrun9ZZcl1ZdtVv3c4+0SqrXde+dUkmy47pt8Mq0bbA+rUXFxxcsU2qtdRtJRNtgGZRS1nWVndtLnP9zkuNqrV/vKdRVq5Ryt7S+wd9Mu8viY9384UWN9UmekeQ5SZ5Yax30F+3q0iVKXp528e55aZWb+6fdjXRNkocl+WmS+w0rObtE7+XdtnHhbgeNfBe/MG3f8/i0QQ5fUGv9yYJlb5VW7blXku/6DlgeC7bBybXWV3V34r0kyUNrrZ8ppexWa722+/+/d1pBwY9rrVf0F/nqUEq5b1o7kI1pSfIj0o6J3pd2x8sVae0dP5Dkr2utT+8p1FWt+zs4OS1x9dpa68tHXjsw7bvh95LcMe1C90yt9U93eaCr1CL3Q5uP/btt8t4kTx29+5TxdcdEJ6Xd4fvctBaOd0prk3l5WruimyZ5YK31M9179k1rz/W9XoJeRax/2DFu+11FFiQBv93N+80kf5fkKUnmSymvq7VeOZLQcmK+zGqtl5dSXp1WvXxCWq/Uk9IG5ds9LWF+TVrl+S1HK0mcqC+Pbhu8Km0bvCStX+HHFyxTR3+OzLcNlkG3f1nXJUN+Ky1x/rQkGe6HuuUeeWO/h/HVWr9SSnlM2nfAW0opd03y5i4pe7u0Crc3JnmFhPnyqrX+uNsHTaW1Rdu31npyV1F7eFrrov3TKj2H77ksceFuuXTfA6ekJWGfm3bx6Pm11qtHl+v+Fs5I8u+11t/t5tkGy2DkuzhpbSeOSusp/+SRRNW13bKXpSV2WSa11rNKKUemDeh5UNr/8z9P8oXhhaNSyr+mjWu0R2+BrnLd38HJafuil5VSNtVaT+5ePr/Wel4p5czaWnLdo9b65cRdj8tlEfuhrVrfdNvjwXVBuzrG1x0TvTqteOzP0o6JXlVKuV+Shyf5UpLpJJeOvOfSbf4ylsz6hx2j0nwVK6Ws7w7AhpWe90m7Lfxlo5We7BwLKg1fU2t9RdcPbN3wQGyk4tMJ+k6woLrklFrrKT2HtOZso+LcfmgXK6UcnlY1dYe0dlA/SDtw3iPJn9da/7Bbzgn6MluwD3pNrfUVI6/tXmu9xv5/5+oqmE/opq2+B7qKwvenVTjfq46MOcLyKa1t2svS7ro4rdZ6bM8hrSml9aTdq9Z68YL5JS2Z/r4k76m1vsn3wM5zY8ekC4uZfC8sP/uh/o3cCfyCXP+ui32GxQPsHNY/jEel+SrWJcxHKz0/nuQRSV6TRLJqJxupNEySk0op19RaX5N2C/hwmfnRnyyvBdUlryylzNdaX91rUGvMNirO7Yd2sVrr50opD0yrcH5AWoLwP5KcM3IbphP0nWDBPuikbh90cvf82oUVbiy/WutlpZTXp93l9cpSSmqtp3QV5n+T9vcw3e2j1qsuXH61janzhrQ77F5USnlFrfVV23sfy6OrKh9Wlu8+cnFon7SWITdPu6ht0Nud6MaOSWutm7qfw7sgfS8sM/uh/o3cAVZz/bsutJDdyax/GI+k+Sq3IGF1VJJfqLVe0ndca8XIAfJ8kleVUm5eaz2x77jWkgXb4JRSyp62wa5lP9S/2tpA/W03bUXidudakCh5eXeS8mrJqV1nwTZ4RXcn2H2S/FySgyXMd77u4sVr0+5yObkrqHURexcbJsxLKRvS+mj/etrgw+f1Gdda4Zi0X/ZD/dvG9/F1tdbXOA7dNax/WDpJ8zVgJGF1XZIL+45nrem+nF6TVsnz3e0tz/KzDfpnP9S/G7rtXvJ259tGouSiWus7+45rLRnZBpuSvCjJ1yNhvkst2AanlFJ+Vmt9Y99xrSWllN2TfCLJLZJcleQBtdb/6jeqtcUxab/sh/q3YBu8qrsb2zbYRax/WBo9zWEXWXBLLD2wDYA+dRXOT0ryF5K0/Sil3CLJsUne3bWxkzDfxbq/g+OTfKDW+tW+41lrSimHJrl3kr+vI4PRs2s5Ju2X/VD/bIN+Wf+wOJLmAAC7mGRt/2yD/hhHoV8G/AT7oUlgG/TL+oftkzQHAAAAAIDOVN8B7KhSymNKKW8tpXy6lHJ5KaWWUt7Xd1wAAAAAAKw8q2Eg0JOSHJzkyrTB5e7SbzgAAAAAAKxUK77SPMkLktw5yd5JntVzLAAAAAAArGArvtK81rpx+LiU0mcoAAAAAACscKuh0hwAAAAAAJaFpDkAAAAAAHQkzQEAAAAAoLPie5ovhwc96EG17xjWstnZ2STJzMxMz5GsXbZBv6z//tkG/bMN+mcb9Mv6759t0L/Z2dlMT0/3HcaaNxgM/B30xH6of7bBZJibm1uNgxauqtzjhg0blrT8xo0bt7/Q0uz0/yMqzQEAAAAAoCNpDgAAAAAAHUlzAAAAAADo6GkOAADARNBPu1/Dfs4AsNat+KR5KeWYJMd0T3+h+3l4KeXd3eNLa63H7/LAAAAAWJLp6enMzc31HcaaNhgM+g4BAHq34pPmSaaTPHnBvDt0U5Kcl0TSHAAAAACA7VrxPc1rrSfXWsuNTLfvO0YAAAAAgLXmdre7Xd8hjGXFJ80BAAAAAJg8559/ft8hjEXSHAAAAAAAOpLmAAAAAADQkTQHAAAAAIDO+r4DAAAAgCQZDAaZmZnpO4w1a3Z2tu8QAGAiSJoDAAAwEaanpzM3N9d3GGvaYDDoOwQAVpH999+/7xDGImkOAAAAAMCSHHDAAZmamrreVErZ/PPxj39832GORdIcAACAiaA9S7+0ZwFgKS644ILtLvPyl788Gzdu3AXRLC9JcwAAAACAFeKKK67IiSeemG9+85tbzS9tCxZbAAAgAElEQVSl3OjzG7Ot99Zal/x7VgtJcwAAACaCnub909McYPJ9+9vfzle/+tW+w1jVpvoOAAAAAACAxbnqqqv6DmHVU2kOAAAAALBCHHrooXnWs56VH/zgB0m2bp9SStk8LZw3uuzC5a677rp84AMf2FUfYeJJmgMAADARDATaLwOBAqwM69aty2Mf+9hl/Z3ao21N0hwAAICJoKd5//Q0B1ibjjzyyLz+9a/P5Zdfnlrr5kFAh4+Hz+fn5/PmN7+5z1B3CUlzAAAAAIA1rJSSww47bFHLroWkuYFAAQAAAACgI2kOAAAAAAAd7VkAAACYCAYC7ZeBQAGgkTQHAABgIhgItH8GAgUA7VkAAAAAAGAzSXMAAAAAAOhImgMAAAAAQEdPcwAAACaCgUD7ZSBQAGgkzQEAAJgIBgLtn4FAAUB7FgAAAAAA2EzSHAAAAAAAOpLmAAAAAADQkTQHAAAAAICOgUABAACYCIPBIDMzM32HsWbNzs72HQIATARJcwAAACbC9PR05ubm+g5jTRsMBn2HAAC9054FAAAAAAA6kuYAAAAAANCRNAcAAAAAgI6kOQAAAAAAy+7AAw/sO4SxSJoDAAAAALDszjvvvL5DGIukOQAAAAAAy2733XfvO4SxSJoDAAAAALDsHvSgB/UdwljW9x0AAAAAAAArx8EHH5wjjjhiu8sddthhuyCa5SdpDgAAAADAop199tk5++yzt7vc2972tmzcuHEXRLS8JM0BAAAAAFi029zmNrn3ve+ddevWbTW/lLLV84c+9KG7MqxlI2kOAAAAAMCiffe7380//uM/bne5v/u7v1NpDgAAAOMaDAaZmZnpO4w1a3Z2tu8QAGAiSJoDAAAwEaanpzM3N9d3GGvaYDDoOwQA6N1U3wEAAAAAAMCkkDQHAAAAAGDZHXDAAX2HMBbtWQAAAJgIepr3S09zAJbbBRdc0HcIY5E0BwAAYCLoad4/Pc0BQHsWAAAAAADYTKU5AAAAE0F7ln5pzwIAjaQ5AAAAE0F7lv5pzwIA2rMAAAAAAMBmkuYAAAAAANCRNAcAAAAAgI6e5gAAAAAALMrU1FTuc5/75Mgjj8z69euz2267Zf36lmYupaTWuvnxXe961z5DHZukOQAAAAAAizI/P58zzzwzZ5555qKW37hx406OaPlpzwIAAAAAwLI74IAD+g5hLJLmAAAAAAAsu4suuqjvEMaiPQsAAAAAwArxne98J095ylP6DmNR5ufn+w5hLJLmAAAATITBYJCZmZm+w1izZmdn+w4BgEUopfQdwqonaQ4AAMBEmJ6eztzcXN9hrGmDwaDvEADYjosvvrjvEFY9SXMAAAAAgBXi0EMPzVvf+tZcccUVm+eVUjZPi3m+2PeMqrUmSZ7znOfsvA83ISTNAQAAAABWkLvf/e59h7AoJ554Yt8hjEXSHAAAgImgp3m/9DQHYLm94Q1vyMMe9rC+w1iyqb4DAAAAAACASSFpDgAAAAAAHe1ZAAAAmAjT09OZm5vrO4w1bTAY9B0CAPROpTkAAAAAAHQkzQEAAAAAoCNpDgAAAAAAHUlzAAAAAACW3YMf/OC+QxiLpDkAAAAAAMvujDPO6DuEsUiaAwAAAABAR9IcAAAAAAA66/sOAAAAAJJkMBhkZmam7zDWrNnZ2b5DAICJIGkOAADARJiens7c3FzfYaxpg8Gg7xAAoHfaswAAAAAAsOye8pSn9B3CWFSaAwAAMBG0Z+mX9iwALLd3vvOdedKTntR3GEsmaQ4AAMBE0J6lf9qzAID2LAAAAAAAsJmkOQAAAAAAy+7444/vO4SxSJoDAAAAALDs3vSmN/Udwlj0NAcAAGAiGAi0XwYCBWC5HXDAAX2HMBaV5gAAAAAALLsLLrig7xDGotIcAACAiTA9PZ25ubm+w1jTBoNB3yEAQO9UmgMAAAAAQEfSHAAAAAAAOtqzAAAAMBEMBNovA4ECQCNpDgAAwETQ07x/epoDgPYsAAAAAACwmaQ5AAAAAAB0JM0BAAAAAKAjaQ4AAAAAAB1JcwAAAAAA6KzvOwAAAAAAAFaHdevWZWpqKvPz83nBC17QdzhjUWkOAAAAAMCy2LRpU6699tps2rQpb3rTm/oOZyyS5gAAAAAA0JE0BwAAAACAjp7mAAAAAAAs2oEHHphHPOIRKaWklJKpqamUUrJu3brNz6empnLIIYf0HepYJM0BAAAAAFi08847L29/+9sXtezGjRt3cjTLT9IcAACAiTAYDDIzM9N3GGvW7Oxs3yEAwESQNAcAAGAiTE9PZ25uru8w1rTBYNB3CADQO0lzAAAAAAAWbbfddssRRxyR9evXZ7fddstuu+2WqamprXqal1LykIc8pO9QxyJpDgAAAADAol177bWL6lX+N3/zNyuyp/lU3wEAAAAAAMCkkDQHAAAAAICOpDkAAAAAAHQkzQEAAAAAoGMgUAAAAACAFeTb3/52rrjiitRak2Srn8PHo88XzlvKsgvnLcXU1Mqs2ZY0BwAAAABYIc4666yccMIJfYexKIccckjfIYxF0hwAAAAAYIU4+OCD84hHPCIXXnjh5nmllM0/h49Hn+/IvIW//1Of+tSiY/3iF7+4xE83GSTNAQAAAABWiJvc5CY5+uijc/nll2/VOmXcVi2Lef/o46UkzVcqSXMAAAAAgBViJbVnWakkzQEAAAAAVojp6ekcc8wxueiii5LccEuW4eOFP7fXymV77/vkJz+50z7bpJA0BwAAAABYIXbfffc8//nP7+3fP/3003Of+9wn97///TM1NZVSylY/h4+T5KCDDuotzh0haQ4AAAAAwKLMz8/nzDPPzJlnnrmo5Tdu3LiTI1p+kuYAAAAAAGtQrTXz8/NJWjJ8dLDQ4TR8ffTxaidpDgAAAACwQnz961/Ps571rL7DWNWm+g4AAAAAAIDFudWtbpX169VC70zWLgAAAADACrHffvvlk5/8ZG///oYNG3r7t3cVleYAAAAAANCRNAcAAAAAgI72LAAAAEyEwWCQmZmZvsNYs2ZnZ/sOAQAmgqQ5AAAAE2F6ejpzc3N9h7GmDQaDvkMAYBG++c1v5vLLL0+tdfO8WuvmaeG84eOlLjv6c3TZ1U7SHAAAAABghTjrrLNywgkn9B3Gouy55559hzAWSXMAAAAAgBVieno6j3rUo3LhhRemlJIkW/0cPh59vnDeUpZd+Hwpd4VdddVVS/twE0LSHAAAAABghdh9991z3HHH9fbvLyVpfre73W3nBbITTfUdAAAAAAAAq89XvvKVvkMYi6Q5AAAAAAB0JM0BAAAAAKCjpzkAAAATYTAYZGZmpu8w1qzZ2dm+QwCAiSBpDgAAwESYnp5e0uBiLL/BYNB3CADQO+1ZAAAAAACgI2kOAAAAAAAdSXMAAAAAAOhImgMAAAAAQEfSHAAAAAAAOuv7DgAAAAAAgJVhamoqxxxzTJ7whCdkamoqpZRMTU1t9biUkiTZfffde452PJLmAAAAAAAs2p577pl999237zB2GklzAAAAJsJgMMjMzEzfYaxZs7OzfYcAwAowPz+f9773vXnve9+73WUf8pCH5KSTTtoFUS0vSXMAAAAmwvT0dObm5voOY00bDAZ9hwDAKnL++ef3HcJYDAQKAAAAAMCyu+lNb9p3CGORNAcAAAAAYNmdc845fYcwFklzAAAAAADoSJoDAAAAAEBH0hwAAAAAADqS5gAAAAAA0JE0BwAAAACAjqQ5AAAAAAB0JM0BAAAAAFh2P//zP993CGORNAcAAAAAYNldfPHFfYcwFklzAAAAAADoSJoDAAAAALDsbne72/UdwlgkzQEAAAAAWHbnn39+3yGMZX3fAQAAAECSDAaDzMzM9B3GmjU7O9t3CAAwESTNAQAAmAjT09OZm5vrO4w1bTAY9B0CAPROexYAAAAAAOhImgMAAAAAQEd7FgAAACaCnub90tMcABpJcwAAACaCnub909McALRnAQAAAACAzSTNAQAAAACgoz0LAAAAE0FP837paQ4AjaQ5AAAAE0FP8/7paQ4A2rMAAAAAALAI8/PzfYewS6g0BwAAAABYw7761a/mOc95zrL/3qmplVmzvTKjBgAAAABgWey999475feu1Mp0leYAAAAAAGvY/vvvn40bNy5q2Q0bNuzkaPqn0hwAAAAAgGX3tKc9re8QxqLSHAAAgIkwGAwyMzPTdxhr1uzsbN8hALDKvOMd78hv//Zv9x3GkkmaAwAAMBGmp6czNzfXdxhr2mAw6DsEAOidpDkAAAAAAIt2m9vcJkcccUTWrVuXdevWpZSSJJt/Dh155JF9hLfDJM0BAAAAAFi07373u/nQhz603eXe8573LHqA0UkiaQ4AAMBE0NO8X3qaA0AjaQ4AAMBE0NO8f3qaA4CkOQAAABNCpXm/VJoDQDPVdwAAAAAAADApVJoDAAAwEbRn6Z/2LACg0hwAAAAAADZTaQ4AQKanp5NEhWfPrP/+2QYAwKT7/ve/nyc84Ql9h7GqSZoDALD5dnwD8PVjOPie9d8f26B/s7Ozmy/gAQA37Morr+w7hFVP0hwAAJXmE8L6759tAABMuj333LPvEFY9SXMAAFSa90yVc/9sg/6pNAeAxfnJT37SdwirnqQ5AAAAAMAKccc73jEbN27s7d/fsGFDb//2rjLVdwAAAAAAADApJM0BAAAAAKAjaQ4AAAAAAB1JcwAAAAAA6EiaAwAAAABAZ33fAQAAAECSDAaDzMzM9B3GmjU7O9t3CAAwESTNAQAAmAjT09OZm5vrO4w1bTAY9B0CAPROexYAAAAAAOhImgMAAAAAsOwe9rCH9R3CWLRnAQAAYCLoad4vPc0BWG4f//jHc+KJJ/YdxpJJmgMAADAR9DTvn57mACBpDgAAwIRQad4vleYA0EiaAwAAMBFUmvdPpTnA5Pvxj3+c448/Pt/85jf7DmXVMhAoAAAAAMAKcf7550uY72QqzQEAAAAAVoiDDjooGzduTK01SVJr3fx49Pm25g0f78iyj370o3fOB5sgkuYAAAAAACtMKWWrnywf7VkAAAAAAKCj0hwAAAAAYAWqtWZ+fv56j+fn57dqvTKcluv11U7SHAAAAABghTjnnHPyvOc9r+8wVjXtWQAAAAAAVohLLrmk7xBWPZXmAAAAAAArxOGHH54jjzwy559/fpLrDwR6Y8+XY9mvfe1rY0a+ckiaAwAAAACsEHvssUdOPvnkZf+911xzTa699trMz89nfn4+mzZt2mq67rrrsmnTpjz1qU/d5vunpqaybt26lFIyNTWVUkqe8YxnLHucu4KkOQAAAADAGnbWWWflhBNO2KHfMUy2j5qdnc1v/MZv7NDv7YOkOQAAAADAGnbnO985d7rTnXLhhRdurhgf/Tl8vG7dulxwwQV9h7vTSZoDAAAAAKxh++yzT/7yL/9yUctu2LBhJ0fTv6m+AwAAAAAAgEkhaQ4AAAAAAB3tWQAAAJgIg8EgMzMzfYexZs3OzvYdAgBMBElzAAAAJsL09HTm5ub6DmNNGwwGfYcAAL3TngUAAAAAADqS5gAAAAAA0JE0BwAAAACAjqQ5AAAAAADL7tBDD+07hLEYCBQAAICJMBgMMjMz03cYa9bs7GzfIQCwypx55pl9hzAWSXMAAAAmwvT0dObm5voOY00bDAZ9hwAAvdOeBQAAAAAAOirNAQAAmAjas/RLexaAleGKK67Iy172snzrW99KkpRSrrdMKWXz/IWvj84ffW1by9/Q48W6/e1vv+T3TAJJcwAAACaC9iz9054FYPJ97WtfyznnnNN3GIty7rnn9h3CWLRnAQAAAABYIaampHR3NpXmAAAAAAArxL3vfe+8613vylVXXZVa6zaXuaH5N7Ts6DQ/P58kmZ+f32re8PFJJ520LJ9jkkmaAwAAAACsICu1V/hKoZYfAAAAAIBl9+u//ut9hzAWSXMAAAAAAJbdP/7jP/YdwlgkzQEAAAAAoKOnOQAAAADACnLppZfm6quv3mpeKWXR7y+lbB4sdOH7Rp8Pl1nKwKKrgaQ5AAAAAMAK8Z//+Z857rjj+g5jVZM0BwAAAABYIX7lV34l97vf/XLuuecm2bpqfDFGK8wXvm8xr1188cVJksc97nHZtGnTVtP8/PxWjx/1qEeN9Rn7JmkOAADARBgMBpmZmek7jDVrdna27xAAWIQ99tgjr33ta3v79zds2JAk+eAHP7jdZS+66KKceuqpOzukZSdpDgAAwESYnp7O3Nxc32GsaYPBoO8QAFiETZs23WAl+I3N3957Rl+/sdcW65JLLlnyeyaBpDkAAAAAwArx5S9/Oc9//vP7DmNRfvSjH/Udwlim+g4AAAAAAIDFuf3tb5873/nOmZqaytTUVNatW7f58Q1NpZQbnLg+leYAAABMBD3N+6WnOcDKsPfee/faJ3zY03w1kzQHAABgIuhp3j89zQFAexYAAAAAANhM0hwAAAAAADraswAAAAAArBDnnntufv/3f7/vMFY1SXMAAAAmgoFA+2UgUICVYd26dX2HsOpJmgMAADARDATaPwOBAky+Aw44IBs3buzt39+wYUNv//auImkOAAAAALBG1Vq3mubn55Mk8/Pz13ut1tpztLuGpDkAAAAAwArx//7f/8szn/nMvsNY1ab6DgAAAAAAgMW55S1vmakpad2dSaU5AAAAAMAKsd9+++X000/v7d9fCz3NXZIAAAAAAICOpDkAAAAAAHS0ZwEAAAAAWCE2bdqUD3/4w/ne976XJKm1bv45fDz6fOG8pSw7uvw4bne72431vr5JmgMAAAAArBBnnXVWTj311L7DWJTb3OY2fYcwFklzAAAAJsJgMMjMzEzfYaxZs7OzfYcAwCIcdthhOfnkk/PDH/4wpZTN09Do82393NFlTznllEXH+vnPf36cj9g7SXMAAAAmwvT0dObm5voOY00bDAZ9hwDAdpRScuSRR/b27y8lab5SGQgUAAAAAAA6kuYAAAAAANCRNAcAAAAAgI6e5gAAAEwEA4H2y0CgANBImgMAADARDATaPwOBAoCkOQAAABNCpXm/VJoDQCNpDgAAwERQad4/leYALKe73e1ufYcwFklzAAAAJoJK836pNAdguX3lK1/pO4SxTPUdAAAAAAAATAqV5gAAAEwE7Vn6pz0LAKg0BwAAAACAzSTNAQAAAACgI2kOAAAAAAAdSXMAAAAAAJbdE5/4xL5DGIuBQAEAAAAAWJJ99903U1NTKaVs/rlu3bqUUjbPu+Md79h3mGORNAcAAAAAYEkuvfTS7S7z6le/Og9+8IN3QTTLS9IcAACAiTAYDDIzM9N3GGvW7Oxs3yEAwESQNAcAAGAiTE9PZ25uru8w1rTBYNB3CADQOwOBAgAAAABAR9IcAAAAAAA62rMAAAAAALBo++67bw499NDMz8+n1rrVz+GUJEcffXTPkY5H0hwAAAAAgEW77rrrsmnTppRSsm7duuy2226bH5dSMjU1lVJK9tprr75DHYukOQAAAAAAi3bZZZflX/7lX7a73Ic//OFs3LhxF0S0vPQ0BwAAAACAjqQ5AAAAAAB0tGcBAABgIgwGg8zMzPQdxpo1OzvbdwgAMBEkzQEAAJgI09PTmZub6zuMNW0wGPQdAgD0TtIcAACAiaDSvF8qzQGgkTQHAABgIqg0759KcwCQNAcAAGBCqDTvl0pzAGgkzQEAAJgIKs37p9IcAJKpvgMAAAAAAIBJIWkOAAAAAAAdSXMAAAAAAOhImgMAAAAAQMdAoAAAAAAAK8SVV16ZU045Jd/61reSJLXWzdNini/2PaM/1xpJcwAAAACAFeIb3/hGvvCFL/QdxqomaQ4AAAAAsEIccsgh+ehHP5rrrrtu87xSyuZp+HxoYYX5tn5u6/Ubeu1xj3vcomN9whOesOhlJ4mkOQAAAADACrL33nv3HcKinHbaaXn605/edxhLZiBQAAAAAACW3cMf/vC+QxiLSnMAAAAmwmAwyMzMTN9hrFmzs7N9hwDAKvONb3yj7xDGImkOAADARJiens7c3FzfYaxpg8Gg7xAAWEVW6oCl2rMAAAAAALDstGcBAACAHaA9S7+0ZwFguf3sZz/rO4SxSJoDAAAwEbRn6Z/2LAAspzPOOCMvf/nL+w5jybRnAQAAAACAjqQ5AAAAAAB0tGcBAAAAAFghLrvssvzBH/xBLrzwwt5ieMADHpCnPe1p211uv/322wXRLD9JcwAAAACAFeKCCy7oNWGeJJ/+9Kdz6aWXZmpqKqWUrX6OPj722GNz3/vet9dYxyFpDgAAAACwQhx00EHZuHHjdpertW7+OXy88Pm2fm5v2aOPPjpJ8rWvfW27MZx11lmLinXSSJoDAAAAAKwypZStfrJ4BgIFAAAAAGDZ7bHHHn2HMBZJcwAAAAAAlt1Pf/rTvkMYi6Q5AAAAAAB0JM0BAAAAAKAjaQ4AAAAAwLI78MAD+w5hLOv7DgAAAAD+//buP9jSur4P+Pt77+66EH6IJKPxJ5uyIQvVHitZs/7CWzVGa6iONmABpXRI0TJybK2Mk2hNsaPDpNPjj8ERM8SEtrLaJDYtaFvjvcVRnK0jpyRAE34oKAM2ASK/dgPe8+0f97m3y7qwZy/37vfcva/XzJnnnOf57vO87+x/7/nO50mS4XCYfr/fOsa6NRgMWkcA4DBTa20dYVmU5gAAAEyEXq+Xubm51jHWteFw2DoCAGN44IEH8thjj6XW+qTF9N7X9rd28dzi+b1/P9G1g3HnnXce1PpJoTQHAAAAAFgjvvjFL+ayyy5rHeOwZqY5AAAAAMAaceqpp7aOcNiz0xwAAAAAYI3YsmVLZmdnmz1/Zmam2bMPFTvNAQAAAACgozQHAAAAAICO0hwAAAAAADpKcwAAAAAA6CjNAQAAAACgozQHAAAAAGDFHXnkka0jLMuG1gEAAAAAAFgbpqamcuaZZ+bcc8/N1NRUSilLn8OF0hwAAAAAgLGMRqN861vfyqZNm1JKydTU1FJ5Pj09vVSgT01N5aUvfWme85zntI580JTmAAAAAACM7fbbb8/tt99+wHWf/OQnMzs7ewgSrSwzzQEAAAAAoKM0BwAAAACAjtIcAAAAAAA6SnMAAAAAAOgozQEAAAAAoKM0BwAAAACAjtIcAAAAAAA6SnMAAAAAAOhsaB0AAAAAkmQ4HKbf77eOsW4NBoPWEQBgIijNAQAAmAi9Xi9zc3OtY6xrw+GwdQQAaM54FgAAAAAA6CjNAQAAAACgozQHAAAAAICOmeYAAAAxTxsAWL9uvfXWnH/++a1jTAylOQAAQBZegNjv91vHWLcGg0F6vV7rGAAw8Sal4P7oRz+ajRs3ZuPGjZmamlr6lFJSa81oNMrznve81jGXRWkOAAAAALBGHHXUUa0jJEk+8IEPHHDNEUcckWuuueYQpFlZSnMAAAAAgDXiWc96VmZnZ5s9f2ZmZuy1u3fvXsUkq8eLQAEAAAAAoKM0BwAAAACAjvEsAAAAAAAclK1bt2Z+fn7ppZ+11tRal87VWvPOd76zdcxlUZoDAAAAAKxjjz76aD796U/n7rvvflzpPRqNlj6L55LkjDPOyAUXXNA49epRmgMAADARhsNh+v1+6xjr1mAwaB0BgEauu+66fOlLXxp7/c6dO7Nz584Drnv5y1+ej3zkI08lWhNKcwAAACZCr9fL3Nxc6xjr2nA4bB0BgAZOO+20fOITn8jDDz+cqamplFIed1z8niTvec97xr7vPffcs1qRV5XSHAAAgIlgp3lbdpoDrG8vfOELV/yet91224rf81BQmgMAADAR7DRvz05zAEimWgcAAAAAAIBJYac5AAAAE8F4lraMZwGABUpzAAAAJoLxLO0ZzwIAxrMAAAAAAMASpTkAAAAAACvunHPOaR1hWYxnAQAAAABgLFNTUznrrLNy3nnntY6yauw0BwAAAACAjp3mAAAAAABrxF133ZWzzz67aYY77rij6fNXm9IcAAAAAGCNePTRR1tHyLXXXpuZmZmx1s7Ozq5ympWnNAcAAAAAWCO2bNnStIgetyxfy8w0BwAAAACAjp3mAAAAAACM7eSTT17acT41NfW4Tyll6fjiF7+4cdLlUZoDAAAAADC2m266KTfddNNYa9fiTHPjWQAAAAAAoKM0BwAAAACAjvEsAAAATIThcJh+v986xro1GAxaRwCAiaA0BwAAYCL0er3Mzc21jrGuDYfD1hEAoDnjWQAAAAAAoKM0BwAAAACAjvEsAAAAAACHiVrrE35Go1GSZDQaPaXrhzulOQAAAADAGnHjjTfmwgsvbB3jsGY8CwAAAADAGvHsZz87z3zmM5s8u5TS5LmHmp3mAAAAAABrxHHHHZerrrqq2fNnZmaaPftQsdMcAAAAAAA6SnMAAAAAAFbcscce2zrCsijNAQAAAABYcT/60Y9aR1gWM80BAACYCMPhMP1+v3WMdWswGLSOAMCYRqNRaq0/8RmNRmNdP5i1i9cXv68HSnMAAAAmQq/Xy9zcXOsY69pwOGwdAYADuOGGG3LRRRe1jnFYU5oDAAAAAKwRJ5xwQrZu3ZrbbrstSVJKSSll6fu+x8Xvy12777n77rtvtf60iaE0BwAAAABYI4455phcfvnlzZ4/MzMz9trjjz9+FZOsHqU5AAAAE8FM87bMNAdgpd17772tIyyL0hwAAICJYKZ5e2aaA0Ay1ToAAAAAAABMCqU5AAAAAAAr7sgjj2wdYVmU5gAAAAAArLhHHnmkdYRlUZoDAAAAAEBHaQ4AAAAAAB2lOQAAAAAAdJTmAAAAAADQUZoDAAAAAEBHaQ4AAAAAAJ0NrQMAAAAAALC2HHPMMZmfn0+tNaPRKKPRaOn74vFd73pX65jLojQHAAAAAOCgPPDAAwdc873vfW/1g6wC41kAAAAAAFhxX/7yl1tHWBalOQAAADndzWgAABINSURBVAAAK27btm2tIyyL8SwAAAAAAIxlamoqZ511Vs4777zWUVaN0hwAAAAAgLGMRqNceeWVufLKKw+49pRTTsmnPvWpQ5BqZRnPAgAAAADAirvxxhtbR1gWpTkAAAAAAHSU5gAAAAAA0FGaAwAAAABAR2kOAAAAAAAdpTkAAAAAACvunHPOaR1hWZTmAAAAAACsuCuvvLJ1hGVRmgMAAAAAQGdD6wAAAACQJMPhMP1+v3WMdWswGLSOAAATQWkOAADAROj1epmbm2sdY10bDoetIwBAc8azAAAAAABAx05zAAAAAIA14t577815552XBx54oHWUw5ad5gAAAAAAa8QPf/jDNVOYn3HGGa0jLIud5gAAAAAAa8TJJ5+cz3zmM3nwwQdTa02tNUn2e1z8vvfv/Z17sn9fa81oNEqSjEajfOxjHxs7686dO3PBBRcs909tRmkOAAAAALBG7Nq1KxdffHHrGIc1pTkAAAAAwBrR6/Vy+umn5wc/+EGSpJSSUsrS9b1/7+/4RN8PdFz8/rWvfW31/rgJoTQHAAAAAFgjNm3alPe+973Nnn8wpfk555yziklWjxeBAgAAAACw4q688srWEZbFTnMAAAAmwnA4TL/fbx1j3RoMBq0jAMBEUJoDAAAwEXq9Xubm5lrHWNeGw2HrCADQnNIcAAAAAICxHXfccdmxY8fSC0KnpqYe91k895rXvKZ11GVRmgMAADARjGdpy3gWAMZ1//3355prrjngup07d2Z2dvYQJFpZSnMAAAAmgvEs7RnPAjD5vv/97+cd73hH6xiHtanWAQAAAAAAGM+ePXtaRzjsKc0BAAAAANaI+++/v3WEsfV6vdYRlsV4FgAAAACANWL79u357Gc/m4ceemjpRZyllKXre//e3/FAa2utqbUufV+0eP7d73732FnX6tgvpTkAAAAAwBpy4oknto5wWDOeBQAAAAAAOkpzAAAAAADoKM0BAAAAAKCjNAcAAAAAgI7SHAAAAAAAOhtaBwAAAIAkGQ6H6ff7rWOsW4PBoHUEAJgISnMAAAAmQq/Xy9zcXOsY69pwOGwdAQCaM54FAAAAAAA6SnMAAAAAAOgozQEAAAAAoKM0BwAAAACAjheBAgAAMBGGw2H6/X7rGOvWYDBoHQEAJoLSHAAAgInQ6/UyNzfXOsa6NhwOW0cAgOaU5gAAAAAA69zu3bszPz+/9Hs0GqXWunScn59PrbVhwkNHaQ4AAMBEMJ6lLeNZANavXbt25eKLL24dY2IozQEAAJgIxrO0ZzwLwPp00kknZdu2bbnzzjtTa136JFnabb7oscceaxXzkFGaAwAAMBHsNG/LTnOA9evYY4/NZZddNtbamZmZVU7TntIcAACAiWCneXt2mgOA0hwAAAAAYM246667cvbZZ7eOcVibah0AAAAAAIDxPProo60jHPbsNAcAAGAimGnelpnmAGvDli1bMjs72+z562GmuZ3mAAAAAADQsdMcAACAieBFoO15ESgAKM0BAAAAANaU0WiUWutPfEaj0VjXD2bt4vXF7+uB0hwAAAAAYI244YYbctFFF7WOcVgz0xwAAAAAYI044YQTsnXr1pRSnvDDU2OnOQAAAADAGnHMMcfk8ssvb/b8mZmZZs8+VOw0BwAAAACAjtIcAAAAAAA6SnMAAAAAAOgozQEAAAAAoKM0BwAAAACAjtIcAAAAAAA6SnMAAAAAAOgozQEAAAAAoKM0BwAAAACAzobWAQAAAAAAWFuOP/741FozGo2WPou/a62pteZ973tf65jLojQHAAAAAOCgPOMZz0iSTE1NPe5TSlk6HnXUUY1TLo/SHAAAgIkwHA7T7/dbx1i3BoNB6wgArCG33HLLAddcf/31mZ2dPQRpVpbSHAAAAABgjbjnnnvy9re/vXWMw5rSHAAAgInQ6/UyNzfXOsa6NhwOW0cA4AAeeuih1hEOe0pzAAAAAIA14sQTT1z1kSf7vtBz799vfOMbV/XZk2CqdQAAAAAAgLWslHJ0KWVQSrmjlLK7lPLNUsovts61XKWUTE9PZ8OGDdm4cWM2bdqUzZs354gjjmgd7ZCw0xwAAAAA4Kn5nSQvSvLOJD9IcnaSr5ZSTq61Ng3W0imnnNI6wrIozQEAAAAAlqmUckSStyZ5a611rjv94VLKryZ5V7NgE+DGG29sHWFZlOYAAAAAAMu3Icl0kj37nN+d5BUr+aD5+fns2rUrt9xyS7Zu3Zrt27dnenp6JR9BlOYAAABMiOFwmH6/3zrGujUYDFpHAFiTaq0PllKuS/KbpZQ/S3JPkrcn2ZHk1pV6zvz8fN7//vfn5ptvzp49e7J58+Zs27Ytl156aaanp5de1pnkJ17iuffLPLvMY69dvL74fT1QmgMAAAAAPDXnJLkiC/PM55N8J8nnk7xkpR6wa9eu3Hzzzdm9e3eSZPfu3fnOd76T1772tSv1CDpKcwAAACZCr9fL3Nxc6xjr2nA4bB0BYE2qtd6W5LRSyk8lOabWencpZWeS25P8wko845ZbbsmePftOgFm+UkpKKY/7vvh7f+cWvz/yyCMrlmFSKc0BAAAAAFZArfXhJA+XUo5L8vok70/yxpW499atW7N58+alneZJsnnz5nzoQx/Kjh07VuIRY5mZmTlkz2plqnUAAAAAAIC1rJTy+lLKG0opW0opr0sym+T/JPndlXrG9u3bs23btmzevDmllGzevDknn3xytm/fvlKPoGOnOQAAABPBi0Db8iJQgKfk2CQfTfLcJPcl+YMkv1FrfWylHjA9PZ1LL700u3btyq233poTTzwx27dvz/T09Eo9go7SHAAAgIlgpnl7ZpoDLE+t9QtJvrDaz5mens6OHTsO6TiW9ch4FgAAAAAA6CjNAQAAAACgozQHAAAAAICO0hwAAAAAADpKcwAAAAAA6GxoHQAAAACSZDgcpt/vt46xbg0Gg9YRAGAiKM0BAACYCL1eL3Nzc61jrGvD4bB1BABoTmkOAADARLDTvC07zQFggZnmAAAAAADQsdMcAACAiWA8S3vGswCAneYAAAAAALBEaQ4AAAAAAB2lOQAAAAAAdJTmAAAAAADQUZoDAAAAAEBHaQ4AAAAAwIp7wQte0DrCsijNAQAAAABYcXfccUfrCMuiNAcAAAAAgI7SHAAAAAAAOkpzAAAAAADoKM0BAAAAAKCzoXUAAAAASJLhcJh+v986xro1GAxaRwCAiaA0BwAAYCL0er3Mzc21jrGuDYfD1hEAoDnjWQAAAAAAWHGvfOUrW0dYFjvNAQAAmAjGs7RlPAsAK+3rX/966wjLojQHAABgIhjP0p7xLACgNAcAAGBC2Gnelp3mALBAaQ4AAMBEsNO8PTvNAcCLQAEAAAAAYInSHAAAAAAAOsazAAAAAAAwlqmpqbzhDW/IW97ylkxPT2d6ejobNmx43HF6ejpJcsQRRzROuzxKcwAAAAAAxjIajXL11Vfn6quvHmv97OzsKidaecazAAAAAABAR2kOAAAAAAAdpTkAAAAAAHSU5gAAAAAArLher9c6wrJ4ESgAAAATYTgcpt/vt46xbg0Gg9YRADjM3HPPPa0jLIvSHAAAgInQ6/UyNzfXOsa6NhwOW0cA4DCyVktz41kAAAAAAKCjNAcAAAAAgI7SHAAAAAAAOkpzAAAAAADoKM0BAAAAAKCjNAcAAAAAgI7SHAAAAAAAOkpzAAAAAABW3FFHHdU6wrJsaB0AAAAAAIC14+ijj86LXvSibNiwIRs3blz6bNq06XHnTjvttNZRl0VpDgAAAADA2B588MF84xvfOOC6K664IrOzs4cg0coyngUAAAAAADpKcwAAAAAA6BjPAgAAAACwjt1xxx0599xzW8eYGHaaAwAAAACsY3v27GkdYaLYaQ4AAAAAsI6ddNJJY7+wc2ZmZpXTtGenOQAAAAAAdJTmAAAAAADQMZ4FAACAiTAcDtPv91vHWLcGg0HrCAAwEQ66NC+lvCbJhUl2JDkuyb1J/jTJx2ut1+yz9mVJfjPJLyU5IsktSa5I8sla6/w+a5+e5PwkvSQvTvLzSaaTvK7W+tUnyfN3k3wgyau6PPck+a9JfqvW+sOD/fsAAABoo9frZW5urnWMdW04HLaOAABjKaW8Ksn7krwkybOT/ONa6+f2un5Jkn+Y5HlJHk3ynSQfrLV+80D3PqjxLKWUS5N8NcmpSf44yb9NcnWSn0ny6n3W/oMk12ahzP6jJJ9KsinJv0ty1X5uf0KSS5P8oyRHJ/mrMfK8Kcm3kry1O348yf9OckGSb5dSnn8wfx8AAAAAAGvCUUn+LMlFSXbv5/qfJ/lnSV6Y5BVJvpvkK6WUZx7oxmPvNC+lnJ/kXyb5vSS/Xmt9dJ/rG/f6fkySzyaZT/LqWuu3u/MfTPK1JG8rpZxZa927PL8jyWuTXF9rva+U8rkk73ySPJuT/E6SjUneWmv9w72uvT3Jf8xCUX/6uH8jAAAAAACTr5t6ck2SdF3yvtf//d6/Syn/PMk/ycKkk//2ZPcea6d5KeVpSf5Nkjuzn8K8C/HYXj/floXd51ctFubdmj1ZGNeSJO/a59/fX2v9k1rrfeNkSvKyJM9M8u29C/PuXp/Pwo7zN5VSXjDm/QAAAAAAWEHXXXdd5ufnD7xwFZVSNiX59SQPJDngLLJxd5q/Lgsl+CDJqJTy95P87SR7kuyqtV63z/q/1x2/sp97XZvkkSQvK6U8rdb6N2Nm2NezuuPtT3D99iR/p8vyu8t8BgAAAIeIF4G25UWgAKyGSy65JNu2bcull16a6enpQ/rsbrz3VUmOTHJ3Ft6fecD3YI5bmv9id9yT5PosFOZ7P/zaJG+rtf5ld+qk7vgX+96o1vrjUsp3k5yS5OeS3Dxmhn0tzjzf8gTXf26fLAAAAEwwLwJtz4tAAVhpu3fvzk033ZRdu3Zlx44dh/rxs1kYx/LTSc5P8oVSyo5a691P9o9KrfWAdy6lfDoLL9ecT3JTkndnYRv7liS/neSXk/zPWuuru/V/kWRrkq211lv3c79vZGG8ysv2s0t9cc3nsjDT/HW11q/u5/pPJflBkqcneXOt9T/vde3Xkuzsfl5ea/2nB/wjAQAAAAB4SmZmZj6Y5MN5/GjwUZJ/NTs7+5HVeGYp5aEkF9ZaP3eAdbck+f1a6yVPtm7cneaLf+CPk5xea/1e9/tPSylvycKbSE/rWvr9luArrdb6cCnloiSfS/KHpZQ/TnJLkl9I8qYslPq9LPyHAAAAAACwymZnZy9J8qSldENTSZ42zqJx/HV3vH6vwjxJUmt9JP//baPbu+OPuuOxT3C/xfN//QTXx1Jr/f0kr0nyP5K8OslFSZ6f5Nwk/6Fb9n+fyjMAAAAAAJgspZSjSim9UkovCz3387vfzy+lHFNK+Ugp5aXd75eUUq5I8twkXzjQvcctzf+8Oz5RyX1/dzxin/U/v58/ZkMWxrr8OE/8Es+x1Vpna62/Ums9rtb6tFprryvTX9Qt+V9P9RkAAAAAAEyUU7Pw/s3rs9BL/1b3/V9noXs+JckfZWE6yX9JcnySV9VabzjQjccdz/InSWqSk0spU7XWfUeeLL4Y9Lvd8WtJzkryK0k+v8/aV2XhbaXX1lr/ZsznH5RSytOT/GqSv8zCLnQAAAAAAA4Ttda5JOVJlrxlufcea6d5rfWOLLTxz8/CCJQlpZRfTvL6LOxC/0p3+j8l+askZ5ZSTt1r7eYki8PeP73c0Hvd7+j9nDsyye9l4QWhH1qtYh4AAAAAgMNPqbWOt7CU5yb5ZpLnZWHn+fVZGLPy5izsQj+z1voHe61/cxbK8z1JrkpyX5LTk5zUnf+1us/DSym/neSnu5+vSPK3kvz3JHd3575Ua/3SXusvTPIvksx1a47Pwg7zn03y8Vprf6w/DgAAAAAAchCleZKUUn4myYeyUH7/bJIHknw9yUdrrbv2s/7lSX4jyY4km5PcmuSKJJ+otc7vZ/33krzgSSL8Vq31w3ut/6Uuz4uzUJg/kGRXkk/WWr889h8GAAAAAAA5yNIcAAAAAAAOZ2PNNAcAAAAAgPVAaQ4AAAAAAB2lOQAAAAAAdJTmAAAAAADQUZoDAAAAAEBHaQ4AAAAAAB2lOQAAAAAAdJTmAAAAAADQUZoDAAAAAEBHaQ4AAAAAAJ3/BwBy2XLxz1NSAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car.Mileage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Si0wLpPAaFy",
        "outputId": "50c5342b-baa0-493c-bb63-fb06c18e5b57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       26.6 km/kg\n",
              "1       19.67 kmpl\n",
              "2        18.2 kmpl\n",
              "3       20.77 kmpl\n",
              "4        15.2 kmpl\n",
              "           ...    \n",
              "6014     28.4 kmpl\n",
              "6015     24.4 kmpl\n",
              "6016     14.0 kmpl\n",
              "6017     18.9 kmpl\n",
              "6018    25.44 kmpl\n",
              "Name: Mileage, Length: 6019, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check for missing values."
      ],
      "metadata": {
        "id": "5YtzoWxuA5B0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(sum(car[\"Mileage\"].isnull()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdG1o-EhAouh",
        "outputId": "baec46eb-81b4-4e29-b8bf-faed054efc03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "so as there are only two value let's remove that row"
      ],
      "metadata": {
        "id": "3Ve3T2OHAqhH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car = car.dropna(subset=[\"Mileage\"])\n"
      ],
      "metadata": {
        "id": "Dlo3g4pVAwoO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car['Mileage']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9avscFYkBgRz",
        "outputId": "f8ed2f81-71ff-40aa-8387-cfc79f927051"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       26.6 km/kg\n",
              "1       19.67 kmpl\n",
              "2        18.2 kmpl\n",
              "3       20.77 kmpl\n",
              "4        15.2 kmpl\n",
              "           ...    \n",
              "6014     28.4 kmpl\n",
              "6015     24.4 kmpl\n",
              "6016     14.0 kmpl\n",
              "6017     18.9 kmpl\n",
              "6018    25.44 kmpl\n",
              "Name: Mileage, Length: 6017, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Correct_Mileage= []\n",
        "for i in car.Mileage:\n",
        "    if str(i).endswith('km/kg'):\n",
        "        i = i[:-6]\n",
        "        i = float(i)*1.40\n",
        "        Correct_Mileage.append(float(i))\n",
        "    elif str(i).endswith('kmpl'):\n",
        "        i = i[:-6]\n",
        "        #print(i)\n",
        "        Correct_Mileage.append(float(i))\n",
        "#    for unit in str(i).split(\" \"):\n",
        "#        print(type(unit))\n",
        "#        if unit=='km/kg':\n",
        "#            print('kmkg')"
      ],
      "metadata": {
        "id": "zGz9IJ9PBL4u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car['Mileage']=Correct_Mileage\n"
      ],
      "metadata": {
        "id": "lQH1WvL5BSqD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car.Mileage"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5KQTZ4ZFfSX",
        "outputId": "b0ac9c34-3a7b-48f8-f253-fb7c24888ab8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       37.24\n",
              "1       19.60\n",
              "2       18.00\n",
              "3       20.70\n",
              "4       15.00\n",
              "        ...  \n",
              "6014    28.00\n",
              "6015    24.00\n",
              "6016    14.00\n",
              "6017    18.00\n",
              "6018    25.40\n",
              "Name: Mileage, Length: 6017, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "gYPifEDf_Rng",
        "outputId": "e5509448-5b10-4706-a12b-88d405e4c868"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year  Kilometers_Driven Fuel_Type Transmission Owner_Type  Mileage  \\\n",
              "0     2010              72000       CNG       Manual      First    37.24   \n",
              "1     2015              41000    Diesel       Manual      First    19.60   \n",
              "2     2011              46000    Petrol       Manual      First    18.00   \n",
              "3     2012              87000    Diesel       Manual      First    20.70   \n",
              "4     2013              40670    Diesel    Automatic     Second    15.00   \n",
              "...    ...                ...       ...          ...        ...      ...   \n",
              "6014  2014              27365    Diesel       Manual      First    28.00   \n",
              "6015  2015             100000    Diesel       Manual      First    24.00   \n",
              "6016  2012              55000    Diesel       Manual     Second    14.00   \n",
              "6017  2013              46000    Petrol       Manual      First    18.00   \n",
              "6018  2011              47000    Diesel       Manual      First    25.40   \n",
              "\n",
              "       Engine      Power  Seats  New_Price  Price Manufacturer  \\\n",
              "0      998 CC  58.16 bhp    5.0        NaN   1.75       Maruti   \n",
              "1     1582 CC  126.2 bhp    5.0        NaN  12.50      Hyundai   \n",
              "2     1199 CC   88.7 bhp    5.0  8.61 Lakh   4.50        Honda   \n",
              "3     1248 CC  88.76 bhp    7.0        NaN   6.00       Maruti   \n",
              "4     1968 CC  140.8 bhp    5.0        NaN  17.74         Audi   \n",
              "...       ...        ...    ...        ...    ...          ...   \n",
              "6014  1248 CC     74 bhp    5.0  7.88 Lakh   4.75       Maruti   \n",
              "6015  1120 CC     71 bhp    5.0        NaN   4.00      Hyundai   \n",
              "6016  2498 CC    112 bhp    8.0        NaN   2.90     Mahindra   \n",
              "6017   998 CC   67.1 bhp    5.0        NaN   2.65       Maruti   \n",
              "6018   936 CC   57.6 bhp    5.0        NaN   2.50    Chevrolet   \n",
              "\n",
              "                      Model  \n",
              "0            Maruti Wagon R  \n",
              "1         Hyundai Creta 1.6  \n",
              "2              Honda Jazz V  \n",
              "3         Maruti Ertiga VDI  \n",
              "4               Audi A4 New  \n",
              "...                     ...  \n",
              "6014       Maruti Swift VDI  \n",
              "6015      Hyundai Xcent 1.1  \n",
              "6016       Mahindra Xylo D4  \n",
              "6017         Maruti Wagon R  \n",
              "6018  Chevrolet Beat Diesel  \n",
              "\n",
              "[6017 rows x 13 columns]"
            ],
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              "\n",
              "  <div id=\"df-4bd73d0a-4f14-4109-900d-b645ebb9cd45\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>Price</th>\n",
              "      <th>Manufacturer</th>\n",
              "      <th>Model</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010</td>\n",
              "      <td>72000</td>\n",
              "      <td>CNG</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>37.24</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>58.16 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.75</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Wagon R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015</td>\n",
              "      <td>41000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>19.60</td>\n",
              "      <td>1582 CC</td>\n",
              "      <td>126.2 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.50</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>Hyundai Creta 1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.00</td>\n",
              "      <td>1199 CC</td>\n",
              "      <td>88.7 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.61 Lakh</td>\n",
              "      <td>4.50</td>\n",
              "      <td>Honda</td>\n",
              "      <td>Honda Jazz V</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012</td>\n",
              "      <td>87000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>20.70</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>88.76 bhp</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.00</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Ertiga VDI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2013</td>\n",
              "      <td>40670</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Second</td>\n",
              "      <td>15.00</td>\n",
              "      <td>1968 CC</td>\n",
              "      <td>140.8 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.74</td>\n",
              "      <td>Audi</td>\n",
              "      <td>Audi A4 New</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6014</th>\n",
              "      <td>2014</td>\n",
              "      <td>27365</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>28.00</td>\n",
              "      <td>1248 CC</td>\n",
              "      <td>74 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>7.88 Lakh</td>\n",
              "      <td>4.75</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Swift VDI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6015</th>\n",
              "      <td>2015</td>\n",
              "      <td>100000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>24.00</td>\n",
              "      <td>1120 CC</td>\n",
              "      <td>71 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Hyundai</td>\n",
              "      <td>Hyundai Xcent 1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6016</th>\n",
              "      <td>2012</td>\n",
              "      <td>55000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>14.00</td>\n",
              "      <td>2498 CC</td>\n",
              "      <td>112 bhp</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.90</td>\n",
              "      <td>Mahindra</td>\n",
              "      <td>Mahindra Xylo D4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6017</th>\n",
              "      <td>2013</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.00</td>\n",
              "      <td>998 CC</td>\n",
              "      <td>67.1 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.65</td>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Wagon R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6018</th>\n",
              "      <td>2011</td>\n",
              "      <td>47000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>25.40</td>\n",
              "      <td>936 CC</td>\n",
              "      <td>57.6 bhp</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.50</td>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Chevrolet Beat Diesel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6017 rows × 13 columns</p>\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Engine, Power and Seats\n",
        "\n",
        "The `Engine` values are defined in CC so I need to remove `CC` from the data. Similarly, `Power` has bhp, so I'll remove `bhp` from it. Also, as there are missing values in `Engine`, `Power` and `Seats`, I'll again replace them with the mean."
      ],
      "metadata": {
        "id": "RumiM7667N6S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cc=car['Engine'].str.split(\" \",expand=True)\n",
        "\n",
        "bhp=car['Power'].str.split(\" \",expand=True)\n",
        "\n",
        "car['Engine']= pd.to_numeric(cc[0],errors='coerce')\n",
        "car['Power']= pd.to_numeric(bhp[0],errors='coerce')\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RSsXYx7f6_dz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car[\"Engine\"].fillna(car[\"Engine\"].astype(\"float64\").mean(), inplace = True)\n",
        "car[\"Power\"].fillna(car[\"Power\"].astype(\"float64\").mean(), inplace = True)\n",
        "car[\"Seats\"].fillna(car[\"Seats\"].astype(\"float64\").mean(), inplace = True)\n"
      ],
      "metadata": {
        "id": "X5azQcjh-DEf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### New Price\n",
        "\n",
        "As most of the values are missing, I'll drop this column altogether."
      ],
      "metadata": {
        "id": "b41KQx-vCuwH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car.drop([\"New_Price\"], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "hnOP2XexDGty"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtAqe4WbF9Nf",
        "outputId": "443a1700-f6ca-4cad-9272-bd03c4f33820"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 6017 entries, 0 to 6018\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Year               6017 non-null   int64  \n",
            " 1   Kilometers_Driven  6017 non-null   int64  \n",
            " 2   Fuel_Type          6017 non-null   object \n",
            " 3   Transmission       6017 non-null   object \n",
            " 4   Owner_Type         6017 non-null   object \n",
            " 5   Mileage            6017 non-null   float64\n",
            " 6   Engine             6017 non-null   float64\n",
            " 7   Power              6017 non-null   float64\n",
            " 8   Seats              6017 non-null   float64\n",
            " 9   Price              6017 non-null   float64\n",
            " 10  Manufacturer       6017 non-null   object \n",
            " 11  Model              6017 non-null   object \n",
            "dtypes: float64(5), int64(2), object(5)\n",
            "memory usage: 611.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Rearrange columns\n",
        " "
      ],
      "metadata": {
        "id": "bbSpoNuaDcZ7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cols=list(car.columns.values)\n",
        "car=car[cols[10:12]+cols[0:10]]\n",
        "car"
      ],
      "metadata": {
        "id": "D6fNkITz9oCf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "outputId": "71d5fe74-666b-45ce-bafc-5b5a905e2f00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     Manufacturer                  Model  Year  Kilometers_Driven Fuel_Type  \\\n",
              "0          Maruti         Maruti Wagon R  2010              72000       CNG   \n",
              "1         Hyundai      Hyundai Creta 1.6  2015              41000    Diesel   \n",
              "2           Honda           Honda Jazz V  2011              46000    Petrol   \n",
              "3          Maruti      Maruti Ertiga VDI  2012              87000    Diesel   \n",
              "4            Audi            Audi A4 New  2013              40670    Diesel   \n",
              "...           ...                    ...   ...                ...       ...   \n",
              "6014       Maruti       Maruti Swift VDI  2014              27365    Diesel   \n",
              "6015      Hyundai      Hyundai Xcent 1.1  2015             100000    Diesel   \n",
              "6016     Mahindra       Mahindra Xylo D4  2012              55000    Diesel   \n",
              "6017       Maruti         Maruti Wagon R  2013              46000    Petrol   \n",
              "6018    Chevrolet  Chevrolet Beat Diesel  2011              47000    Diesel   \n",
              "\n",
              "     Transmission Owner_Type  Mileage  Engine   Power  Seats  Price  \n",
              "0          Manual      First    37.24   998.0   58.16    5.0   1.75  \n",
              "1          Manual      First    19.60  1582.0  126.20    5.0  12.50  \n",
              "2          Manual      First    18.00  1199.0   88.70    5.0   4.50  \n",
              "3          Manual      First    20.70  1248.0   88.76    7.0   6.00  \n",
              "4       Automatic     Second    15.00  1968.0  140.80    5.0  17.74  \n",
              "...           ...        ...      ...     ...     ...    ...    ...  \n",
              "6014       Manual      First    28.00  1248.0   74.00    5.0   4.75  \n",
              "6015       Manual      First    24.00  1120.0   71.00    5.0   4.00  \n",
              "6016       Manual     Second    14.00  2498.0  112.00    8.0   2.90  \n",
              "6017       Manual      First    18.00   998.0   67.10    5.0   2.65  \n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Price in lakhs"
      ],
      "metadata": {
        "id": "aa04ViFaHU9w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car['Price']=car['Price']*100000"
      ],
      "metadata": {
        "id": "N7G5Jyup9poC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "GOrLLAmyHhng",
        "outputId": "cf7d321f-be04-49bb-fbd0-0e7f8b35b0e2"
      },
      "execution_count": null,
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            "text/plain": [
              "     Manufacturer                  Model  Year  Kilometers_Driven Fuel_Type  \\\n",
              "0          Maruti         Maruti Wagon R  2010              72000       CNG   \n",
              "1         Hyundai      Hyundai Creta 1.6  2015              41000    Diesel   \n",
              "2           Honda           Honda Jazz V  2011              46000    Petrol   \n",
              "3          Maruti      Maruti Ertiga VDI  2012              87000    Diesel   \n",
              "4            Audi            Audi A4 New  2013              40670    Diesel   \n",
              "...           ...                    ...   ...                ...       ...   \n",
              "6014       Maruti       Maruti Swift VDI  2014              27365    Diesel   \n",
              "6015      Hyundai      Hyundai Xcent 1.1  2015             100000    Diesel   \n",
              "6016     Mahindra       Mahindra Xylo D4  2012              55000    Diesel   \n",
              "6017       Maruti         Maruti Wagon R  2013              46000    Petrol   \n",
              "6018    Chevrolet  Chevrolet Beat Diesel  2011              47000    Diesel   \n",
              "\n",
              "     Transmission Owner_Type  Mileage  Engine   Power  Seats      Price  \n",
              "0          Manual      First    37.24   998.0   58.16    5.0   175000.0  \n",
              "1          Manual      First    19.60  1582.0  126.20    5.0  1250000.0  \n",
              "2          Manual      First    18.00  1199.0   88.70    5.0   450000.0  \n",
              "3          Manual      First    20.70  1248.0   88.76    7.0   600000.0  \n",
              "4       Automatic     Second    15.00  1968.0  140.80    5.0  1774000.0  \n",
              "...           ...        ...      ...     ...     ...    ...        ...  \n",
              "6014       Manual      First    28.00  1248.0   74.00    5.0   475000.0  \n",
              "6015       Manual      First    24.00  1120.0   71.00    5.0   400000.0  \n",
              "6016       Manual     Second    14.00  2498.0  112.00    8.0   290000.0  \n",
              "6017       Manual      First    18.00   998.0   67.10    5.0   265000.0  \n",
              "6018       Manual      First    25.40   936.0   57.60    5.0   250000.0  \n",
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              "      <td>46000</td>\n",
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              "      <td>1199.0</td>\n",
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              "      <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>6014</th>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Swift VDI</td>\n",
              "      <td>2014</td>\n",
              "      <td>27365</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>28.00</td>\n",
              "      <td>1248.0</td>\n",
              "      <td>74.00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>475000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6015</th>\n",
              "      <td>Hyundai</td>\n",
              "      <td>Hyundai Xcent 1.1</td>\n",
              "      <td>2015</td>\n",
              "      <td>100000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>24.00</td>\n",
              "      <td>1120.0</td>\n",
              "      <td>71.00</td>\n",
              "      <td>5.0</td>\n",
              "      <td>400000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6016</th>\n",
              "      <td>Mahindra</td>\n",
              "      <td>Mahindra Xylo D4</td>\n",
              "      <td>2012</td>\n",
              "      <td>55000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>Second</td>\n",
              "      <td>14.00</td>\n",
              "      <td>2498.0</td>\n",
              "      <td>112.00</td>\n",
              "      <td>8.0</td>\n",
              "      <td>290000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6017</th>\n",
              "      <td>Maruti</td>\n",
              "      <td>Maruti Wagon R</td>\n",
              "      <td>2013</td>\n",
              "      <td>46000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>18.00</td>\n",
              "      <td>998.0</td>\n",
              "      <td>67.10</td>\n",
              "      <td>5.0</td>\n",
              "      <td>265000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6018</th>\n",
              "      <td>Chevrolet</td>\n",
              "      <td>Chevrolet Beat Diesel</td>\n",
              "      <td>2011</td>\n",
              "      <td>47000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Manual</td>\n",
              "      <td>First</td>\n",
              "      <td>25.40</td>\n",
              "      <td>936.0</td>\n",
              "      <td>57.60</td>\n",
              "      <td>5.0</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6017 rows × 12 columns</p>\n",
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              "            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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              "  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "car.describe()"
      ],
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          "height": 344
        },
        "id": "wkyncr6yHiIo",
        "outputId": "a9a87734-a287-4a31-b432-6dabba69f880"
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      "execution_count": null,
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            "text/plain": [
              "              Year  Kilometers_Driven      Mileage       Engine        Power  \\\n",
              "count  6017.000000       6.017000e+03  6017.000000  6017.000000  6017.000000   \n",
              "mean   2013.358152       5.874228e+04    17.990371  1621.505935   113.272203   \n",
              "std       3.269967       9.128375e+04     4.888706   599.316375    53.229183   \n",
              "min    1998.000000       1.710000e+02     0.000000   624.000000    34.200000   \n",
              "25%    2011.000000       3.400000e+04    15.000000  1198.000000    78.000000   \n",
              "50%    2014.000000       5.300000e+04    18.000000  1493.000000    98.600000   \n",
              "75%    2016.000000       7.300000e+04    21.000000  1969.000000   138.030000   \n",
              "max    2019.000000       6.500000e+06    46.956000  5998.000000   560.000000   \n",
              "\n",
              "             Seats         Price  \n",
              "count  6017.000000  6.017000e+03  \n",
              "mean      5.278828  9.478340e+05  \n",
              "std       0.806130  1.118961e+06  \n",
              "min       0.000000  4.400000e+04  \n",
              "25%       5.000000  3.500000e+05  \n",
              "50%       5.000000  5.640000e+05  \n",
              "75%       5.000000  9.950000e+05  \n",
              "max      10.000000  1.600000e+07  "
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              "      <th>count</th>\n",
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              "      <td>6017.000000</td>\n",
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              "      <td>6017.000000</td>\n",
              "      <td>6.017000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2013.358152</td>\n",
              "      <td>5.874228e+04</td>\n",
              "      <td>17.990371</td>\n",
              "      <td>1621.505935</td>\n",
              "      <td>113.272203</td>\n",
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              "      <th>min</th>\n",
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              "      <td>624.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>4.400000e+04</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2011.000000</td>\n",
              "      <td>3.400000e+04</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1198.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.500000e+05</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2014.000000</td>\n",
              "      <td>5.300000e+04</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1493.000000</td>\n",
              "      <td>98.600000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.640000e+05</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>7.300000e+04</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>1969.000000</td>\n",
              "      <td>138.030000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.950000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2019.000000</td>\n",
              "      <td>6.500000e+06</td>\n",
              "      <td>46.956000</td>\n",
              "      <td>5998.000000</td>\n",
              "      <td>560.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.600000e+07</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          "metadata": {},
          "execution_count": 91
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car[car[\"Price\"]>10e6]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "2HR9iA3mIdKF",
        "outputId": "b5db3e5a-bd98-4f53-858a-157b0bdd973e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Manufacturer                       Model  Year  Kilometers_Driven  \\\n",
              "4079         Land            Land Rover Range  2017              25000   \n",
              "5781  Lamborghini  Lamborghini Gallardo Coupe  2011               6500   \n",
              "\n",
              "     Fuel_Type Transmission Owner_Type  Mileage  Engine  Power  Seats  \\\n",
              "4079    Diesel    Automatic      First     13.3  2993.0  255.0    5.0   \n",
              "5781    Petrol    Automatic      Third      6.0  5204.0  560.0    2.0   \n",
              "\n",
              "           Price  \n",
              "4079  16000000.0  \n",
              "5781  12000000.0  "
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              "      <th>4079</th>\n",
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              "      <td>Diesel</td>\n",
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              "      <th>5781</th>\n",
              "      <td>Lamborghini</td>\n",
              "      <td>Lamborghini Gallardo Coupe</td>\n",
              "      <td>2011</td>\n",
              "      <td>6500</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>Third</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5204.0</td>\n",
              "      <td>560.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>12000000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b69db14-0fba-4e1a-a4b1-b77a40d3232a')\"\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-6b69db14-0fba-4e1a-a4b1-b77a40d3232a 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-6b69db14-0fba-4e1a-a4b1-b77a40d3232a');\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": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car=car[car[\"Price\"]<10e6].reset_index(drop=True)"
      ],
      "metadata": {
        "id": "1uHIU1xNJR1g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "we have outlier of two cars having prices in crore which will affect our model, therefore we will remove it"
      ],
      "metadata": {
        "id": "eZRh1i82I_2o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car.info()"
      ],
      "metadata": {
        "id": "Cv9U4xxjHlZM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "px.scatter(car,x = 'Year',y='Kilometers_Driven')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "1YAmbhkSGTFQ",
        "outputId": "28a8def2-3ee5-4afe-dd22-2f3c947c2773"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing outlier\n",
        "car = car[car['Kilometers_Driven']<6000000]"
      ],
      "metadata": {
        "id": "btCBKwJ5GeSy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "px.scatter(car,x = 'Year',y='Kilometers_Driven',color='Kilometers_Driven')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "xOBcAjc-GmbV",
        "outputId": "23ff029e-a6ab-428f-c940-4fb458f9d6e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(14,12))\n",
        "sns.heatmap(car.corr(), annot=True)\n",
        "plt.savefig('Correlation of Data.png')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
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        "id": "4oFistJRg_mT",
        "outputId": "eccb1f2c-1c85-4b32-8bbf-f3386ac9a24f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# seats and Transmission drop\n",
        "\n",
        "as we are already having Manufacturer and model , seats and transmission will be extra feature"
      ],
      "metadata": {
        "id": "ZvjPZiksqu5m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car.drop('Seats',axis=1,inplace=True)\n",
        "car.drop('Transmission',axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "IbggAIvMquOl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "msg.matrix(car)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "QNGsHXu7GtTb",
        "outputId": "2ab5e7f0-5588-4134-db9a-bb896a8b3d8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7dfd0b43d0>"
            ]
          },
          "metadata": {},
          "execution_count": 223
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#save cleaned dataset\n"
      ],
      "metadata": {
        "id": "3aLGkFegKCk3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car.to_csv(\"cleaned_car.csv\")"
      ],
      "metadata": {
        "id": "zwZl-1lDIBWt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#model\n"
      ],
      "metadata": {
        "id": "Cvf9pVK2KPfw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X=car.drop(columns='Price')\n",
        "y=car['Price']"
      ],
      "metadata": {
        "id": "pCcOVQ5aKOk6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train , x_test , y_train , y_test = train_test_split(X,y,test_size=0.2)"
      ],
      "metadata": {
        "id": "xzugv94gLfiu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import VotingRegressor\n",
        "from sklearn.metrics import r2_score , mean_absolute_error, mean_squared_error\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.pipeline import make_pipeline"
      ],
      "metadata": {
        "id": "Ua6ehk04LL50"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ohe = OneHotEncoder(handle_unknown='ignore')\n",
        "ohe.fit(X[['Manufacturer','Model','Fuel_Type', \"Owner_Type\"]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THg_2zKQLosT",
        "outputId": "43dbc995-5189-4290-f805-2cb5e68496c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneHotEncoder(handle_unknown='ignore')"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_trans = make_column_transformer((OneHotEncoder(categories=ohe.categories_),['Manufacturer','Model','Fuel_Type', \"Owner_Type\"]), remainder=\"passthrough\")"
      ],
      "metadata": {
        "id": "OURr6vrvL5hh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### lets check best random_State so that our model will give good results"
      ],
      "metadata": {
        "id": "bGyL6OjRNUrA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "checking random_State in favor of linear regressor"
      ],
      "metadata": {
        "id": "YZjeEZneNm_b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scores=[]\n",
        "for i in range(1000):\n",
        "  x_train, x_test,y_train,y_test = train_test_split(X,y,test_size=0.2 , random_state=i)\n",
        "  lr = LinearRegression()\n",
        "  pipe=make_pipeline(column_trans,lr)\n",
        "  pipe.fit(x_train,y_train)\n",
        "  y_pred = pipe.predict(x_test)\n",
        "  scores.append(r2_score(y_test,y_pred))"
      ],
      "metadata": {
        "id": "9fMZ1sGQNlaG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(scores)\n",
        "# random_state value 901 performs best for linear regressor let's check for the others"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KT9cQNpYOMex",
        "outputId": "bf6bc78e-e0a9-401f-a3ac-8c96ae555eac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "269"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "in favor of decision tree regressor"
      ],
      "metadata": {
        "id": "agmBBaPdN03R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scores1=[]\n",
        "for i in range(1000):\n",
        "  x_train, x_test,y_train,y_test = train_test_split(X,y,test_size=0.2 , random_state=i)\n",
        "  T = DecisionTreeRegressor()\n",
        "  pipe=make_pipeline(column_trans,T)\n",
        "  pipe.fit(x_train,y_train)\n",
        "  y_pred = pipe.predict(x_test)\n",
        "  scores1.append(r2_score(y_test,y_pred))"
      ],
      "metadata": {
        "id": "1RXJYv2VNUOx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(scores1)\n",
        "# random_state value 667 performs best for linear regressor let's check for the others"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lu1OqMJJPvIt",
        "outputId": "a1133849-8afc-4ff3-a3f6-7c59140d5128"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "49"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "in favor of random forest regressor"
      ],
      "metadata": {
        "id": "zFCZ6kdKO48M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scores2=[]\n",
        "for i in range(1000):\n",
        "  x_train, x_test,y_train,y_test = train_test_split(X,y,test_size=0.2 , random_state=i)\n",
        "  rf = RandomForestRegressor(n_estimators = 100)\n",
        "  pipe=make_pipeline(column_trans,rf)\n",
        "  pipe.fit(x_train,y_train)\n",
        "  y_pred = pipe.predict(x_test)\n",
        "  scores2.append(r2_score(y_test,y_pred))"
      ],
      "metadata": {
        "id": "SKefbVQbO9t-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores2[np.argmax(scores2)]\n",
        "# random_state value 13 performs best for linear regressor let's check for the others"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sbgmFAn7U1_t",
        "outputId": "cdd9a431-3956-42fd-e409-ddd89bf94afc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9390430103232675"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#splitting train test again for better results"
      ],
      "metadata": {
        "id": "hK5qVTumOcnE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test,y_train,y_test = train_test_split(X,y,test_size=0.2 , random_state=269)"
      ],
      "metadata": {
        "id": "wB8HZzfcOk9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "linear regressor"
      ],
      "metadata": {
        "id": "po4VBhndMQl7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()\n",
        "pipe=make_pipeline(column_trans,lr)\n",
        "pipe.fit(x_train,y_train)\n",
        "y_pred = pipe.predict(x_test)\n",
        "r2_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6pnyN9GZMTyx",
        "outputId": "cb7b9bb7-283e-4a77-d711-d8f75b989af7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8982174548987518"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create the Scatter plot of Linear Regression model\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.xlabel('y_test - True Car Price')\n",
        "plt.ylabel('Linear Regression - Predicted Car Price')\n",
        "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)])\n",
        "plt.tight_layout()\n",
        "plt.savefig('linear.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "QCETt4P3Uvq_",
        "outputId": "5e830ccd-ce5a-40e7-c1f7-b1276f79f62a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dislay MAE, MSE and RMSE for Random Forest Regressor\n",
        "print('MAE:', mean_absolute_error(y_test, y_pred))\n",
        "print('MSE:', mean_squared_error(y_test, y_pred))\n",
        "print('RMsE:', np.sqrt(mean_squared_error(y_test,y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rsbf7zjXQlKF",
        "outputId": "088c9d63-1880-4e9b-fbca-c37b73d57e51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE: 139984.40523690774\n",
            "MSE: 64030256115.67565\n",
            "RMsE: 253042.0046468089\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decison Tree Regressor"
      ],
      "metadata": {
        "id": "2lQoBe01MTPW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "T = DecisionTreeRegressor()\n",
        "pipe=make_pipeline(column_trans,T)\n",
        "pipe.fit(x_train,y_train)\n",
        "y_pred = pipe.predict(x_test)\n",
        "r2_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJJP_rdXMfdt",
        "outputId": "e1cbf8d8-9f4f-4a2b-9586-e8c3d89306a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8774347436465411"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create the Scatter plot of Linear Regression model\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.xlabel('y_test - True Car Price')\n",
        "plt.ylabel('Decision Tree Regressor - Predicted Car Price')\n",
        "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)])\n",
        "plt.tight_layout()\n",
        "plt.savefig('decisiontree.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "_82vcIcIVSVc",
        "outputId": "49905ba3-ae0d-4c0b-fd61-c0df192d914e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest Regessor"
      ],
      "metadata": {
        "id": "3YnISG3NMsy7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestRegressor(n_estimators = 100)\n",
        "pipe=make_pipeline(column_trans,rf)\n",
        "pipe.fit(x_train,y_train)\n",
        "y_pred = pipe.predict(x_test)\n",
        "r2_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3ovCs3GMhdD",
        "outputId": "c61aea8a-03c7-41d3-9db6-9ea336ca1fa1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9410712571715776"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create the Scatter plot of Linear Regression model\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.xlabel('y_test - True Car Price')\n",
        "plt.ylabel('Random forest Regression - Predicted Car Price')\n",
        "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)])\n",
        "plt.tight_layout()\n",
        "plt.savefig('Randomforest.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "zJo-7ssZVbAf",
        "outputId": "81fbcf98-1a7b-4b8a-8feb-9c0173a67775"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ensemble- Voting Regressor"
      ],
      "metadata": {
        "id": "yu_acqqWM-g2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "estimators = [('linear_reg', lr), ('tree_reg', T), ('forest_reg', rf)]\n",
        "ensemble = VotingRegressor(estimators)\n",
        "pipe=make_pipeline(column_trans,ensemble)\n",
        "pipe.fit(x_train,y_train)\n",
        "y_pred = pipe.predict(x_test)\n",
        "r2_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mEOXLl7M-CN",
        "outputId": "228d2b98-dd2c-4a50-e01c-147c3d50e708"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9404023927429903"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create the Scatter plot of Linear Regression model\n",
        "plt.figure(figsize=(12,8))\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.xlabel('y_test - True Car Price')\n",
        "plt.ylabel('Ensemble - Voting Regressor - Predicted Car Price')\n",
        "plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)])\n",
        "plt.tight_layout()\n",
        "plt.savefig('votingregressor.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "LHlPQ9cDV9pj",
        "outputId": "c53b66e8-8d88-481f-d8f4-0ee1638089e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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sUj84c6B+cu4QtW3FW1z4h+8+AAAANNqc5XnHjVnPyy/WtNkrJCkkJevDDfuUMTdbubsO6WtDuihj0kgN7tbW8/MCDaFgAQAAoNFmZeYedw8rSSour9SszFxPC9aOgmLdvXC15n++XSnJSfrLt8Zp4sjubAdE2KBgAQAAoNG25xc36nhzlVVU6a/vbtSfFq9VRZXpp+cO0f+cNUhJCbGenA9oKgoWAAAAGq1XcpLyailTvZKTgn6ut9fs0Yx52dqwt1DnDe+u2y8dob6dWwf9PEAwxPgdAAAAAJFn6sRUJcUfv3qUFB+rqRNTg3aOrfuLdPPzWbr+6Y9lkp757kl66vp0yhXCGitYAAAAaLSj11l5MUWwpLxSj721Xn95e71inNOvL0zVjWcMUKs4tgMi/FGwAAAA0CRT0lKCOtDCzPRazi7d8UqOth0o1qUn9NStlwxXzw7B33YIeIWCBQAAAN+t33NYM+bnaOmaPRrava1evOlknTaoi9+xgEajYAEAAMA3haUVenjxWj397kYlxsXq9ktH6Nun9lN8LKMCEJkoWAAAAAg5M9O8z7fr7oWrtOtgqa4c11u/uXCYurZr5Xc0oFkoWAAAAAip1TsP6va52fp4436NSmmvP183TuP6dfQ7FhAUFCwAAACEREFxuf7w+hr97cPNapcYp7svH61vntRHsTHO72hA0FCwAAAA4KmqKtNLy7bpvldXa39Rma47ua9+eX6qOrZJ8DsaEHQULAAAAHjmi235um1utj7fmq9x/TrqucnjNSqlg9+xAM94WrCcc8mSnpI0SpJJ+p6ZfeDlOQEAAOC//YVlmpW5Wv/8ZKs6t2ml3181Rpenpcg5tgMiunm9gvVHSa+a2ZXOuQRJrT0+HwAAAHxUUVmlFz/eogdfW6PDpRW68fQB+ul5Q9Q+Md7vaEBIeFawnHMdJJ0p6QZJMrMySWVenQ8AAAD++mTTft0+N1urdhzUaYM6a/rkkRravZ3fsYCQ8nIFa4CkPZKecc6NkbRM0s/MrPDYBznnbpZ0syT17dvXwzgAAADwwu6DJbpn0Wq9vDxPvTok6s/XjdVFo3qwHRAtkjMzb17YuXRJH0o63cw+cs79UdJBM7utruekp6dbVlaWJ3kAAAAQXGUVVXr2/Y364xtrVV5puvnMgfrRhEFqncAcNUQ/59wyM0uvedzL7/5tkraZ2UfVH78k6RYPzwcAAIAQeXftXmXMW6n1ewo1IbWrMiaNVP8ubfyOBfjOs4JlZjudc1udc6lmlivpXEk5Xp0PAAAA3tt2oEh3LVilRSt3qm+n1vrr9ek6d3h3v2MBYcPr9dufSHqheoLgBknf9fh8AAAA8EBJeaWeXLpBj761TpL0y/OH6qYzByoxPtbnZEB48bRgmdlnkr6yLxEAAACR481VuzRjfo627C/SxaN76NZLRiglOcnvWEBY4gpEAAAA1Grj3kLNnJ+tJbl7NKhrG/39xpN1xpAufscCwhoFCwAAAMcpKqvQo0vW6cmlG5UQF6NbLx6u60/rr4S4GL+jAWGPggUAAABJkplp4YqdunNBjnYUlOiKtBTdctEwdWuf6Hc0IGJQsAAAAKA1uw4pY262PtiwT8N7ttfD16TppP6d/I4FRBwKFgAAQAt2sKRcf3xjrZ59f5PatorTHZeN1LUn91NsjPM7GhCRKFgAAAAtUFWV6eXlebpn0WrtKyzV1Sf11dSJqerUJsHvaEBEo2ABAAC0MCvzCpQxL1vLNh/QiX2S9fQN6Tqhd7LfsYCoQMECAABoIQ4UlumB13L14sdb1Kl1gu6/8gRdOba3YtgOCAQNBQsAACDKVVaZ/vnJFs3KzNWhkgpdf2p//e/5Q9UhKd7vaEDUoWABAABEsWWbDyhj3kqtzDuokwd00ozLRmpYj/Z+xwKiFgULAAAgCu05VKr7Xl2tl5ZtU/f2rfTwNWmadEJPOcd2QMBLFCwAAIAoUl5Zpec/2KyHXl+jkopK/fCsQfrJOYPVphVv+4BQ4P80AACAKPH++r2aPi9ba3Yd1plDuypj0ggN6trW71hAi0LBAgAAiHA7Cop114JVeuWLHerdMUlPfHuczh/Rne2AgA8oWAAAABGqtKJST72zUY8sXqcqM/38vCH64VmDlBgf63c0oMWiYAEAgIg1Z3meZmXmant+sXolJ2nqxFRNSUvxO1ZILMndrRnzsrVpX5Emjuyu310yQn06tfY7FtDiBVSwnHNJkvqaWa7HeQAAAAIyZ3meps1eoeLySklSXn6xps1eIUlRUbLqKo9b9hVp5is5emPVLg3s0kbPfW+8zhra1e+4AKo1WLCcc5MkPSApQdIA59yJkmaa2WSvwwEAANRlVmbul+XqqOLySs3KzI34glVbebzl/77QopU7tCR3j+JinG65aJi+d/oAJcTF+JwWwLECWcGaLmm8pLckycw+c84N8DATAABAg7bnFzfqeCSprTyWVFQpM3uXJo/ppd9ePFw9OiT6lA5AfQL5J49yMyuoccy8CAMAABCoXslJjToeSeoriQ9fk0a5AsJYIAUr2zl3raRY59wQ59yfJL3vcS4AAIB6TZ2YqqQa0/KS4mM1dWKqT4mCp64ClRIF5RGIdoFsEfyJpFsllUp6UVKmpDu9DAUAANCQo9dZRdMUQTPT3M+2q7C04iufi5byGOla8uRKBMaZhc9uv/T0dMvKyvI7BgAAQMjlbD+o6fOy9fGm/TqhdwedM6yb/pO1jTfyYaTm8BHpSPG954rR/LdpgZxzy8wsvebxQKYIvi7pG2aWX/1xR0n/NLOJwY8JAADQshQUlev3r+fqbx9uVoekeN17xWhdld5HMTFOPz9vaMCvw8qK96J5ciWCJ5Atgl2OlitJMrMDzrluHmYCAACIelVVpn9nbdX9mbnKLyrTt07pp1+cP1TJrRMa/VrRfk+wcBHNkysRPIEUrCrnXF8z2yJJzrl+YoogAABAk322NV8Zc1fq820FSu/XUTMuG6+RvTo0+fVYWQmNXslJyqulTEXD5EoETyAF61ZJ7zrn3pbkJH1N0s2epgIAAIhC+w6X6v5Xc/WvrK3q2q6V/vDNMZpyYoqcc816XVZWQmPqxNRar8Fi+AiO1WDBMrNXnXNjJZ1SfejnZrbX21gAAADRo6KySi98tEUPvparorJK3fS1AfrpuUPULjE+KK/PykpoROPkSgRfnQXLOTfMzFZXlytJ2l79c9/qLYOfeh8PAAAgsn20YZ8y5mVr9c5DOmNwF02fPEKDu7UL6jlYWQmdKWkpFCrUq74VrF/oyFbAB2v5nEk6x5NEAAAAUWDXwRLdvXCV5n62Xb06JOqx68bqwlE9mr0dsDasrADho977YDnnYiSdambvhSIM98ECAACRrqyiSs+8t1EPv7lW5VWmH5w5UD86e7CSEmL9jgYgiJp0Hywzq3LOPSIpzbNkAAAAUWLpmj2aPj9bG/YU6rzh3XTbpSPUr3Mbv2MBCKFApgi+6Zz7uqTZVt9yFwAAQAu1dX+R7lyQo8zsXerXubWeviFd5wzr7ncsAD4IpGD9QEeux6pwzpXoyKh2M7P2niYDAAAIcyXllXr87Q3681vrFOOcpk5M1Y1nDFBiPNsBgZYqkDHtwR1zAwAAEOHMTG+s2q2Zr2Rr6/5iXXJCT9168XDGogOod0z7EEkPSBok6QtJU80sL1TBAABA8M1ZnsekuWbasOewZszP0dtr9mhIt7Z68fsn67TBXfyOBSBM1LeC9bSk5yUtlTRZ0p8kXRGKUAAAIPjmLM877l5JefnFmjZ7hSRRsgJQWFqhR5as01PvbFCruFj97pLhuv60/oqPjfE7GoAwUl/BamdmT1b/epZzjhsLAwAQwWZl5h53I1pJKi6v1KzM3KgqWMFepTMzvfLFDt21YJV2HizR18f21m8uSlW3dolBTA0gWtRXsBKdc2k6MtRCkpKO/djMKFwAAESQ7fnFjToeiYK9Spe785Ay5q3Uhxv2a2Sv9nr0ujSN69cpqJkBRJf6CtYOSb8/5uOdx3xsks7xKhQAAAi+XslJyqulTEXTYIZgrdIVFJfroTfW6PkPNqtdYpzunDJK14zvq9gY1/CTAbRodRYsM5sQyiAAAMBbUyemHre6I0lJ8bGaOjHVx1TB1dxVuqoq0/99uk33vbpa+wrLdM34vpp6Qao6tkkIZkwAUSyQ+2ABAIAocHQFJ5qnCDZnlW7FtgLdPm+llm/JV1rfZD1zw3iN7t3Bi5gAohgFCwCAFmRKWkpUFaqamrJKt7+wTLMyc/XPT7aoc5sEPfCNMboiLUUxbAcE0AT1FiznnJPU28y2higPAABAkzVmla6yyvTix1v0QGauDpdW6LunDdDPzx+i9onxoY4NIIrUW7DMzJxzCyWNDlEeAACAZglklW7Z5v26bU62cnYc1KkDO2v65JFK7dEuRAkBRLNAtgh+6pw7ycw+8TwNAACAh3YfKtG9i1Zr9qd56tkhUY9cm6ZLRvfUkU07ANB8gRSskyVd55zbLKlQR+6DZWZ2gqfJAAAAgqS8skrPvb9JD72xVqUVlfrR2YP04wmD1aYVl6MDCK5A/lSZ6HkKAAAAj7y3bq8y5mVr3e7DOju1q26/dIQGdm3rdywAUarBgmVmmyXJOddNUqLniQAAAIIgL79Ydy9YpQUrdqhPpyQ99Z10nTu8m6/bAecsz4vqMfkAAihYzrnJkh6U1EvSbkn9JK2SNNLbaAAAAI1XUl6pp97ZoEeXrFeVmX5x/lDdfOZAJcbH+pprzvK840bI5+UXa9rsFZJEyQKiSCBbBO+QdIqkN8wszTk3QdK3Anlx59wmSYckVUqqMLP0pgYFAABoyOLVuzRjfo427yvShSN76NZLhqtPp9Z+x5J0ZHT8sffnkqTi8krNysylYAFRJJCCVW5m+5xzMc65GDNb4px7qBHnmGBme5saEAAAoCGb9xVq5vwcvbl6twZ2baO/3TheXxvS1e9Yx9meX9yo4wAiUyAFK98511bSUkkvOOd268g0QQAAAF8VlVXoz0vW64mlGxQf6/Tbi4fphtMGKCEuxu9oX9ErOUl5tZSpXslJPqQB4JVA/vS5TFKRpP+V9Kqk9ZImBfj6Juk159wy59zNtT3AOXezcy7LOZe1Z8+eAF8WAAC0ZGamhSt26LwH39YjS9bp4tE9tPhXZ+vmMweFZbmSpKkTU5VU4zqwpPhYTZ2Y6lMiAF6ocwXLOTdYUncze6/6UJWk55xzZ0hKlrQvgNc/w8zyqicQvu6cW21mS499gJk9IekJSUpPT7em/CYAAEDLsXbXIU2fn6331u3TsB7t9NDVaRo/oJPfsRp09DorpggC0a2+LYIPSZpWy/GC6s81uIplZnnVP+92zr0sabyObDUEAABolEMl5Xr4zbV65r1Nap0QqxmTR+q6k/sqLjY8V6xqMyUthUIFRLn6ClZ3M1tR86CZrXDO9W/ohZ1zbSTFmNmh6l9fIGlmU4MCAOAl7k8UvsxMcz7L090LV2vv4VJ9M72Ppk5MVee2rfyOBgBfUV/BSq7nc4Fcjdld0svVN/OLk/Simb3aiGwAAIQE9ycKX9nbC5QxN1tZmw9oTO8OevI76Tqxz1ffolCQAYSL+gpWlnPuJjN78tiDzrnvS1rW0Aub2QZJY5qZDwAAz3F/ovCTX1SmB19boxc+2qzk1gm67+uj9Y1xfRQT477yWAoygHBSX8H6uY6sQF2n/xaqdEkJki73OhgAAKHC/YnCR2WV6d9ZW3X/q6tVUFyu75zaX/973lB1aB1f53MoyADCSZ0Fy8x2STrNOTdB0qjqwwvMbHFIkgEAECJ+3p+IrW3/9emWA8qYm60VeQUa37+Tpk8eqRG92jf4PAoygHDS4I2GzWyJpCUhyAIAgC+mTkw9bouZFJr7E7G17Yi9h0t136LV+s+yberevpX+ePWJmjyml6qv424QN/AFEE4iZ64pAAAemZKWonuuGK2U5CQ5SSnJSbrnitGel5z6tra1BBWVVXrmvY2a8MBbenl5nn5w1kC9+cuzddmJKQGXK4kb+AIILw2uYAEA0BL4cX+ilry17cMN+5QxN1u5uw7pa0O6KGPSSA3u1rZJr8UNfAGEEwoWAD6gzHAAACAASURBVAA+aYlb23YUFOvuhas1//PtSklO0l++NU4TR3Zv1IpVbbiBL4BwUWfBcs4dkmR1fd7MGr7qFAAA1Mmva7/8UFpRqaff3aQ/LV6riirTz84doh+eNUhJCbENPxmeYMAK4I36pgi2kyTn3B2Sdkj6myQn6TpJPUOSDgCAKNZStra9vWaPZszL1oa9hTp/RHfddskI9e3c2u9YLbpgMGAF8I4zq3OR6sgDnPvczMY0dCwY0tPTLSsrK9gvCwAAfLB1f5HueCVHr+Xs0oAubXT7pBEqKCoPi1JTs2BIR1YPQzHcJBycfu/iWrenpiQn6b1bzvEhERB5nHPLzCy95vFArsEqrL7Z8D91ZMvgNZIKg5wPAAAEQTisypSUV+qxt9brL2+vV4xz+vWFqbrxjAFatGJn2KyaTJ+X3aJvTtySB6wAXgukYF0r6Y/VP0zSe9XHAABAGPF725eZ6bWcXbrjlRxtO1CsSWN66bcXD1PPDkeGdtQ3lj6UpWbO8jzlF5fX+rmWUjBa4oAVIFQCudHwJkmXeR8FAAA0h58FZv2ew5oxP0dL1+zR0O5t9Y+bTtGpgzof95hwWTWp7z5jLaVgtKQBK0CoNViwnHNDJT0mqbuZjXLOnSBpspnd6Xk6AAAQsNpWJOo7HgyHSyv0p8Vr9fS7G5UYF6vbLx2hb5/aT/GxMV95bLismtRX6FpKwWgpA1YAPwSyRfBJSVMlPS5JZvaFc+5FSRQsAADCSKxzqqxleFVsLfeYau61WmameZ9v190LV2nXwVJ9Y1xv/frCYerarlWdzwmXVZO6il7H1vEtqmBw7zDAG4EUrNZm9nGNGwBWeJQHAICIEw6DJSTVWq5qO97ca7VW7TiojHnZ+njjfo1Kaa/HvjVOY/t2bPB54bJqUlfRy5g0MqQ5AESnQArWXufcIFXfdNg5d6WO3BcLAIAWz+/BEsdKqWNlJqXGFrymXqtVUFyuP7y+Rn/7cLPaJcbp7stH65sn9VFszFdXyOoSDqsm4VL0AESnQArWjyU9IWmYcy5P0kYdudkwAAAtXrhMxpMC34LX2GETVVWml5Zt032vrtaBojJde3Jf/fL8VHVskxC88CEWDkUPQHQKpGCZmZ3nnGsjKcbMDjnnBngdDACASBAuk/GkwFdmGjNs4vOt+bp9XrY+35qvcf066rnJ4zUqpYM3vwEAiAKBFKz/kzTWzI69ufBLksZ5EwkAgMgRLpPxjgpkZSaQla79hWWalbla//xkqzq3aaXfXzVGl6elyNUyMAMA8F91Fizn3DBJIyV1cM5dccyn2ktK9DoYAACRIFwm4zVGfStdFZVVevHjLXrwtTUqLK3QjacP0M/OG6J2ifE+pwaAyFDfClaqpEslJUuadMzxQ5Ju8jIUAACRIlIHJtS20vXJpv26fW62Vu04qNMGddaMySM1pHs7nxICQGRyVsdI1y8f4NypZvZBKMKkp6dbVlZWKE4FAACq7T5YonsWrdbLy/PUq0OifnfpCF00qgfbAQGgHs65ZWaWXvN4INdg/dA5t8rM8qtfqKOkB83se8EOCQAAQqesokrPvr9Rf3xjrcorTf9vwmD9aMIgtU4I5O0BAKA2gfwJesLRciVJZnbAOZfmYSYAAOCxd9fuVca8lVq/p1DnDOum2y8dof5d2vgdCwAiXiAFK8Y519HMDkiSc65TgM8DAABhZtuBIt21YJUWrdypfp1b66/Xp+vc4d39jgUAUSOQovSgpA+cc/+R5CRdKekuT1MBAICgKimv1JNLN+jRt9ZJkn51wVB9/2sDlRgf63MyAIguDRYsM3veOZcl6ZzqQ1eYWY63sQAAQLC8kbNLM1/J0Zb9Rbp4dA/deskIpfh0ny4AiHb13QervZkdrN4SuFPSi8d8rpOZ7Q9FQAAA0DQb9xZq5vxsLcndo8Hd2uqF75+s0wd38TsWAES1+lawXtSR+2Atk3TsLHdX/fFAD3MBAIAmKiqr0KNL1unJpRuVEBej310yXNef1l/xsTF+RwOAqFdnwTKzS6t/HhC6OAAAoKnMTAtX7NSdC3K0o6BEV6Sl6JaLhqlb+0S/oyGE5izPi7gbXwPRpL4tgmPre6KZfRr8OAAAoCnW7DqkjLnZ+mDDPo3o2V5/uiZN6f07+R3LM5SI2s1Znqdps1eouLxSkpSXX6xps1dIEl8fIETq2yL4YPXPiZLSJX2uI9sDT5CUJelUb6MBAI7izSTqcrCkXH98Y62efX+T2raK0x1TRuna8X0VG+P8juYZSkTdZmXmfvl1Oaq4vFKzMnNb/NcGCJX6tghOkCTn3GxJY81sRfXHoyRND0k6AABvJlGrqirTy8vzdM+i1dpXWKqrT+qrqRNT1alNQrNfO9wLPSWibtvzixt1HEDwBXIfrNSj5UqSzGylc264h5kAAMfgzSRqWplXoNvnrtSnW/J1Yp9kPX1Duk7onRyU146EQk+JqFuv5CTl1fJ16MVYfiBkAhkn9IVz7inn3NnVP56U9IXXwQAAR/BmEkcdKCzTrS+v0KRH3tXmfUW6/8oTNPt/TgtauZLqL/Thoq6yQImQpk5MVVKNm0cnxcdq6sRUnxIBLU8gK1jflfQ/kn5W/fFSSY95lggAcBz+Rfqrwn0LW7BVVpn++ckWzcrM1aGSCt1wWn/9/Lyh6pAUH/RzRUKhnzox9bhVNokScdTR/w9a0v8fQLhpsGCZWYlz7i+SFppZ+PzzFQC0ELyZPF4kbGELpmWbDyhj3kqtzDuokwd00ozLRmpYj/aenS8SCj0lon5T0lL4WgA+arBgOecmS5olKUHSAOfciZJmmtlkr8MBAHgzWVNLuSZtz6FS3btotf7v023q3r6VHr4mTZNO6CnnvJ0OGCmFnhIBIFwFskUwQ9J4SW9Jkpl95pzj5sMAEEK8mfyvSNjC1hzllVV6/oPNeuj1NSqpqNT/nD1I/2/CYLVpFchf2c1HoQeA5gnkT+tyMyuo8S9m5lEeAADqFQlb2Jrq/fV7NX1ettbsOqwzh3bV9EkjNLBr25DnoNADQNMFUrCynXPXSop1zg2R9FNJ73sbCwCA2kXKFrbG2J5frLsWrtKCL3aod8ckPfHtcTp/RHfPtwMCAIIvkIL1E0m3SiqV9KKkTEl3ehkKAIC6RNMWttKKSj31zkY9snidqsz0v+cN1Q/OGqjEGmO2AQCRw5nVvdvPORcr6Q0zmxCKMOnp6ZaVlRWKUwEA4Kslubs1Y162Nu0r0sSR3fW7S0aoT6fWfsf6UksbhQ8AjeWcW2Zm6TWP17uCZWaVzrkq51wHMyvwLh4AAC3Dln1FmvlKjt5YtUsDu7TRc98br7OGdvU71nFa2ih8AAimQLYIHpa0wjn3uqTCowfN7KeepQIAIMoUl1XqsbfW6S9LNyguxumWi4bpe6cPUEJcjN/RvqKljMIHAC8EUrBmV/8AAACNZGbKzN6pO15Zpbz8Yk0e00u/vXi4enRI9DtanaJ9FD4AeKneguWcmyKpq6QVZpYZmkgAAESHdbsPa8b8bL2zdq+G9Winf958ik4Z2NnvWA2K5lH4AOC1OguWc+7PkkbqyEj2O5xz483sjpAlAwAgQh0urdDDb67V0+9uVFJCrKZPGqFvndJPcbHhtx2wNtE4Ch8AQqW+FawzJY2pHnTRWtI7kihYAADUwcw097PtunvhKu0+VKqr0nvr1xcOU5e2rZr8mn5M84umUfgAEGr1FawyM6uUJDMrctztEACAOuVsP6jp87L18ab9OqF3Bz3+7XFK69uxWa/p5zS/KWkpFCoAaIL6CtYw59wX1b92kgZVf+wkmZmdEMgJqu+llSUpz8wubVZaAADCTEFRuR58PVd//3CzOiTF694rRuuq9D6KiWn+v0syzQ8AIk99BWt4kM7xM0mrJLUP0usBAOC7qirTv7O26v7MXOUXlelbp/TTL84fquTWCUE7B9P8ACDy1FmwzGxzc1/cOddb0iWS7pL0i+a+HgAA4eCzrfnKmLtSn28r0En9O2rG5JM1olfw/x2RaX4AEHm8Hmf0kKRfS6qq6wHOuZudc1nOuaw9e/Z4HAcAgKbbd7hUv3npC0159D1tLyjRQ988Uf/+wamelCvpyDS/pPjY444xzQ8AwlsgNxpuEufcpZJ2m9ky59zZdT3OzJ6Q9IQkpaenm1d5AABoqorKKr3w0RY9+FquisoqdfOZA/WTcwarXWK8p+dlmh8ARJ5GFSzn3Fgz+zTAh58uabJz7mJJiZLaO+f+bmbfamxIAAD88tGGfcqYl63VOw/pjMFdNH3yCA3u1i5k52eaHwBElsauYD0laWwgDzSzaZKmSVL1CtavKFcAgEix62CJ7l64SnM/266U5CQ9dt1YXTiqh7hrCQCgPo0tWPytAgCIamUVVXrmvY16+M21Kq8y/fScwfqfswcrKSG24ScDAFq8xhasGU05iZm9JemtpjwXAIBQWbpmj6bPz9aGPYU6b3g33XbpCPXr3MbvWACACNKogmVmc7wKAgCAX7buL9KdC3KUmb1L/Tu31jM3nKQJw7r5HQsAEIE8myIIAEC4Kymv1ONvb9Cf31qnGOc0dWKqvv+1AWoVx3ZAAEDTULAAAC2OmemNVbs185Vsbd1frEtO6KlbLx7ODXwBAM1Wb8FyzsVKyjazYSHKAwCIAnOW54XtvZs27DmsGfNz9PaaPRrSra1e/P7JOm1wF79jBU04f+0BoCWot2CZWaVzLtc519fMtoQqFAAgcs1Znqdps1eouLxSkpSXX6xps1dIkq9v9AtLK/TIknV66p0NSoyL1W2XjtB3Tu2n+NgY3zIFW7h+7QGgJQlki2BHSdnOuY8lFR49aGaTPUsFAIhYszJzv3yDf1RxeaVmZeb68ibfzPTKFzt014JV2nmwRF8f21u/uShV3dolhjyL18Ltaw8ALVEgBes2z1MAAKLG9vziRh33Uu7OQ8qYt1Ifbtivkb3a69Hr0jSuX6eQ5wiVcPraA0BL1WDBMrO3nXPdJZ1UfehjM9vtbSwAQKTqlZykvFre0IdygERBcbkeemONnv9gs9olxumuy0fp6pP6KjbGhSyDH8Lhaw8ALV2DG8+dc1dJ+ljSNyRdJekj59yVXgcDAESmqRNTlRR//JjzpPhYTZ2Y6vm5q6pM/8naqnMffEvPvr9JV5/UR0t+ebauO7lf1Jcryd+vPQDgiEC2CN4q6aSjq1bOua6S3pD0kpfBACDUmL4WHEe/ZqH+Wq7YVqDb563U8i35Gts3Wc9+d7xGpXTw9Jzhxq+vPQDgv5yZ1f8A51aY2ehjPo6R9Pmxx4IlPT3dsrKygv2yANCgmtPXpCP/8n/PFaN5cxrm9heWaVZmrv75yRZ1bpOgWy4arivSUhTTAlasAAD+cc4tM7P0mscDWcF61TmXKekf1R9/U9LCYIYDAL8xfS3yVFaZXvx4ix7IzNXh0gp97/QB+tl5Q9Q+Md7vaACAFiyQIRdTnXNXSDqj+tATZvayt7EAILSYvhZZsjbt1+1zs5Wz46BOHdhZMy4bqaHd2/kdCwCAhguWc66NpLlmNts5lyop1TkXb2bl3scDgKZrzDVVTF+LDLsPlejeRas1+9M89eyQqEeuTdMlo3vKueZvB+QaPABAMASyRXCppK855zpKelVSlo5sE7zOy2AA0Bw1r6nKyy/WtNkrJKnWN81TJ6bWeg0W09fCQ3lllZ57f5MeemOtyiqq9KOzB+nHEwarTatA/hprWGO/XwAAqEsgfzM5Mytyzt0o6TEzu98595nXwQCgORp7TRXT18LXe+v2KmNettbtPqyzU7sqY9JIDejSJqjnqOv7Zfq8bL4nAACNElDBcs6dqiMrVjdWH4ut5/EA4LumXFM1JS2FN89hJC+/WHcvWKUFK3aoT6ckPfWddJ07vFtQtgPWVNf3RX5xufKLy7/Mw6oWAKAhDd5oWNLPJU2T9LKZZTvnBkpa4m0sAGieuq6d4pqq8FdSXqlHFq/VhFlvaeGKHZKkykrT4dIKT8qVFPj3xdFVUAAA6tJgwTKzt81sspndV30PrL1m9tMQZAOAJps6MVVJ8ccvtnNNVfhbvHqXJj60VA+8tkYVVVU6eqfG7QUlmjZ7heYsz/PkvLV9v9SFyZIAgPo0WLCccy8659pXTxNcKSnHOTfV+2gA0HRT0lJ0zxWjlZKcJCcpJTmJmwaHsc37CnXjs5/oe89mKS7GqXObBFXZ8Y/xcvWotu+Xjq1rv59Wc1ZB5yzP0+n3LtaAWxbo9HsXe1YYAQD+CeQarBFmdtA5d52kRZJukbRM0ixPkwFAM3FNVfgrKqvQn5es1xNLNyg+1um3Fw/TDacNUOrvFtX6eC9Xj2p+v8xZnqepL32u8sr/Nr34WNfkVVAmFQJAyxBIwYp3zsVLmiLpETMrd85ZQ08CAKAuZqZFK3fqzldytL2gRJenpeiWi4ape/tESWF0X7Kaf9s142+/xk62BABEpkAK1uOSNkn6XNJS51w/SQe9DAUAiF5rdx3S9PnZem/dPg3r0U4PXZ2m8QM6HfeYYN6XrKk3EJ6VmavyGvsUy6tMM+Y3bXR7UyZbAgAiT4MFy8welvTwMYc2O+cmeBcJABCNDpWU6+E31+qZ9zapdUKsZl42UteO76u42K9eDhys+5I1Z1tebStoknSgqFwHiho/uj1sVuUAAJ5qsGA557pLultSLzO7yDk3QtKpkv7qdTgAQOQzM728PE/3LFqtvYdL9c30Ppo6MVWd27aq93nBuIauOdvyYp1TpTW8JzDQ1wvmqhwAIHwFskXwWUnPSLq1+uM1kv4lChYAoAHZ2wuUMTdbWZsPaEyfZD31nXSN6ZMcsvM3Z1teIOWqMa8XrFU5AEB4C6RgdTGzfzvnpkmSmVU45yobehIAoOXKLyrTg6+t0QsfbVZy6wTd//UTdOW43oqJ8eZGwXVpzra8lDqeW9d5AhGKyZZNveYMABAcDd4HS1Khc66zqmcnOedOkVTgaSoAQESqrDK9+NEWTXjgLb3w0WZ959T+WvLLs3XVSX1CXq6k5t1wurbnxsc4xcce//sIp21+R685y8svlum/14hxvy0ACJ1AVrB+IWmepEHOufckdZV0paepAAAR59MtB5QxN1sr8go0fkAnnT20q174aIuee3+TbyspzdmWV9dzm/p6ocAoeADwn7N69pg752Il/VTSnySlSnKScs2s3Isw6enplpWV5cVLAwA8sudQqe5/dbX+s2yburdvpd9ePFxVVabfvrzyuDf78bFObRLiVFBcHnbFJFoMuGVBrbfqcpI23ntJqOMAQFRzzi0zs/Sax+tdwTKzSufcNWb2B0nZnqUDAEScisoq/e3Dzfr962tUUl6pH5w1UD85Z4jatorT6fcu/spKSnmlKb+48ePNEThGwQOA/wLZIviec+4RHZkcWHj0oJl96lkqAMCXwnFowQfr92n6vGzl7jqkrw3pooxJIzW4W9svPx/IVL26tq6F2+833PLUh1HwAOC/QArWidU/zzzmmEk6J/hxAADHas6Ncr2wo6BYdy9crfmfb1dKcpIe//Y4XTCiu5w7fvBDXSspNdUsYuH2+w23PA1hFDwA+K/BgmVmE0IRBABwxLErJjG13OzWj6EFpRWVevrdTfrT4rWqqDL97Nwh+uFZg5SUEFvr42tbSalNza1r4TakIdzyBCIUo+ABAHVrsGA5535Ry+ECScvM7LPgRwKAlqvmikldN7sNZAtesLyVu1sz5+dow95CnT+iu267ZIT6dm5d73NqrqQkt47X4ZIKlVf99/dT29a15twY2AvhlgcAEP4C2SKYXv1jfvXHl0r6QtIPnXP/MbP7vQoHAC1NbSsmtQnF0IKt+4s085UcvZ6zSwO6tNGz3z1JZ6d2C/j5NVdSArmWKdyGNDQnTyRduwUACJ5AClZvSWPN7LAkOecyJC2QdKakZZIoWAAQJIGsjHg9tKCkvFKPvbVef3l7vWJjnH5z4TB974z+ahVX+3bAQAWydS3chjQ0NU+kXbsFAAieQApWN0mlx3xcLqm7mRU750rreA4AoAnqWjGJdU5VZp6uhJiZMrN36c4FOdp2oFiTxvTSby8epp4dQrd6FG5DGpqaJxKv3QIABEcgBesFSR855+ZWfzxJ0ovOuTaScjxLBgAtUF0rJvdcMdrTN+br9xzW9HnZemftXqV2b6d/3HSKTh3U2bPz1SfchjQ0JQ/XbgFAyxXIFME7nHOLJJ1efeiHZpZV/evrPEsGAC1QqFdwDpdW6E+L1+rpdzcqMS5WGZNG6Nun9FNcbIwn52spwu1aMgBA6ASygiVJiZIOmtkzzrmuzrkBZrbRy2AA0FKFYgXHzDTv8+26e+Eq7TpYqm+M661fXzhMXdu18vS8LUW4XUsGAAidQMa0Z+jIFMFUSc9Iipf0d/13RQsAasUUtfC0asdBZczL1scb92t0Sgc99q1xGtu3o9+xQsrr781wu5YMABA6gaxgXS4pTdKnkmRm251z7TxNBSDiRcMUtWgriAXF5frD62v0/Aeb1CEpXndfPlrfPKmPYmOc39FCKlTfm+F2LRkAIDQCKVhlZmbOOZOk6uEWAFCvSJ+iFg0F8aiqKtNLy7bpvldX60BRma47uZ9+ecFQJbdO8DuaLyL9exMAEN4CKVj/ds49LinZOXeTpBslPeVtLACRLtKnqEXLm/DPt+br9nnZ+nxrvsb166jnJo/XqJQOfsfyVaR/bwIAwlsgUwQfcM6dL+mgjlyHdZuZve55MgARLdKnqEX6m/B9h0s1KzNX/8raqi5tW+n3V43R5Wkpcq5lbQesTaR/bwIAwlu9c3idc7HOuS5m9rqZTZX0W0kDnHOrQhMPQKSaOjFVSfGxxx2LpClqdb3ZDvc34RWVVXr+g02a8MBbemnZNn3/jAFa/MuzdMXY3pSrapH+vQkACG91rmA5566W9LikQufcWkl3SXpa0ifi/lcAGhDpU9Qiccz2J5v26/a52Vq146BOH9xZ0yeN1JDuzCSqKdK/NwEA4c2ZWe2fcG6lpClmts45N1bSB5KuNLP5XoVJT0+3rKyshh8IACEQ6imCTT3froMlumfhKs35bLt6dUjU7y4doYtG9YjYFatom94IAIhOzrllZpZe83h912CVmdk6STKzT51za70sVwAQbkI5ZruhqYXHlo4OSfFyTjpQVK72iXEqraiSmfT/JgzWjyYMUuuEQO8hH36iaXojAKBlqu9v4W7OuV8c83HysR+b2e/re2HnXKKkpZJaVZ/nJTPLaE5YAKhPJK981De1UNJxpSO/uPzLxxwsqVCMk6ZdNFw3nTkwdIE9Ei3TGwEALVd9Qy6elNTumB81P25IqaRzzGyMpBMlXeicO6V5cQGgdkdXPvLyi2X678rHnOV5fkcLSH1TC2srHceqMunZ9zd5lCy0In16IwAAda5gmdmM5rywHbm463D1h/HVP2q/4AsAminSVz7qGx0eSLmIlgLCCHUAQKSrd0x7c1WPef9M0m5Jr5vZR7U85mbnXJZzLmvPnj1exgEQxSJ15WPO8jydfu9i5eUXq+ZIiqT4WP3qgqHq2CahwdeJlgLCCHUAQKTz9EpoM6uUdKJzLlnSy865UWa2ssZjnpD0hHRkiqCXeQBEr0hc+ag50MEkueqfU5KTdMNp/TXv8+3aX1j25fHaeFVA/LimjRHqAIBIF5JRU2aW75xbIulCSSsbejwANFYk3reqtm2NJqlnh0RNSeulWZm5SoiL0e8uGa6OrRP0+9fXHDdFML+o3LMC4uc0v1BObwQAINgaLFg1JgkeVSBpmZl9Vs/zukoqry5XSZLOl3Rfk5MCQD0iceWjru2LOwpK9OiS9bpibIpuuXCYurVPPG5YR5tWcXX+3oK16hTp17QBAOCXQFaw0qt/HL0H1qWSvpD0Q+fcf8zs/jqe11PSc865WB251uvfZvZKcwMDQF0ibeWjrm2N8bFO/7jpFKX37yQp8NWkYK46eX1NWyi3H0by+H4AQOQJZMhFb0ljzeyXZvZLSeMkdZN0pqQb6nqSmX1hZmlmdoKZjTKzmUFJDABRYurEVCXGHf/HcHyM031XnPBluZIavkdWYx8XiLquXQvGNW2hHKkf6eP7AQCRJ5CC1U1H7ml1VLmk7mZWXOM4gChydLrdgFsW6PR7Fzf7DWmwXy/SVVWZKqpM8ccUrB7tEzXrG2P+f3v3HydXXd97/P3Z3UkyCZAFiQpLAhE1UW8kkVXQ1NagghZ/RKwiYm+tVdve1iv+SBv8BVgqUaribatXRGu9oiIGVjSUqE380ViQDQmEQGJFfg5UomEDJJvsr8/9Y2Y2s7PnnDkzc2bOzM7r+Xjkwe7M2XO+Z4bAfPbz/b6/OufUE6YcG9Y1yg0NT3ktk+w6NTLNL8lCsJWuBQCAFG+K4NWSbjGz7xa+f62kb5jZPEl3NWxkAFKTdMBBmoEJrejO3D597Lt36rYHhrR8Ya8+/vrn6fkn9IYeHzaVUJr6Ws7PZjQ0PDrtmPnZTNVjbOSatmZG6rdrfD8AoH1VLLDc/e/M7CZJLyk89BfuPlj4+vyGjQxAapIOOCAwIe/qm+/XJ268W/tHxtVl0nkvWqi/X71MXV3lO2BNddJTwgss6fBraSGnCXu8kkataWtmpH47xvcDANpb3I2Gb5N0raTrJT1qZosaNyQAaUv6t/6N6CK005TD8QnXmmtv14cH7tT+kXyhOeHSwLaHdcPtD0f+7MC2nH5+z96K13h4aFhDB6Z3rySFPp6WZm4mzMbFAIBmixPT/h5JF0n6jaRxHd4H8/mNHRqAtCT9W/+kz9dOUw633v+YLrrhTt2Ze3zac3G6eJdv3B26wXCpLjONe/CR9XRrGpHA18xI/XaM7wcAtLc4a7DeK2mJu/+u0YMBMFVa8dJJb9obdb4491h+zIGRscSnHNb6Wof9MtjWqwAAIABJREFU3KNPHNQn/2231t/2kJ5+1JzQn6/UxYvb5Qsrrup53xpZyDYzUj+paxH3DgCII06B9aDyGwsDaKI0uzRJ/9Y/7HySKt5j0OsQptYph7W+1kE/t3b9Hfrx7kf173c/qoNj4/rLl52sv171TJ352Z/W1MWLCriQpO6IzlVfne8ba+cOa6euKQAgXeYh/2OePMDsy5KWSNqgklh2d/9M0oPp7+/3wcHBygcCHWDluk2BH6z7erPasvaMFEaUvDj3GHZMkFpfm1pf66ixLX36kdq7f0R7njik43uzWrV0gdZvzU3r4l12zrKqijgpP0/7/NMX6dLVy7R47YbAKYQm6d51Z4eeN45GnrvddMLfRwBAdcxsq7v3lz8ep4P1QOHPrMIfAE3QCfHSce4x7v3WMxWu1tc66vn7frtfB8cmJOW7Heu35vTGU/u0edeeqrqClbqJjUzJI4HvsE74+wgASEacmPZLmjEQAFN1wofbOPcYdkxvNqN5s3sSmcJY62t93Pw5enjfwWmPd5tNFldFw6Pj2rxrT03djvI1RMUExYeHhjU/m1Gm2zQ6frjXVG9KXnGtUW5oeDLVKKlzt6tO+PsIAEhGaIFlZle4+wVm9j1p+iwRd39dQ0cGdLikgybSFhQQEOceVy1doKtvfmDah/yLX/e8ute+1FNIbN71qEbGJ6Y9ns10T1u3VJREt6N8yuDQ8Ki6JHVZPvq920xvPLX2UIfy87sOR8fWu6arnc20v48AgMaJ6mD9v8I//6EZAwEwVdrx0kkmpoUFBFx2zjJdds6y0OsMbMtp/dbclMLHpLoKiLAxBRUSkiY7RcWxvWDR0fr493fqR3c/qmccO09vfMEJ+v4dj0w5pli0lUui2xEUPDFRvAHl0wTXb82p/8RjEtsUuviadPJao7T/PgIA2kdogeXuWwtfLnf3z5U+Z2bvlfSTRg4MQHOjrEslnZgWlUa3Ze0ZoecM+7C/edeeqscQ99zFQiLoNfjgtbdLkmb1dGntq5fqHSsXa1ZPly78w+dMO3+juh1xumD1JP2x1ihcWn8fAQDtpSvGMX8S8NjbEx4HgBYSVRDVIukQiSQ+7Fc6d9BrMDbhynR3adMHXqa/+IOTNasn+D+hq1f06bJzlqmvNytTvmgLSwssrqdavHaDVq7bpIFtuchxx+2C1foahZ2ftUYAAMQTtQbrPElvlbTYzG4oeeooSXsbPTAA6Um6sKk1ICDNhLywez04Oq6nzw/fOLgoTrejlk5h0FqgILW+Rqw1AgCgPlEdrJ9L+rSkXYV/Fv+8X9JZjR8akK5qOwszSdJdjDVnLVE20z3lsTgf2qN+rt73J+rcTx4a07zZwb9/SrKTU0unsLw71ltIESxVT0FUTfcNAABMF7UG635J90t6sZk9TdILC0/d7e5jzRgckJak1yC1m6S7GLUGBIT9nKS635/yc8/PZiS5Lrhm+5REvnFPLv68XK2dwqDY9iTDF1hrBABA7cx9WgL71APM3qR8kuCPlQ/ZeqmkNe7+naQH09/f74ODg0mfFqjaynWbAqePdVKSWtIf2pOU9PszsC2nv11/hw6V7F01u7tLb3rhCVVvDBx07rDXkX/PAABoX2a21d37yx+vuNGwpI9IeqG7P1o40QJJP5KUeIEFtAqS1Fq7i5Hk+7PvwKg+fP2OKcWVJB0an6h5Y+CiqE6oJO0/NH0yAOudAABob3EKrK5icVXwO8VLHwTaViPDFVC/JN6fiQnXtwcf1Kc27tb+kcZsDBy2xuqS7+3UwdGJac8dPTeji15b/wbKAAAgPXEKpZvMbKOZvd3M3i5pg6QbGzssIF21hjKgOSq9P5UCMLY/OKQ3fH6L1l63QycvmKcj5wT/rql3bqaucYYVaI8dGA1MAZw7q4fiCgCANhcV0/4mSd9z9zVmdo6k3ys8daW7X9+U0QEpqTWUAc0R9f5ETcv7vWcdq8tv2q1rBh/UU4+crSvOXa7XLz9ez/vYTYHXOVQhCr2SsE5bmE6aggoAwEwVNUXwrZL+2cw2Svqm8sEW9X3aANpIK69BQvj7EzYt74JrtsskmUnv/v1n6H+//Fk6ohDFfmB0Ytp5oh6PKyyNcXZPl4aGR6cdzxTUvFYOWJFaf3wAgHSFThF09zdIeqbygRbvkfSQmf1fM/uDZg0OAKoV1QVySZnuLj33uKMmi6tGCttT6uLXPY8pqCGKHcjc0LBchzuQrbIPXauPDwCQvshPGO7+uKR/lfSvZvYUSX8k6f+Y2THuvrAZAwSAalSalndobEKXb9w9peMwN9MV2K2am6k/zyeqE0oXZLqozZdb4fVp9fEBANIX61e4Zna0pHMknSvpGBHRDjQN05Gq875XPEtrr9uhsYnwPf7Ku1yzeroDC6xZPd3THksKU1CDtfoWCa0+PgBA+qJCLo6Q9AZJ50laIekGSX8n6cdeaXdiAImICmxotw/nzSgUf/rLPfr8T+7R2IRrTk+XDo4Fr6EqX+u0L2A9VNTjaJxW3yKh1ccHAEhfVAfrPkk3Sfq8pI3uzicNoEmKxUjQB7l2nI7U6ELxwb0HdOmGu7Rx52900lPm6l/e/kKtWvrUadeV8mudVi1doJXrNk0We/OzGUInWkRYMEirrE9r9fEBANIXVWAtdHfmPABNFlQUlGu36UiNWrdycHRcX/zJr/X5H/9KXWZac9YSvfOlizW7MLUvKM591dIFWr81N6XYy3SbMl2m0ZJphXxoTkerb5HQ6uMDAKQvtMCiuALSEVSMlGu3zkrS61bcXT+86zf6+Pfv0kOPDevs5x+nD//hcwJfl/K1TivXbZr2+o6Ou8yko+dmNHRgtKkfmlljN12rr09r9fEBANLV+JxiAFWpVHS0SmelmsKg3nUrpddacORsHTNvlnb99xN69tOO0DfedZpecvKxsccU9vq6SwdHJ/TZc5c37cPzTFpjBwAA8qoqsMzs6e7+340aDIDomPG+FulwVFsY1LNupfxajz5xSI8+cUirlx+vy990ijLdXRXHJB2e0tVlpvGQnJ5mr28j8hsAgJmn2g7WjZJe0IiBAMgLK0YuO2dZy3zorrYwqGfdyqdu2hU4ZfLW+x6bLK6ixnTJ93bq4OjE5HNhxVVRM9e3EfkNAMDMU22BZQ0ZBYBJ7bCIvpbCoJZ1K7v/+wk9vO9g4HO5oWGtXLdp8rUJu/ZjB6oLQG3m+jYivwEAmHmqLbC+1JBRAG2kGaEErbyIfmBbLnSaXbEwqPc12jc8qit+9Et97T/vl1l+fVSQ0mmAUVMrw2Qz3anGbRP5DQDAzGOttGdwf3+/Dw4Opj0MIFTYvkqtNH2vkaIi5Iuvg6SaXqOBbTl96qZdenjfQXUViqq3nrZIzz3uKF264e7IZMXi2rRK8fZBP5N2p5AUQQAA2pOZbXX3/vLHSREEqtDpoQRhEfLdZpMFVFAMeqXXaGBbTn/7nTt0aHxCkjTh0uyeLr3wpGO0ekWf5s3uCd14WcpPTSydWlmpk1XsErVCp7AVxgAAAJLTVfkQAEWdHkoQdp8T7pNFQrWv0d79I/rQ9Tsmi6uiQ2MTunzjbkn5ImTL2jPUF7I2qTg1sXhc1GLRvt5sx3QcAQBA88XqYJnZiZKe5e4/MrOspB53f6KxQwNaT7WhBGlP/6rm+nGOjXP/cV+j8QnXN265X//wg1/qwEjwtL7yoizumqWwMfT1ZrVl7RmB1wIAAEhCxQ6Wmb1L0nckfbHw0AmSBho5KKBVrTlribKZ7imPhYUSFNcr5YaG5TocyDCwLdeUsVZz/aBj33fNdp20doNWrts0+TOrli6Y1h0qv/84r9HgfXv12n/8D330uzv13OOO0lOPnB14D11mU8a7ekWfLjtnmfp6szKFd6OqeZ8AAACSVDHkwsy2S3qRpFvcfUXhsR3uvizpwRBygXYQtyu0ct2mVLsoYdfvzWa0/aIzYx1blM10642n9mn91tyU7pFJOv/0Rbp09dT/HIS9Ro8+flDr/m2XrtuW03Hz5+jDZz9HZy87Tt/d/nDF8IxqO39pdw/japdxAgCAqeoJuTjk7iNmVjxRj6TWiR5E22u3D5hxQwnSXq8Vdp2h4VENbMtNuYdKYxoeHdc3b3lwWjS7S9q8a8+048tfo9HxCV31s1/rih/9l0bGJvRXq07WX616pubO6pk8XpI+8O3bp12j1hCRdgiPKE9lLI2db/WxAwCAYHFCLn5iZh+SlDWzV0q6VtL3GjssdIpGT6Mb2JbTynWbtLhsqlszhK3LatYmslHXKYZHxDm2KGjfK6lycbblV7/Vqz/3M1264W6Njk9oZHxCA9se1g92/mbKcatX9Gmixmu0q6hUyjSk+fcFAICZIk6BtVbSHkk7JP25pBslfaSRg0LnaOQHzLTXQKW9DijqOkHhEeVjLddtwdl8YcVZbmhY/+vqrTr/qlv02P4Rzeru0qGxicnngt6LJIvSdigWwqZlVrthchLS/vsCAMBMUbHAcvcJd/+Su7/J3f+o8DVTBJGIRk6jS7s7EDeQoZHXP3puJvC58oKldKySAoMszjttYayC8eDouP5p03/p5Z/+sTbtelQfeOWzNbunSyNlMexB78WqpQsCxxv2eJhWKBbiFHhhRWvY442U9t8XAABmitA1WGa2QxFrrdz9+Q0ZETpKtbHn1Uh7DZSU/jqgi177vFix5tLUsYati+s/8ZjJjXy7zaZ8AF+9ok+bdv1Gf/OdO/TbJ0ckSU87arYWHjNXj+w7GDi+8vdiwx2PBB634Y5HpgVpREl7Q+i4a6vCpl2GPd5IrfD3BQCAmSAq5OI1TRsFOlbcfY1q0cjirajVAzqKY6l2jGGFYfGx8uLhb9ffoSt/+mvd9cjjU7pfv3n8kC68bod652b02IHRaecrfy+Cjol6PExYUZAbGp4W8NEIcQu8voj9upqtGX9fAADoBKFTBN39/uIfSYcknSLp+cqnCt7frAFiZmvkNLp610BVmuLVCtPQ4li9ok9b1p6hz567XJL0vmu217UmKah4ODQ2obsfeVxHzemZ1vYeHh2XuxJdj1bpvYkqCprxHsXtBqW9Tq9VxwIAQDurGNNuZu+U9DFJm5RfmvGPZvZxd/9KoweHmSOq09OoaXS1dm+K4600xSusS/GBb9+u912zvaU6WknGgYcVDy7piYNjgc/tGx7VZ89dnki3L869BHVGi5oxVTBuN6ief0eT1kpjAQCgncXZaHi3pJe4++8K3z9F0s/dPfFfa7LR8MxU/oFYqn3z2GaJs0nw4rUbKm4I1yr3Weumx0GF8SduvFuPPnEo8FxScAJe3M2VV3z8B4HTAefN6lbv3Fl6eGhYXWaBa5R6sxnNm92jh4eGNT+b0cjYuA6MTkw7Tsr/pujedWdXHE+t2vHfeQAAUJ16Nhr+naQnSr5/ovBYpQsulPQ1SU9T/pfbV7r75+INFzNJ2oEDtYgzxSusS1GqUfdZ7dqvWgIMgjpFH7x2+kbA0tSpZNWsqSu/j7Off5yuufVBjY4fvkZ3l2lkbGLytQ4LgBgaHtXQ8Ojk11Eava6IbhAAAJ0rKkXw/YUvfyXpFjP7rvKF0usl3RHj3GOSPuDut5nZkZK2mtkP3f2uegeN9tKO6WRxpnhFTUMrlfR91jLdr5YAg6DCeGzCNXdWtz706ufoCz+5J7R4iFNYBN3H+q05nfvChdq8a8/kz+8/NFaxYKpGs9YVpZ0gCQAA0hHVwTqy8M97Cn+KvhvnxO7+iKRHCl8/YWZ3S+qTRIHVYdoxnSxOumF5lyJs6lrS91lLR7CWtMaw7tzwyLje9uIT9bYXnxj4fNzCIuw+Nu/aM2U64eK1GyqeK64+OkkAAKDBQgssd78kqYuY2UmSVki6JeC5d0t6tyQtWrQoqUuihTQyir1R4k7xKt87qhn3WUtHsNopa1ffHB4UWl4w1hpVH1bAlT8eVqB3m2nCXcf3ZnVgZKxilHvcdWAAAAD1iJMi2C/pw5JOLD0+7kbDZnaEpPWSLnD3x8ufd/crJV0p5UMu4g0b7aRZ61GS3pOq2ileSdxnnHsIKzh652Yqjq/SWMYnXNfc+qA++t07A583aUrBWE86YXdIx8+UD+Uovgarli7Q+q25yMCIgW05vf+a7QqOtGj9gh4AAMwccVME10jaIR3+/BJnLywzy0j6vqSN7v6ZSseTIohazYTUtrB7eOOpfVPWJK1aumBaEIQkZbpMl7/plIr3G1bE3fbAY7rouzu1I7cv8ufvK0nfqzWdUJJOijn1L+g1CCo8n3HhBk2E/OfsinOXt82/BwAAoD3UkyK4x91vqOGCJunLku6OU1wB9UgrqTDJrlnYPVx98wOTcfDFIIieLptWYI1O+JT7DRqbpGkdp7Xr79A3fvGAfnHvXj3tqNn63FuW65P/tksP7zs4bYx9ZdMD6wkw6YuRwigFr8sKElZcSdXv9dVMSXdeAQBAuuIUWBeZ2VWS/l3S5OY37n5dhZ9bKemPJe0ws+2Fxz7k7jfWNFIgQhpJhUlu3itFb+BbKiq1sHiOsLHNyXRN+/mDYxP6xb17dcTsHv3m8UP61E27dcZznho4La98ml09ASZxUxhL7ytK2JTDbrOKP5uWpP8dAgAA6YtTYP2ppKWSMjo8RdAlRRZY7v4fyi+nABoujaTCpLtm87OZuuPIi/cbNraoYubJQ2OSDnfJ4kzLqyfAJGjNWlhYRdD7WN75Of0ZR2vLPXunHXfeaQsrjiUt7bhHHAAAiBanwHqhu7M6HC0tjaTCsK5Kbmh4SkhD3Clf9TZaMl02eb/1du7iTsurN9ijPHgjbhJjUOdn7/4RrTz5GN3868c07q5uM5132kJdunpZrLGkoR33iAMAANHiFFg/N7PnskEwWlktH/TrXfvSOzcTGg1e7KZVM+VrqELMeCVHzOmZvEZYR68acT/kJ7mhbtz3Mazzc9/vhnXPZX+YyFiaoR33iAMAANHiFFinS9puZvcqvwbLJHncmHagWar5oJ/E2pcKAZyT4k75CvuwbRbvWqUF2qqlC/T1mx+IN8CI8aQhzvs4Uzo/7bhHHAAAiBanwHpVw0cBNFkSa1+qWS8V54N/0Ift/G8z4l3DlY9NL+4bVa9VSxdU/TPNSsSbKZ2fZu0RBwAAmqdigeXu95vZ70l6lrv/i5ktkHRE44cGNE4SHZCw1LogcT74l37Yzg0N54ur2KPJyw0N1925Ktq8a09VxzczES/Nzk/aG1oDAIDW1lXpADO7SNLfSrqw8FBG0tcbOSig0cIKnmo6IHGLq2o++K9e0acta89QX282srjKdJt6s5lY5wxSvp9VkGqn20V1BZO2ekWfLjtnmfp6szLl76cZm0oXi8jc0LBch4vIgW31dwwBAMDMEGeK4BskrZB0myS5+8NmdmRDR4WW0U6boFYz1iQ6IL0hserZTJeOmTe7pteseA9RARV9JedcvHZD1V2uvt6stqw9QyvXbYq8Tu/c6gq4Zq+LSqPzQ6w6AACopGIHS9KIu7sKs5XMbF5jh4RW0U6/ra92rEl0QEbHJwIfH584XPLsPzSmS763U4vXbtDKdZsiX7vSewhTLI6K43z6/DmBx4UlvpcWkWvOWqJspjv0WnHXfhUl0RVsdTMlXAMAADROnA7Wt83si5J6zexdkt4h6UuNHRZaQTv9tj5srB/49u2SgtcA1dsB2T8SvGnvyLhPFkmlHa5Ka5KC7qFUaXHk7rrh9od1IGAM2Uz35CbBuaHhybVifWXdtOI/L7hme+D19lW56XFUV7CdOqFRZkq4BgAAaJw4IRf/YGavlPS4pCWSPubuP2z4yJC6dvptfdiYxt0bFrRQi6gCNep1LS2O7n7kcV10w0794t69WtY3Xy9f+lRdu/Whmjf6DZuSGFQ0RBVKYYl4kpoWftFoxKoDAIBKKhZYhSmBm9z9h2a2RNISM8u4e327oqJpau0etNNv66M21m1U1y1sDVYlYYXU/JDz9WYz2rL2DO0bHtXFN+zU1/7zPs3PZnTZOcv05v6F6u4ynXTsvMn3uBgqEfd+4xYNcVICg7qCK9dtaptOaCXEqgMAgEriTBH8qaSXmtnRkm6SNCjpXEnnN3JgSEY90dnt9Nv6oLGWakTX7TWnHFdTJHpYgRq2pmtkbFzfvvVBffKmXXrswIjOP+1EfeDMZ6t37ixJ9cejxy0aap0y2k6d0DiIVQcAAFHiFFjm7gfM7M8kfcHdP2VmwYs20HLqWUfVTr+tL47pA9++PTA+vRFdt7B9oirtXxW2gW/Ymq4DoxP6m/V3qP/Eo/W1179Izzt+/pTnK8Wjx3n/4hQNtRZK7dQJBQAAqFesAsvMXqx8x+rPCo+FR4+hpdTbPWin39YXx9msrlvYa+jKr5kKm7JY7Qa+kvTZc0/R6uV9MpueDxg2jmInK6m1T7UWSu3UCQUAAKhXnJj29yq/yfD17r7TzJ4haXNjh4WkdEJ0dqlGbEA7sC2nles2TYtaD3sNi1HqYVHpYQVR2MbB8+f06A0rTggsrqLG0W2W6Ma/QbHucQqltDYFBgAASIN5tZvdNFB/f78PDg6mPYwZpXx9jpT/UMwH3HiiXj8puFtWfG3DNvLtNtOn33zKtNf/Mz/Yrf+z6VeB4yiPWI8zxrD1aCbp3nVnh990hJkStw4AAFAvM9vq7v3lj8dJEXy2pA9KOqn0eHc/I8kBojHaaR1VK4pa37Rl7RmTxwS9tmHBG+XR8b95/KAuu/FuDWx/WL1zM+qSae+BkSlruaKm94W9x9XEr8fVTlNGAQAA0hBnDda1kv6vpKskhe+CipbVTh+Kk+6QxD1f2HGV1rBFvbZRwRvDo+P61E279OgTB/W5H/2XRsdd7znjmfrLl52subN6ArtfpeEkQeMtFnylWPsEAADQXHEKrDF3/0LDR4KOV2/ceK3nizqu3gS81Sv69L5rgkM3H953UJ+4cZdevvSp+uhrnquTjp13+LmIwi7ufdG9zGNaIwAAaKY4Bdb3zOx/Sbpe0qHig+6+t2GjQkeqJ1K+nvOFHffh64P31aq2CxRWpHV3ma7841P18uc8LfbPHN+brep1aqfuZSMkXbQDAABUEidF8E8krZH0c0lbC39IokDi6o2UL0/7C4tJLz9f2Pn3j4xroiwDJpvpqjogZM1ZSzSnZ+pftZ4u07pzlgUWV8WfKU/sk6QDI2Ox7wuV9wgDAABIWsUOlrsvbsZAgHqm4wV1KsI2/C0/X9h1g4yM+bTphVHTz9xdR8zu0bzZPTo4NiJJetpRs3Xhq58TWaQVn7v4hp0aGh6dfPyxA6NhPzJjo/frUW/RDgAAUK3QDpaZ/U3J128qe+4TjRwUOlOt+yxJwZ0Kl6btRRV0vrBuUZDSsIpiUZcbGpbr8PSz4j5Z9/52v97x1Vv1zq8N6uh5s3T1O0/TfevO1i0fekWsDtjqFX2aNzvOLF7CK8J02j5wAAAgfVGf3t4i6VOFry9UPk2w6FWSPtSoQaEzhYUySNLKdZsiQwrCOhKu/B5SUT9bft3euZnQTlFXScUWNv3skzft0i9/84Su+tm9mtXTpY+c/Rz9yUtOUqY7zozcqeJ0WrrNYk1b7MSwh6CofIpRAADQSFEFloV8HfQ9kIjyUIa4IQWhQRJmsQqK0uuuXLcptMCaXbKWKqz4eWTfQX3+x/fonBf0ae2rl+qpR86JuuVIcaYvTriHRrcX76lVwx4aXfSRpAgAAJrN3INWqUhmdpu7v6D866Dvk9Lf3++Dg+Rn4LCwsIouk9w1pcsVtKlvqWymO7DTU/4hP6qgMUn3rjs7cmzdJh09b5Z+9+RI3R/oywujIH2FawR1aor3GzbWvt5s4P5ZzRB0b2HvEQAAQKsxs63u3l/+eNScpVPM7HEze0LS8wtfF79f1rCRAiXCukQTrinrniTpsnOWqa83K1O+c1WuND2umDh40toNet8126eso4pqz5au3QlKB+yyfBH22ydHAtdlVWv1ir7J+5LC15RVSstrxbAHEv4AAMBMFFpguXu3ux/l7ke6e0/h6+L3mWYOEp0rThjB8Oi4Lr5hp1av6NOWtWfo3nVnayKkM1u6UW+xo1N+ZPBP5q1aukCSNDHhGptwZUoKrKcfNUdHzclorOwE9RYNxfu6b93Z+uy5yyeLyL7e7GS3p1IB1YphD61Y9AEAANQrXkQZkJI1Zy3RBddsr3jc0PCoBrblJqeWVbtRb1ybd+3Rnbl9+th379RtDwxpxaJenbHkqfrWrQ/q4UIXLEhSRUPYxsGVIu5bMeyhnlh+AACAVlV9rBlQhfLNfytNlSs/XpJ6s/EapqVdoqjI93qKndzQsF77T/+hB/Ye0OV/9Hz98ekn6vM/vmdyimGY+dlMVa9DtSpF3JdONSzvfqWlnlh+AACAVhUacpEGQi5mlmpDDMKOf+OpfVq/NVex61QaQFE8X1B63IqP/yByw95spltzMl2hx/zpypN0wSuePVk0VUr5yxQWZo2OH/671ogwh3aMYW/HMQMAAEjhIRcUWGiYapProo5ftXSBrr75gcguUdxEvOWX/EBDw8HFk1m+YxZWXPWY9A9vXi4p3zGrVFz1ZjMyU+D50kzwSwPFFAAAmEnCCizWYCFSPR+Kqw0xiHp88649kcVVNVPLwoorKR/9HtXdGnPp4ht26tDYRKx1XFHHdVKYQ6vuwwUAAJA01mAhVGnaXi2R49Um10U9HlWMVLueKCjCvRpDw6OxQzKGR8dDr9dJYQ5EsgMAgE5BgYVQ1X4oLg+oWLV0QVUhBlGhB2HFSHGaXWlxVSlYY7zJ02LH3Ts+zIFIdgAA0CkosBCqmg/FQd2u9VtzeuOpfbGT68KS7iRp/6Gxacdb4TqlRVTQOC64ZrsWX7hBHxnIT0nra1DnKKxTVbyPVkrwa7ZW3IcLAACgEViDhVDV7FMU1u3avGtPVUEO5ftXyXVKAAAaZklEQVQ8BSULFhX7UMWpi4P37w0NwnCXvn7zA7p3z5NatXSBvn7zA7HHFEdY2mGxUxW2f1WnaMV9uAAAABqBDhZCVbNPUVi3Kzc0XNfeT3E3BR4eHdfXK6QMStKWe/JFWByzuqPXanWbTelIXbp6Wcd3qsK04j5cAAAAjUAHC6GKH37jpAiGdbskTQnIGLx/rzbv2hN6vvLUwkox6LUIK8Ks8FxfYVxSPjEwLHVw3F2lJRgx5NE6vYsHAAA6A/tgIRFRU/lKFYuYotINd4POUX58I73t9EW6dHV+zVfc+ynKdJvk0uhEYzcTBgAAQGsI2weLKYJIRPkUsDDlxVJpKmHQdECXIs+XpPVbc5PTGONOTSwaHfcpxZXU3BjySsmJAAAAaA4KLCRm9Yo+bVl7hu5dd3ZVSX3F9Vth67hc9e9dFUdpQZRUfHgzYsjr3a8MAAAAyaHAQt2CuidBARlhJVKXmRav3RA5FbDS3lWZLtPsnvr/dS7Gvs/PZgKf7+vNVlU8NiOGnE18AQAAWgcFFuoS1j2RNC017vzTF00ruqR88VTvOqsj5vRoTgIFlpS/h/0jY8p0TS0JiwmKQcVjkEox5ElN62MTXwAAgNZBiiDqEtU92bL2jGkBD/0nHqNLvrdTjx0ITuarVdzzxQ3NGB13HT03o7mzekJTAS+4Znvoz/dVSBEsD9EoLUyrDcWoZr8yAAAANBYF1gzXyOjwgW250Bj18u5JcRy5oeGmhVYEOf/0RdM2Aw4zdGBU2z525uT3xY5T8bU8em4msLDr681W3Fw5qjANen+i3kc28QUAAGgdFFgzWD1dkkqFWfHcYVzSSWs3qDeb0WtOOW5KUZPWxgB9vVldunqZ+k88Zsq9HRgZCyyUSjtAQa9lpsuU6TaNjk+NZo9T2FQzra/S+1jNfmUAAABoLPbBmsFWrtsUuVFvt5nOO23h5N5PRUF7QJXv6VTp3K0mak+qeu63N5vRvNnh0wjDhJ0vqPtVzbEAAABoDvbB6kCVQg7G3fX1mx/QRwamdqLipNK1U4BCt1nkhr/le3j19WanHR92v0PDta0lCwrKCOt+EWIBAADQPiiwZrC4IQdfv/mBKSl2cT7Qpxmg0Nc7p6rjP/3mU+qeLhd2vybVtP9UnKKu0rUJsQAAAGg9DSuwzOwrZvaomd3ZqGsgWtw4cWlqcRDnA301+1zVq5iWPrunSxe+eqnWnLVUme54V8tmumKtN6u0UW/Y/ZZPsK1m/6nSjZmDEhejrk2IBQAAQGtqZMjFVyX9k6SvNfAaiFAeflBptV2xOIiTSrd6RZ8G79+rb97yoMbd1W2m059xtH5x32NTQh+kQoHk0kSN9zHh0jtWnqSPvua5MjOtXLdp2jXCzCkrTAa25XTxDTsnp/YdPTe/oXClRL+gIIm4CYr1IsQCAACgfTSswHL3n5rZSY06P+IpTZn7yMAOff3mByKPf3hoOPIDfXncerHMGXfXz+/Zq5ecfIy2P7hP+0fyBYsp33kaHq21vMr75i8e1PNP6NXqFX1VFTBDJemAA9tyWnPt7RqdOFycRe2fVX6d0tdSCg+faMTUvfJrAwAAoDWlHtNuZu+W9G5JWrRoUcqjmdkuXb1M9+55Ulvu2Rt6TLE4CPpAX562V95Dcklb7tk7ZfqeS3UXV9LUjlJU96hcabFz+cbdU4qran42CPtPAQAAoFzqIRfufqW797t7/4IFC9Iezow2sC2n2x7YF/p8peIgKF0wSNzpe9XKDQ1r5bpNWrV0Qaz1XuX3U03nK5vp1qqlC7Ry3SYtXrthSghIUTVBFQAAAOgMqXew0DxRBdLRczO66LXPiywOWiEWPDc0rPVbc3rJycfo5/fsndJFy3SZjpjTo6EDo4HrlKI6X+X7Wa1aumDK5shhmzQzdQ8AAAClKLA6SFSBdDBiGl9x3VWrbEk9PDqu+343rM+eu7yq4Ic1Zy2ZtgZLkjLdpotf97xp66sqBV8AAAAA5RpWYJnZNyW9TNKxZvaQpIvc/cuNuh4qm5/NhG6MG1Y8lK+7aqagGPSiYhhHNcVO8djyFMGgzh2b+wIAAKAWjUwRPK9R58Zhxe5SpS7OwLac9o+MRZ6rWDyUnrPLTOPe/N6VmfTZNy+fTCwsNz+bqem8cYuysOmEbO4LAACAKKmHXKB2cTbILbp84+6K4RPH92annTON4kqS3KXB+/dqzVlLlOmaHmmxf2Rs2n0ObMtFhlJUg819AQAAUAsKrDYWFFpRnOonTS04KsWam6RVSxfETgpshuKeXUfMmd5oHR33yfuUqis24yAhEAAAALUg5KKNRa0TqnbtlEtTUvNaxcU37NS+kHVjpfcfVWzWWhSREAgAAIBq0cFqY2HrgY7vzdbUiWq14kqShoZH1Ts3eL1V6f0TSgEAAIBWQIHVxqLWCVWaEthO9h2Y3sHKdNuU9VBRxSYAAADQLBRYbSxsnZCUX1M1UwTt0DVvVs+U6XuEUgAAAKAVsAarjYVFtK9ct6llNgVulPJ1WcViq5qNhwEAAICkUWC1qfIQi2Jq3uD9e2fU9MAwQVP/CKUAAABA2iiw2kR5t2r/obHA1LyrC9HmM1m1U//ibsYMAAAA1IsCqw0EdavCtNPUQFO88XZ3mY6c3aN9w6NVF0hhnT5JFFkAAABIHAVWG2ilzX+T5JKOnpvRYwEpgaWOnN2j7RedWdM1GrE/FgAAABCGFMEWNrAtp+WX/KCt11R1RcQZHj03o7mz8jV+t4UfODQ8qoFtuZquz/5YAAAAaCYKrBY1sC2nNdferqHh6O5Oq3vraYuU6Z5ePHWZ9OTBscnicdyjJwteeN2Omoos9scCAABAMzFFsIWUhjF0mVUsOtrB5l17NDo+/T5c0uhE/PurdVrfmrOWTFmDJbE/FgAAABqHAqtFlIcxzITiSgqfilfL7dUyrY/9sQAAANBMFFgpKY8OPzAyPXZ9Jji+Nxu4hqy7hg5drdP62B8LAAAAzcIarBQUu1W5oWG58tHhlZL02tWBkTFlypIusplunXfaQmUz3VMez3TbtGNLf4ZpfQAAAGh1dLCaoFO6VUEeOzCqTLepN5uZto9V/4nHTJu6J+Wn8+WGhie7XH1M6wMAAECbMG+htT79/f0+ODiY9jASVb62qlP19Wa1Ze0ZaQ8DAAAASISZbXX3/vLHmSLYYDN1k+AgEVtese8UAAAAOgIFVoN1UmFx/umL1Me+UwAAAOhgFFgN1kmFxeZde7Rq6YJp4RUEVAAAAKBTUGA12JqzlkROnZtJckPDWr81pzee2qe+3qxM+bVXl52zjIAKAAAAdARSBBts9Yo+XXDN9rSH0TTDo+PavGsPgRYAAADoSHSwmiBsXdJM1UnrzgAAAIBSFFhNsGrpgrSHkLi+3qyOnpsJfK6T1p0BAAAApZgimKDyDYWLm+Nu3rUn7aElqrinVdAeXwRaAAAAoJNRYCWkvNjIDQ3rwut2aPD+vcrNoClzpQVUMbgiqKgEAAAAOpG5e9pjmNTf3++Dg4NpD6MmK9dtmlGFVJCj52Z00WufRwEFAACAjmdmW929v/xxOlgJmYnBDsV4eTpTAAAAQDwUWAk5vjc7ozpY2Uw3+1cBAAAAVSJFMCHtvKFwX29Wbzt9EZsDAwAAAHWig5WQdtxQmC4VAAAAkCwKrDoMbMvp4ht2amh4NO2hxNZl0oTnu1SsqwIAAACSRYFVo4FtOa259naNTrROCmO5K85dToQ6AAAA0EQUWDW6fOPuli6u+nqzWr2ij4IKAAAAaCJCLmrUyomBpZsBAwAAAGgeCqwadVtrZgZmM10EVwAAAAApYYpgTAPbclPWM417a04PPGbe7LSHAAAAAHQsCqwYBrbldOF1OzQ8Oi6ptacH5oaGdeF1OySJLhYAAADQZEwRjOHyjbsni6tW0xUwU3F4dFyXb9zd/MEAAAAAHY4OVgyt2LG6b93ZkqTFazcEPv9wC44ZAAAAmOnoYFUwsC2nVouz6OvNTn59fMnXpcIeBwAAANA4FFgVXL5xt1opzqI8gn3NWUuUzXRHHgMAAACgOZgiGGFgWy716YFdJh03PzuZXrjmrCVTwiuKX5cmHJYfAwAAAKA5KLBCFJMD0/bW0xbp0tXLIo9ZvaKPggoAAABoAUwRDNEqyYGViisAAAAArYMCK0SjU/i6rXJ0Rh9BFQAAAEBbocAK0egUvvNOWzgtnKIUQRUAAABA+6HACvGuly5u2LnnZrp06epluuycZerrzcokHT03o95sRqZ85+qyc5axrgoAAABoMw0NuTCzV0n6nKRuSVe5+7pGXi9Jrz3leF2+cbf2jyS7DivTZfrEOc+XRDgFAAAAMNM0rINlZt2S/lnSqyU9V9J5ZvbcRl0vaU85Yra2X3Smrjh3eaLnPfdFCymqAAAAgBmqkR2sF0n6lbv/WpLM7FuSXi/prgZeMzED23KTe0slafOuPYmeDwAAAEDraOQarD5JD5Z8/1DhsSnM7N1mNmhmg3v2tEbxUdwDKzc0LE/43I1OJwQAAACQntRDLtz9Snfvd/f+BQsWpD0cSY3dA6vLTIvXbtDKdZs0sC3XkGsAAAAASEcjpwjmJC0s+f6EwmMtr5FdpnHP98RyQ8O68LodksSaLAAAAGCGaGQH61ZJzzKzxWY2S9JbJN3QwOslJqk9sLqUj183BW8sPDw6rss37k7kWgAAAADS17ACy93HJP21pI2S7pb0bXff2ajrJWnNWUsiNwGOa0LS3Fk9unfd2Zrw4NVcrMkCAAAAZo6G7oPl7jdKurGR12iE4pS9C67ZXve5igXU8b1Z5QKKqaS6ZQAAAADSl3rIRatavaJPfRWKn+mT/qbrnZvRynWblBsannZ8NtOtNWctqXmMAAAAAFoLBVaESsWPS+rrzcqUX2uV6ZpaQmW6TU8eHJvsXLkOF2V9vVldds4yAi4AAACAGYQCK0Kl4qc3m5n8eu6sHp37ooWTBVdfb1bzZvVodGLq2qtiUbZl7RkUVwAAAMAM09A1WDNBt9lktHq5/SNjGhoelZSPXV+/NTelK7V47YbAnyPYAgAAAJiZ6GBVcN5pCwMfn93TpdHxqYVXeex6WIAFwRYAAADAzESBVcGlq5fpbacvmtzHqttMbzt9kUbGJgKPL+1OBcW9E2wBAAAAzFxMEYzh0tXLdOnqZVMe27xrT8XY9eJUwcs37tbDQ8M6vjerNWctYe0VAAAAMENRYIUY2JaLLIzWnLVEF163Q8Oj45OPBXWnVq/oo6ACAAAAOgQFVoCBbbkpxVNuaFgXXrdD0uGuFN0pAAAAAOUosAJcvnH3lM6UdDjAorSAojsFAAAAoBQhFwHCYtSJVwcAAAAQhQIrAPHqAAAAAGpBgRWAeHUAAAAAtWANVgACLAAAAADUwtw97TFM6u/v98HBwbSHMUWluHYAAAAAncfMtrp7f/njdLAixIlrBwAAAIAi1mBFiIprBwAAAIByFFgRiGsHAAAAUA0KrAjEtQMAAACoBgVWBOLaAQAAAFSDkIsIxLUDAAAAqAYFVgWrV/RRUAEAAACIhSmCAAAAAJAQCiwAAAAASAgFFgAAAAAkhAILAAAAABJCgQUAAAAACaHAAgAAAICEUGABAAAAQEIosAAAAAAgIRRYAAAAAJAQCiwAAAAASAgFFgAAAAAkhAILAAAAABJCgQUAAAAACaHAAgAAAICEUGABAAAAQEIosAAAAAAgIRRYAAAAAJAQCiwAAAAASIi5e9pjmGRmeyTdn9Llj5X025Sujel4P1oH70Vr4f1oHbwXrYX3o3XwXrQW3o/GOdHdF5Q/2FIFVprMbNDd+9MeB/J4P1oH70Vr4f1oHbwXrYX3o3XwXrQW3o/mY4ogAAAAACSEAgsAAAAAEkKBddiVaQ8AU/B+tA7ei9bC+9E6eC9aC+9H6+C9aC28H03GGiwAAAAASAgdLAAAAABICAUWAAAAACSEAkuSmb3KzHab2a/MbG3a4+lkZvYVM3vUzO5MeyydzswWmtlmM7vLzHaa2XvTHlOnMrM5ZvYLM7u98F5ckvaYIJlZt5ltM7Pvpz2WTmZm95nZDjPbbmaDaY+n05lZr5l9x8x2mdndZvbitMfUicxsSeHvRPHP42Z2Qdrj6hQdvwbLzLol/VLSKyU9JOlWSee5+12pDqxDmdnvS3pS0tfc/X+kPZ5OZmbHSTrO3W8zsyMlbZW0mr8bzWdmJmmeuz9pZhlJ/yHpve5+c8pD62hm9n5J/ZKOcvfXpD2eTmVm90nqd3c2Um0BZvavkn7m7leZ2SxJc919KO1xdbLCZ92cpNPc/f60x9MJ6GBJL5L0K3f/tbuPSPqWpNenPKaO5e4/lbQ37XFAcvdH3P22wtdPSLpbUl+6o+pMnvdk4dtM4U9n/3YsZWZ2gqSzJV2V9liAVmFm8yX9vqQvS5K7j1BctYSXS7qH4qp5KLDyHxgfLPn+IfEhEpjCzE6StELSLemOpHMVpqNtl/SopB+6O+9Fuq6Q9DeSJtIeCOSSfmBmW83s3WkPpsMtlrRH0r8Ups9eZWbz0h4U9BZJ30x7EJ2EAgtAJDM7QtJ6SRe4++Npj6dTufu4uy+XdIKkF5kZU2hTYmavkfSou29NeyyQJP2eu79A0qsl/VVhqjnS0SPpBZK+4O4rJO2XxNr2FBWmab5O0rVpj6WTUGDl56QuLPn+hMJjQMcrrPdZL+lqd78u7fFAKky32SzpVWmPpYOtlPS6wtqfb0k6w8y+nu6QOpe75wr/fFTS9cpP/Uc6HpL0UEmH/TvKF1xIz6sl3ebuv0l7IJ2EAisfavEsM1tcqPLfIumGlMcEpK4QrPBlSXe7+2fSHk8nM7MFZtZb+DqrfCjPrnRH1bnc/UJ3P8HdT1L+/xmb3P1tKQ+rI5nZvEIIjwpT0c6URAptStz9vyU9aGZLCg+9XBLBSOk6T0wPbLqetAeQNncfM7O/lrRRUrekr7j7zpSH1bHM7JuSXibpWDN7SNJF7v7ldEfVsVZK+mNJOwprfyTpQ+5+Y4pj6lTHSfrXQhJUl6RvuzvR4ID0NEnX538fpB5J33D3m9IdUsd7j6SrC7+0/rWkP015PB2r8EuHV0r687TH0mk6PqYdAAAAAJLCFEEAAAAASAgFFgAAAAAkhAILAAAAABJCgQUAAAAACaHAAgAAANAxzOwrZvaomcXa1sHM3mxmd5nZTjP7RsXjSREEAAAA0CnM7PclPSnpa+7+Pyoc+yxJ35Z0hrs/ZmZPLWxsHooOFgCgamZ2kpm9tY6f/1CVxy8zs+2FP3vN7N7C1z+qdQwh1znCzL5oZveY2VYz+7GZnVbH+b5aMtbbzOzFIcd93MxeUfvIAQBxuftPJe0tfczMTjazmwr/7f+ZmS0tPPUuSf/s7o8VfjayuJIosAAAtTlJUs0FlqSqCix33+Huy919uaQbJK0pfD9ZlJhZTx3jKbpK+f/pPsvdT1V+k9Rj4/5wYTPocmsK414r6YtBP+PuH3P3RItFAEBVrpT0nsJ/+z8o6fOFx58t6dlmtsXMbjazV1U6EQUWAEDSZBflgpLv/97M3hty+DpJLy10Zt5nZt1mdrmZ3Wpmd5jZnxfOcZyZ/bRw3J1m9lIzWycpW3js6jrH/GMzu8LMBiW9t9Ax+qOS558s+XpNyfguCTjXyZJOk/QRd5+QJHe/1903FJ4fKPxmc6eZvbv0Gmb2aTO7XVJgh6rgp5KeWfiZ+8zsk2Z2m6Q3lY7bzF5oZj83s9vN7BdmdmTY6wsAqJ+ZHSHpJZKuNbPtyv8y7LjC0z2SniXpZZLOk/QlM+uNOl8Sv+0DAMwMX5F0naQrzKxL0lskvSjk2LWSPujur5GkQsGxz91faGazJW0xsx9IOkfSRnf/+0J3Z667/8zM/rrQ1UnCLHfvL4zjq0EHmNmZyv8P8kWSTNINZvb7hWkiRc+TtN3dx0Ou8w5332tmWUm3mtl6d/+dpHmSbnH3D1QY52sl7Sj5/nfu/oLC+F5V+OcsSddIOtfdbzWzoyQNS/ozBby+7n5vhWsCACrrkjQU8v+lh5T/b/yopHvN7JfK///k1qiTAQAgd79P0u/MbIWkMyVtKxQQcZwp6X8WfvN3i6Sn6PD/gP7UzC6WtMzdn0h84PmCJM74zpS0TdJtkpYWxleN/13oUt0saWHJz49LWh/xc5cXXpd3K18oRY17iaRH3P1WSXL3x919TOGvLwCgTu7+uPLF05skyfJOKTw9oHz3SmZ2rPJTBn8ddT46WACAUldJerukpyvf0YrLlJ+7vnHaE/m0prMlfdXMPuPuXws9idkbJF1U+Pad7j4Y49r7S74eU+GXh4Uu3KyS8V3m7tPWQJXYKemUwpqoKV0sM3uZpFdIerG7HzCzH0uaU3j6YETXS8qvwfpOhXFXEvr6AgCqY2bfVL5oOtbMHlL+/zvnS/qCmX1EUkbStyTdLmmjpDPN7C7lf6G2ptIvHymwAAClrpf0ceX/5xIVYvGEpCNLvt8o6S/NbJO7j5rZsyXllA+IeMjdv1SY2vYCSV+TNGpmmcKUi0nufn1hDLW6T9Kpykfqvq5wH8Xx/Z2ZXe3uT5pZn6TR0jQod7+nsJbrEjP7qLu7mZ2k/NTBHkmPFYqrpZJOr2OMUXZLOs7MXliYInik8lMEA19fd6+mSAMASHL380KemhZg4fk9rd5f+BMLBRYAYJK7j5jZZuXnokd1Ze6QNF6YMvdVSZ9TPlnwNjMzSXskrVb+N4RrzGxU+T1H/mfh56+UdIeZ3ebu5yd4C1+S9N3CuG5SoUvk7j8ws+dI+s/88PSkpLdJKo/bfaekT0v6lZkNS/qtpDWF+/0LM7tb+SLo5gTHPKnw+p8r6R8La72Gle+cXaXg1xcA0GLYaBgAMKkwre42SW9y9/9KezwAALQbQi4AAJIkM3uupF9J+neKKwAAakMHCwAQysyWSfp/ZQ8fcvfT0hgPAACtjgILAAAAABLCFEEAAAAASAgFFgAAAAAkhAILAAAAABJCgQUAAAAACfn/ZiWLvcZIWQMAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle"
      ],
      "metadata": {
        "id": "dAkDQpe6Xd0K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(pipe,open(\"RandomForestRegressionModel.pkl\",'wb'))"
      ],
      "metadata": {
        "id": "haHfEUX1Xg4B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_AdRcrQYT5J",
        "outputId": "420322f2-3ec3-47fb-f590-271a993598af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6016 entries, 0 to 6015\n",
            "Data columns (total 10 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Manufacturer       6016 non-null   object \n",
            " 1   Model              6016 non-null   object \n",
            " 2   Year               6016 non-null   int64  \n",
            " 3   Kilometers_Driven  6016 non-null   int64  \n",
            " 4   Fuel_Type          6016 non-null   object \n",
            " 5   Owner_Type         6016 non-null   object \n",
            " 6   Mileage            6016 non-null   float64\n",
            " 7   Engine             6016 non-null   float64\n",
            " 8   Power              6016 non-null   float64\n",
            " 9   Price              6016 non-null   float64\n",
            "dtypes: float64(4), int64(2), object(4)\n",
            "memory usage: 470.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe.predict(pd.DataFrame([['Maruti','Maruti Wagon R',2019,100,'Petrol',26.60,998,58.16,'First']],columns=['Manufacturer','Model','Year','Kilometers_Driven','Fuel_Type', 'Mileage', 'Engine', 'Power','Owner_Type']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aNs8eP8X6Mi",
        "outputId": "64ca91b0-4ea8-45c6-c5d7-26ae350d2fa1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([389440.])"
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "jIkUlsopaps9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}