IT2K21_11_Data_Science.ipynb

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      "authorship_tag": "ABX9TyNvgekY/d3wm3+A4OLyKp6P",
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/17arindam/data-science-project/blob/main/IT2K21_11_Data_Science.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 1.1"
      ],
      "metadata": {
        "id": "vS4YTrIxs53X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(\"Hello, World!\")  # This is comment\n"
      ],
      "metadata": {
        "id": "Y5-DdDDrBqcq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 1.2"
      ],
      "metadata": {
        "id": "nO8EG3x8B3tP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "result = !ls -l\n",
        "\n",
        "\n",
        "print(result)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_c7YFmcxB6B3",
        "outputId": "c335cfca-0cff-4683-c6bc-898aada25d85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['total 48', '-rw-r--r-- 1 root root 41858 Dec  5 20:12 EV_cars.csv', 'drwxr-xr-x 1 root root  4096 Dec  4 14:27 sample_data']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 1.3"
      ],
      "metadata": {
        "id": "YYEJlIrZCMZm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "\n",
        "x = 4.7\n",
        "\n",
        "\n",
        "ceiling_result = math.ceil(x)\n",
        "print(f\"Ceiling of {x} is: {ceiling_result}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eOrg2GyCPn3",
        "outputId": "6bfefc9a-a101-425f-b5fc-84ee69b20d54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ceiling of 4.7 is: 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 1.4"
      ],
      "metadata": {
        "id": "DOyh49VFCsOd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "file_path = '/content/EV_cars.csv'\n",
        "\n",
        "\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "\n",
        "print(df.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhBlbE4jCuUl",
        "outputId": "ba1bd28a-304e-4018-c38d-3b08e54bb175"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Battery                             Car_name  \\\n",
            "0     75.0  Tesla Model Y Long Range Dual Motor   \n",
            "1     57.5                        Tesla Model 3   \n",
            "2     60.5                           BYD ATTO 3   \n",
            "3     61.7               MG MG4 Electric 64 kWh   \n",
            "4     75.0  Tesla Model 3 Long Range Dual Motor   \n",
            "\n",
            "                                       Car_name_link  Efficiency  Fast_charge  \\\n",
            "0  https://ev-database.org/car/1619/Tesla-Model-Y...         172        670.0   \n",
            "1     https://ev-database.org/car/1991/Tesla-Model-3         137        700.0   \n",
            "2        https://ev-database.org/car/1782/BYD-ATTO-3         183        370.0   \n",
            "3  https://ev-database.org/car/1708/MG-MG4-Electr...         171        630.0   \n",
            "4  https://ev-database.org/car/1992/Tesla-Model-3...         149        780.0   \n",
            "\n",
            "   Price.DE.  Range  Top_speed  acceleration..0.100.  \n",
            "0    59017.0    435        217                   5.0  \n",
            "1    46220.0    420        201                   6.1  \n",
            "2    44625.0    330        160                   7.3  \n",
            "3    39990.0    360        160                   7.9  \n",
            "4    55220.0    505        201                   4.4  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 1.5"
      ],
      "metadata": {
        "id": "Fb61NFLfDDhL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "output_file_path = 'your_output.csv'\n",
        "\n",
        "\n",
        "df.to_csv(output_file_path, index=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBRCn6TjDGK1",
        "outputId": "177505e4-0214-4424-eb06-f883f6960394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 1.6"
      ],
      "metadata": {
        "id": "CX8njEmkDag-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "file_path = '/content/EV_cars.csv'\n",
        "\n",
        "\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LWCaYMpDfSd",
        "outputId": "d76c0301-d346-470b-c151-8a44eec16bd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Battery                             Car_name  \\\n",
            "0       75.0  Tesla Model Y Long Range Dual Motor   \n",
            "1       57.5                        Tesla Model 3   \n",
            "2       60.5                           BYD ATTO 3   \n",
            "3       61.7               MG MG4 Electric 64 kWh   \n",
            "4       75.0  Tesla Model 3 Long Range Dual Motor   \n",
            "..       ...                                  ...   \n",
            "355     46.3      Citroen e-Jumpy Combi XL 50 kWh   \n",
            "356     46.3              Fiat E-Ulysse L2 50 kWh   \n",
            "357     46.3         Opel Vivaro-e Combi L 50 kWh   \n",
            "358     68.0          Opel Zafira-e Life M 75 kWh   \n",
            "359     46.3              Fiat E-Ulysse L3 50 kWh   \n",
            "\n",
            "                                         Car_name_link  Efficiency  \\\n",
            "0    https://ev-database.org/car/1619/Tesla-Model-Y...         172   \n",
            "1       https://ev-database.org/car/1991/Tesla-Model-3         137   \n",
            "2          https://ev-database.org/car/1782/BYD-ATTO-3         183   \n",
            "3    https://ev-database.org/car/1708/MG-MG4-Electr...         171   \n",
            "4    https://ev-database.org/car/1992/Tesla-Model-3...         149   \n",
            "..                                                 ...         ...   \n",
            "355  https://ev-database.org/car/1596/Citroen-e-Jum...         257   \n",
            "356  https://ev-database.org/car/1721/Fiat-E-Ulysse...         250   \n",
            "357  https://ev-database.org/car/1601/Opel-Vivaro-e...         257   \n",
            "358  https://ev-database.org/car/1348/Opel-Zafira-e...         257   \n",
            "359  https://ev-database.org/car/1723/Fiat-E-Ulysse...         257   \n",
            "\n",
            "     Fast_charge  Price.DE.  Range  Top_speed  acceleration..0.100.  \n",
            "0          670.0    59017.0    435        217                   5.0  \n",
            "1          700.0    46220.0    420        201                   6.1  \n",
            "2          370.0    44625.0    330        160                   7.3  \n",
            "3          630.0    39990.0    360        160                   7.9  \n",
            "4          780.0    55220.0    505        201                   4.4  \n",
            "..           ...        ...    ...        ...                   ...  \n",
            "355        290.0    52730.0    180        130                  12.1  \n",
            "356        290.0    55990.0    185        130                  12.1  \n",
            "357        290.0    51825.0    180        130                  12.1  \n",
            "358        290.0    69250.0    265        130                  13.3  \n",
            "359        290.0    56990.0    180        130                  12.1  \n",
            "\n",
            "[360 rows x 9 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 2.1"
      ],
      "metadata": {
        "id": "4wEzP_t3D9m4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "file_path = '/content/your_output.csv'\n",
        "\n",
        "\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "\n",
        "print(\"Original DataFrame:\")\n",
        "print(df)\n",
        "\n",
        "\n",
        "df = df.set_index('Car_name', drop=True)\n",
        "\n",
        "\n",
        "print(\"\\nDataFrame with New Index:\")\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8MOFYGHD_bO",
        "outputId": "ec5412fa-882c-4cc5-8249-5e960b388982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original DataFrame:\n",
            "     Battery                             Car_name  \\\n",
            "0       75.0  Tesla Model Y Long Range Dual Motor   \n",
            "1       57.5                        Tesla Model 3   \n",
            "2       60.5                           BYD ATTO 3   \n",
            "3       61.7               MG MG4 Electric 64 kWh   \n",
            "4       75.0  Tesla Model 3 Long Range Dual Motor   \n",
            "..       ...                                  ...   \n",
            "355     46.3      Citroen e-Jumpy Combi XL 50 kWh   \n",
            "356     46.3              Fiat E-Ulysse L2 50 kWh   \n",
            "357     46.3         Opel Vivaro-e Combi L 50 kWh   \n",
            "358     68.0          Opel Zafira-e Life M 75 kWh   \n",
            "359     46.3              Fiat E-Ulysse L3 50 kWh   \n",
            "\n",
            "                                         Car_name_link  Efficiency  \\\n",
            "0    https://ev-database.org/car/1619/Tesla-Model-Y...         172   \n",
            "1       https://ev-database.org/car/1991/Tesla-Model-3         137   \n",
            "2          https://ev-database.org/car/1782/BYD-ATTO-3         183   \n",
            "3    https://ev-database.org/car/1708/MG-MG4-Electr...         171   \n",
            "4    https://ev-database.org/car/1992/Tesla-Model-3...         149   \n",
            "..                                                 ...         ...   \n",
            "355  https://ev-database.org/car/1596/Citroen-e-Jum...         257   \n",
            "356  https://ev-database.org/car/1721/Fiat-E-Ulysse...         250   \n",
            "357  https://ev-database.org/car/1601/Opel-Vivaro-e...         257   \n",
            "358  https://ev-database.org/car/1348/Opel-Zafira-e...         257   \n",
            "359  https://ev-database.org/car/1723/Fiat-E-Ulysse...         257   \n",
            "\n",
            "     Fast_charge  Price.DE.  Range  Top_speed  acceleration..0.100.  \n",
            "0          670.0    59017.0    435        217                   5.0  \n",
            "1          700.0    46220.0    420        201                   6.1  \n",
            "2          370.0    44625.0    330        160                   7.3  \n",
            "3          630.0    39990.0    360        160                   7.9  \n",
            "4          780.0    55220.0    505        201                   4.4  \n",
            "..           ...        ...    ...        ...                   ...  \n",
            "355        290.0    52730.0    180        130                  12.1  \n",
            "356        290.0    55990.0    185        130                  12.1  \n",
            "357        290.0    51825.0    180        130                  12.1  \n",
            "358        290.0    69250.0    265        130                  13.3  \n",
            "359        290.0    56990.0    180        130                  12.1  \n",
            "\n",
            "[360 rows x 9 columns]\n",
            "\n",
            "DataFrame with New Index:\n",
            "                                     Battery  \\\n",
            "Car_name                                       \n",
            "Tesla Model Y Long Range Dual Motor     75.0   \n",
            "Tesla Model 3                           57.5   \n",
            "BYD ATTO 3                              60.5   \n",
            "MG MG4 Electric 64 kWh                  61.7   \n",
            "Tesla Model 3 Long Range Dual Motor     75.0   \n",
            "...                                      ...   \n",
            "Citroen e-Jumpy Combi XL 50 kWh         46.3   \n",
            "Fiat E-Ulysse L2 50 kWh                 46.3   \n",
            "Opel Vivaro-e Combi L 50 kWh            46.3   \n",
            "Opel Zafira-e Life M 75 kWh             68.0   \n",
            "Fiat E-Ulysse L3 50 kWh                 46.3   \n",
            "\n",
            "                                                                         Car_name_link  \\\n",
            "Car_name                                                                                 \n",
            "Tesla Model Y Long Range Dual Motor  https://ev-database.org/car/1619/Tesla-Model-Y...   \n",
            "Tesla Model 3                           https://ev-database.org/car/1991/Tesla-Model-3   \n",
            "BYD ATTO 3                                 https://ev-database.org/car/1782/BYD-ATTO-3   \n",
            "MG MG4 Electric 64 kWh               https://ev-database.org/car/1708/MG-MG4-Electr...   \n",
            "Tesla Model 3 Long Range Dual Motor  https://ev-database.org/car/1992/Tesla-Model-3...   \n",
            "...                                                                                ...   \n",
            "Citroen e-Jumpy Combi XL 50 kWh      https://ev-database.org/car/1596/Citroen-e-Jum...   \n",
            "Fiat E-Ulysse L2 50 kWh              https://ev-database.org/car/1721/Fiat-E-Ulysse...   \n",
            "Opel Vivaro-e Combi L 50 kWh         https://ev-database.org/car/1601/Opel-Vivaro-e...   \n",
            "Opel Zafira-e Life M 75 kWh          https://ev-database.org/car/1348/Opel-Zafira-e...   \n",
            "Fiat E-Ulysse L3 50 kWh              https://ev-database.org/car/1723/Fiat-E-Ulysse...   \n",
            "\n",
            "                                     Efficiency  Fast_charge  Price.DE.  \\\n",
            "Car_name                                                                  \n",
            "Tesla Model Y Long Range Dual Motor         172        670.0    59017.0   \n",
            "Tesla Model 3                               137        700.0    46220.0   \n",
            "BYD ATTO 3                                  183        370.0    44625.0   \n",
            "MG MG4 Electric 64 kWh                      171        630.0    39990.0   \n",
            "Tesla Model 3 Long Range Dual Motor         149        780.0    55220.0   \n",
            "...                                         ...          ...        ...   \n",
            "Citroen e-Jumpy Combi XL 50 kWh             257        290.0    52730.0   \n",
            "Fiat E-Ulysse L2 50 kWh                     250        290.0    55990.0   \n",
            "Opel Vivaro-e Combi L 50 kWh                257        290.0    51825.0   \n",
            "Opel Zafira-e Life M 75 kWh                 257        290.0    69250.0   \n",
            "Fiat E-Ulysse L3 50 kWh                     257        290.0    56990.0   \n",
            "\n",
            "                                     Range  Top_speed  acceleration..0.100.  \n",
            "Car_name                                                                     \n",
            "Tesla Model Y Long Range Dual Motor    435        217                   5.0  \n",
            "Tesla Model 3                          420        201                   6.1  \n",
            "BYD ATTO 3                             330        160                   7.3  \n",
            "MG MG4 Electric 64 kWh                 360        160                   7.9  \n",
            "Tesla Model 3 Long Range Dual Motor    505        201                   4.4  \n",
            "...                                    ...        ...                   ...  \n",
            "Citroen e-Jumpy Combi XL 50 kWh        180        130                  12.1  \n",
            "Fiat E-Ulysse L2 50 kWh                185        130                  12.1  \n",
            "Opel Vivaro-e Combi L 50 kWh           180        130                  12.1  \n",
            "Opel Zafira-e Life M 75 kWh            265        130                  13.3  \n",
            "Fiat E-Ulysse L3 50 kWh                180        130                  12.1  \n",
            "\n",
            "[360 rows x 8 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 2.2"
      ],
      "metadata": {
        "id": "-8T9UwibFRgK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "file_path = '/content/your_output.csv'\n",
        "\n",
        "\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "print(\"Original DataFrame:\")\n",
        "print(df)\n",
        "\n",
        "\n",
        "value_mapping = {\n",
        "    '137': '140',\n",
        "\n",
        "\n",
        "}\n",
        "\n",
        "\n",
        "df.replace({'Efficiency': value_mapping}, inplace=True)\n",
        "\n",
        "\n",
        "print(\"\\nDataFrame with Corrected Values:\")\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6g652IjFUIb",
        "outputId": "762cce6b-be26-4712-8c43-1539cd496263"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original DataFrame:\n",
            "     Battery                             Car_name  \\\n",
            "0       75.0  Tesla Model Y Long Range Dual Motor   \n",
            "1       57.5                        Tesla Model 3   \n",
            "2       60.5                           BYD ATTO 3   \n",
            "3       61.7               MG MG4 Electric 64 kWh   \n",
            "4       75.0  Tesla Model 3 Long Range Dual Motor   \n",
            "..       ...                                  ...   \n",
            "355     46.3      Citroen e-Jumpy Combi XL 50 kWh   \n",
            "356     46.3              Fiat E-Ulysse L2 50 kWh   \n",
            "357     46.3         Opel Vivaro-e Combi L 50 kWh   \n",
            "358     68.0          Opel Zafira-e Life M 75 kWh   \n",
            "359     46.3              Fiat E-Ulysse L3 50 kWh   \n",
            "\n",
            "                                         Car_name_link  Efficiency  \\\n",
            "0    https://ev-database.org/car/1619/Tesla-Model-Y...         172   \n",
            "1       https://ev-database.org/car/1991/Tesla-Model-3         137   \n",
            "2          https://ev-database.org/car/1782/BYD-ATTO-3         183   \n",
            "3    https://ev-database.org/car/1708/MG-MG4-Electr...         171   \n",
            "4    https://ev-database.org/car/1992/Tesla-Model-3...         149   \n",
            "..                                                 ...         ...   \n",
            "355  https://ev-database.org/car/1596/Citroen-e-Jum...         257   \n",
            "356  https://ev-database.org/car/1721/Fiat-E-Ulysse...         250   \n",
            "357  https://ev-database.org/car/1601/Opel-Vivaro-e...         257   \n",
            "358  https://ev-database.org/car/1348/Opel-Zafira-e...         257   \n",
            "359  https://ev-database.org/car/1723/Fiat-E-Ulysse...         257   \n",
            "\n",
            "     Fast_charge  Price.DE.  Range  Top_speed  acceleration..0.100.  \n",
            "0          670.0    59017.0    435        217                   5.0  \n",
            "1          700.0    46220.0    420        201                   6.1  \n",
            "2          370.0    44625.0    330        160                   7.3  \n",
            "3          630.0    39990.0    360        160                   7.9  \n",
            "4          780.0    55220.0    505        201                   4.4  \n",
            "..           ...        ...    ...        ...                   ...  \n",
            "355        290.0    52730.0    180        130                  12.1  \n",
            "356        290.0    55990.0    185        130                  12.1  \n",
            "357        290.0    51825.0    180        130                  12.1  \n",
            "358        290.0    69250.0    265        130                  13.3  \n",
            "359        290.0    56990.0    180        130                  12.1  \n",
            "\n",
            "[360 rows x 9 columns]\n",
            "\n",
            "DataFrame with Corrected Values:\n",
            "     Battery                             Car_name  \\\n",
            "0       75.0  Tesla Model Y Long Range Dual Motor   \n",
            "1       57.5                        Tesla Model 3   \n",
            "2       60.5                           BYD ATTO 3   \n",
            "3       61.7               MG MG4 Electric 64 kWh   \n",
            "4       75.0  Tesla Model 3 Long Range Dual Motor   \n",
            "..       ...                                  ...   \n",
            "355     46.3      Citroen e-Jumpy Combi XL 50 kWh   \n",
            "356     46.3              Fiat E-Ulysse L2 50 kWh   \n",
            "357     46.3         Opel Vivaro-e Combi L 50 kWh   \n",
            "358     68.0          Opel Zafira-e Life M 75 kWh   \n",
            "359     46.3              Fiat E-Ulysse L3 50 kWh   \n",
            "\n",
            "                                         Car_name_link  Efficiency  \\\n",
            "0    https://ev-database.org/car/1619/Tesla-Model-Y...         172   \n",
            "1       https://ev-database.org/car/1991/Tesla-Model-3         137   \n",
            "2          https://ev-database.org/car/1782/BYD-ATTO-3         183   \n",
            "3    https://ev-database.org/car/1708/MG-MG4-Electr...         171   \n",
            "4    https://ev-database.org/car/1992/Tesla-Model-3...         149   \n",
            "..                                                 ...         ...   \n",
            "355  https://ev-database.org/car/1596/Citroen-e-Jum...         257   \n",
            "356  https://ev-database.org/car/1721/Fiat-E-Ulysse...         250   \n",
            "357  https://ev-database.org/car/1601/Opel-Vivaro-e...         257   \n",
            "358  https://ev-database.org/car/1348/Opel-Zafira-e...         257   \n",
            "359  https://ev-database.org/car/1723/Fiat-E-Ulysse...         257   \n",
            "\n",
            "     Fast_charge  Price.DE.  Range  Top_speed  acceleration..0.100.  \n",
            "0          670.0    59017.0    435        217                   5.0  \n",
            "1          700.0    46220.0    420        201                   6.1  \n",
            "2          370.0    44625.0    330        160                   7.3  \n",
            "3          630.0    39990.0    360        160                   7.9  \n",
            "4          780.0    55220.0    505        201                   4.4  \n",
            "..           ...        ...    ...        ...                   ...  \n",
            "355        290.0    52730.0    180        130                  12.1  \n",
            "356        290.0    55990.0    185        130                  12.1  \n",
            "357        290.0    51825.0    180        130                  12.1  \n",
            "358        290.0    69250.0    265        130                  13.3  \n",
            "359        290.0    56990.0    180        130                  12.1  \n",
            "\n",
            "[360 rows x 9 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 2.3"
      ],
      "metadata": {
        "id": "rAK0aU5EGRaR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "file_path = '/content/your_output.csv'\n",
        "\n",
        "\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "print(\"Original DataFrame:\")\n",
        "print(df)\n",
        "\n",
        "category_mapping = {\n",
        "    'Tesla Model 3': 'Tata Curvv',\n",
        "\n",
        "\n",
        "}\n",
        "\n",
        "\n",
        "df.replace({'Car_name': category_mapping}, inplace=True)\n",
        "\n",
        "\n",
        "print(\"\\nDataFrame with Reexpressed Values:\")\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Szapc3R0GToA",
        "outputId": "74d4298a-2f40-4edb-81b3-3bb6b25026fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original DataFrame:\n",
            "     Battery                             Car_name  \\\n",
            "0       75.0  Tesla Model Y Long Range Dual Motor   \n",
            "1       57.5                        Tesla Model 3   \n",
            "2       60.5                           BYD ATTO 3   \n",
            "3       61.7               MG MG4 Electric 64 kWh   \n",
            "4       75.0  Tesla Model 3 Long Range Dual Motor   \n",
            "..       ...                                  ...   \n",
            "355     46.3      Citroen e-Jumpy Combi XL 50 kWh   \n",
            "356     46.3              Fiat E-Ulysse L2 50 kWh   \n",
            "357     46.3         Opel Vivaro-e Combi L 50 kWh   \n",
            "358     68.0          Opel Zafira-e Life M 75 kWh   \n",
            "359     46.3              Fiat E-Ulysse L3 50 kWh   \n",
            "\n",
            "                                         Car_name_link  Efficiency  \\\n",
            "0    https://ev-database.org/car/1619/Tesla-Model-Y...         172   \n",
            "1       https://ev-database.org/car/1991/Tesla-Model-3         137   \n",
            "2          https://ev-database.org/car/1782/BYD-ATTO-3         183   \n",
            "3    https://ev-database.org/car/1708/MG-MG4-Electr...         171   \n",
            "4    https://ev-database.org/car/1992/Tesla-Model-3...         149   \n",
            "..                                                 ...         ...   \n",
            "355  https://ev-database.org/car/1596/Citroen-e-Jum...         257   \n",
            "356  https://ev-database.org/car/1721/Fiat-E-Ulysse...         250   \n",
            "357  https://ev-database.org/car/1601/Opel-Vivaro-e...         257   \n",
            "358  https://ev-database.org/car/1348/Opel-Zafira-e...         257   \n",
            "359  https://ev-database.org/car/1723/Fiat-E-Ulysse...         257   \n",
            "\n",
            "     Fast_charge  Price.DE.  Range  Top_speed  acceleration..0.100.  \n",
            "0          670.0    59017.0    435        217                   5.0  \n",
            "1          700.0    46220.0    420        201                   6.1  \n",
            "2          370.0    44625.0    330        160                   7.3  \n",
            "3          630.0    39990.0    360        160                   7.9  \n",
            "4          780.0    55220.0    505        201                   4.4  \n",
            "..           ...        ...    ...        ...                   ...  \n",
            "355        290.0    52730.0    180        130                  12.1  \n",
            "356        290.0    55990.0    185        130                  12.1  \n",
            "357        290.0    51825.0    180        130                  12.1  \n",
            "358        290.0    69250.0    265        130                  13.3  \n",
            "359        290.0    56990.0    180        130                  12.1  \n",
            "\n",
            "[360 rows x 9 columns]\n",
            "\n",
            "DataFrame with Reexpressed Values:\n",
            "     Battery                             Car_name  \\\n",
            "0       75.0  Tesla Model Y Long Range Dual Motor   \n",
            "1       57.5                           Tata Curvv   \n",
            "2       60.5                           BYD ATTO 3   \n",
            "3       61.7               MG MG4 Electric 64 kWh   \n",
            "4       75.0  Tesla Model 3 Long Range Dual Motor   \n",
            "..       ...                                  ...   \n",
            "355     46.3      Citroen e-Jumpy Combi XL 50 kWh   \n",
            "356     46.3              Fiat E-Ulysse L2 50 kWh   \n",
            "357     46.3         Opel Vivaro-e Combi L 50 kWh   \n",
            "358     68.0          Opel Zafira-e Life M 75 kWh   \n",
            "359     46.3              Fiat E-Ulysse L3 50 kWh   \n",
            "\n",
            "                                         Car_name_link  Efficiency  \\\n",
            "0    https://ev-database.org/car/1619/Tesla-Model-Y...         172   \n",
            "1       https://ev-database.org/car/1991/Tesla-Model-3         137   \n",
            "2          https://ev-database.org/car/1782/BYD-ATTO-3         183   \n",
            "3    https://ev-database.org/car/1708/MG-MG4-Electr...         171   \n",
            "4    https://ev-database.org/car/1992/Tesla-Model-3...         149   \n",
            "..                                                 ...         ...   \n",
            "355  https://ev-database.org/car/1596/Citroen-e-Jum...         257   \n",
            "356  https://ev-database.org/car/1721/Fiat-E-Ulysse...         250   \n",
            "357  https://ev-database.org/car/1601/Opel-Vivaro-e...         257   \n",
            "358  https://ev-database.org/car/1348/Opel-Zafira-e...         257   \n",
            "359  https://ev-database.org/car/1723/Fiat-E-Ulysse...         257   \n",
            "\n",
            "     Fast_charge  Price.DE.  Range  Top_speed  acceleration..0.100.  \n",
            "0          670.0    59017.0    435        217                   5.0  \n",
            "1          700.0    46220.0    420        201                   6.1  \n",
            "2          370.0    44625.0    330        160                   7.3  \n",
            "3          630.0    39990.0    360        160                   7.9  \n",
            "4          780.0    55220.0    505        201                   4.4  \n",
            "..           ...        ...    ...        ...                   ...  \n",
            "355        290.0    52730.0    180        130                  12.1  \n",
            "356        290.0    55990.0    185        130                  12.1  \n",
            "357        290.0    51825.0    180        130                  12.1  \n",
            "358        290.0    69250.0    265        130                  13.3  \n",
            "359        290.0    56990.0    180        130                  12.1  \n",
            "\n",
            "[360 rows x 9 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 2.4"
      ],
      "metadata": {
        "id": "ZDAbzZ2oG-dG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "file_path = '/content/your_output.csv'\n",
        "\n",
        "\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "\n",
        "print(\"Original DataFrame:\")\n",
        "print(df)\n",
        "\n",
        "\n",
        "numeric_columns = ['Efficiency', 'Range', 'Top_speed']\n",
        "numeric_data = df[numeric_columns]\n",
        "\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "\n",
        "standardized_data = scaler.fit_transform(numeric_data)\n",
        "\n",
        "\n",
        "df[numeric_columns] = standardized_data\n",
        "\n",
        "\n",
        "print(\"\\nDataFrame with Standardized Numeric Fields:\")\n",
        "print(df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zxcKTdejHg_Q",
        "outputId": "268e5bf0-7aa2-452d-e8b7-83673cc9d9de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original DataFrame:\n",
            "     Battery                             Car_name  \\\n",
            "0       75.0  Tesla Model Y Long Range Dual Motor   \n",
            "1       57.5                        Tesla Model 3   \n",
            "2       60.5                           BYD ATTO 3   \n",
            "3       61.7               MG MG4 Electric 64 kWh   \n",
            "4       75.0  Tesla Model 3 Long Range Dual Motor   \n",
            "..       ...                                  ...   \n",
            "355     46.3      Citroen e-Jumpy Combi XL 50 kWh   \n",
            "356     46.3              Fiat E-Ulysse L2 50 kWh   \n",
            "357     46.3         Opel Vivaro-e Combi L 50 kWh   \n",
            "358     68.0          Opel Zafira-e Life M 75 kWh   \n",
            "359     46.3              Fiat E-Ulysse L3 50 kWh   \n",
            "\n",
            "                                         Car_name_link  Efficiency  \\\n",
            "0    https://ev-database.org/car/1619/Tesla-Model-Y...         172   \n",
            "1       https://ev-database.org/car/1991/Tesla-Model-3         137   \n",
            "2          https://ev-database.org/car/1782/BYD-ATTO-3         183   \n",
            "3    https://ev-database.org/car/1708/MG-MG4-Electr...         171   \n",
            "4    https://ev-database.org/car/1992/Tesla-Model-3...         149   \n",
            "..                                                 ...         ...   \n",
            "355  https://ev-database.org/car/1596/Citroen-e-Jum...         257   \n",
            "356  https://ev-database.org/car/1721/Fiat-E-Ulysse...         250   \n",
            "357  https://ev-database.org/car/1601/Opel-Vivaro-e...         257   \n",
            "358  https://ev-database.org/car/1348/Opel-Zafira-e...         257   \n",
            "359  https://ev-database.org/car/1723/Fiat-E-Ulysse...         257   \n",
            "\n",
            "     Fast_charge  Price.DE.  Range  Top_speed  acceleration..0.100.  \n",
            "0          670.0    59017.0    435        217                   5.0  \n",
            "1          700.0    46220.0    420        201                   6.1  \n",
            "2          370.0    44625.0    330        160                   7.3  \n",
            "3          630.0    39990.0    360        160                   7.9  \n",
            "4          780.0    55220.0    505        201                   4.4  \n",
            "..           ...        ...    ...        ...                   ...  \n",
            "355        290.0    52730.0    180        130                  12.1  \n",
            "356        290.0    55990.0    185        130                  12.1  \n",
            "357        290.0    51825.0    180        130                  12.1  \n",
            "358        290.0    69250.0    265        130                  13.3  \n",
            "359        290.0    56990.0    180        130                  12.1  \n",
            "\n",
            "[360 rows x 9 columns]\n",
            "\n",
            "DataFrame with Standardized Numeric Fields:\n",
            "     Battery                             Car_name  \\\n",
            "0       75.0  Tesla Model Y Long Range Dual Motor   \n",
            "1       57.5                        Tesla Model 3   \n",
            "2       60.5                           BYD ATTO 3   \n",
            "3       61.7               MG MG4 Electric 64 kWh   \n",
            "4       75.0  Tesla Model 3 Long Range Dual Motor   \n",
            "..       ...                                  ...   \n",
            "355     46.3      Citroen e-Jumpy Combi XL 50 kWh   \n",
            "356     46.3              Fiat E-Ulysse L2 50 kWh   \n",
            "357     46.3         Opel Vivaro-e Combi L 50 kWh   \n",
            "358     68.0          Opel Zafira-e Life M 75 kWh   \n",
            "359     46.3              Fiat E-Ulysse L3 50 kWh   \n",
            "\n",
            "                                         Car_name_link  Efficiency  \\\n",
            "0    https://ev-database.org/car/1619/Tesla-Model-Y...   -0.727285   \n",
            "1       https://ev-database.org/car/1991/Tesla-Model-3   -1.825666   \n",
            "2          https://ev-database.org/car/1782/BYD-ATTO-3   -0.382080   \n",
            "3    https://ev-database.org/car/1708/MG-MG4-Electr...   -0.758668   \n",
            "4    https://ev-database.org/car/1992/Tesla-Model-3...   -1.449078   \n",
            "..                                                 ...         ...   \n",
            "355  https://ev-database.org/car/1596/Citroen-e-Jum...    1.940212   \n",
            "356  https://ev-database.org/car/1721/Fiat-E-Ulysse...    1.720536   \n",
            "357  https://ev-database.org/car/1601/Opel-Vivaro-e...    1.940212   \n",
            "358  https://ev-database.org/car/1348/Opel-Zafira-e...    1.940212   \n",
            "359  https://ev-database.org/car/1723/Fiat-E-Ulysse...    1.940212   \n",
            "\n",
            "     Fast_charge  Price.DE.     Range  Top_speed  acceleration..0.100.  \n",
            "0          670.0    59017.0  0.609385   0.997205                   5.0  \n",
            "1          700.0    46220.0  0.469416   0.554993                   6.1  \n",
            "2          370.0    44625.0 -0.370400  -0.578178                   7.3  \n",
            "3          630.0    39990.0 -0.090462  -0.578178                   7.9  \n",
            "4          780.0    55220.0  1.262575   0.554993                   4.4  \n",
            "..           ...        ...       ...        ...                   ...  \n",
            "355        290.0    52730.0 -1.770093  -1.407327                  12.1  \n",
            "356        290.0    55990.0 -1.723437  -1.407327                  12.1  \n",
            "357        290.0    51825.0 -1.770093  -1.407327                  12.1  \n",
            "358        290.0    69250.0 -0.976934  -1.407327                  13.3  \n",
            "359        290.0    56990.0 -1.770093  -1.407327                  12.1  \n",
            "\n",
            "[360 rows x 9 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 2.5"
      ],
      "metadata": {
        "id": "ZmPtesYBBQP_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "numeric_columns = df.select_dtypes(include=[np.number]).columns\n",
        "\n",
        "\n",
        "z_scores = np.abs((df[numeric_columns] - df[numeric_columns].mean()) / df[numeric_columns].std())\n",
        "\n",
        "\n",
        "z_score_threshold = 3.0\n",
        "\n",
        "\n",
        "outliers = (z_scores > z_score_threshold).any(axis=1)\n",
        "\n",
        "\n",
        "print(\"Rows with outliers:\")\n",
        "print(df[outliers])\n",
        "\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.boxplot(data=df[numeric_columns])\n",
        "plt.title(\"Box Plot for Outlier Detection\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XgTd5MHnBP2G",
        "outputId": "8d215dd4-0db8-493a-e660-b8b2a5c19642"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows with outliers:\n",
            "     Battery                                 Car_name  \\\n",
            "25      25.0                 Dacia Spring Electric 45   \n",
            "56      74.0           Hyundai IONIQ 6 Long Range 2WD   \n",
            "67     118.0                Lucid Air Dream Edition R   \n",
            "147    118.0                Lucid Air Dream Edition P   \n",
            "210    101.7                        BMW i7 M70 xDrive   \n",
            "215     83.0             Maserati GranTurismo Folgore   \n",
            "231     83.7                   Porsche Taycan Turbo S   \n",
            "305     90.0  Mercedes eVito Tourer Extra-Long 90 kWh   \n",
            "329     83.7     Porsche Taycan Turbo S Cross Turismo   \n",
            "335     83.7     Porsche Taycan Turbo S Sport Turismo   \n",
            "346     60.0  Mercedes eVito Tourer Extra-Long 60 kWh   \n",
            "\n",
            "                                         Car_name_link  Efficiency  \\\n",
            "25   https://ev-database.org/car/1705/Dacia-Spring-...         152   \n",
            "56   https://ev-database.org/car/1718/Hyundai-IONIQ...         149   \n",
            "67   https://ev-database.org/car/1696/Lucid-Air-Dre...         172   \n",
            "147  https://ev-database.org/car/1098/Lucid-Air-Dre...         177   \n",
            "210  https://ev-database.org/car/1838/BMW-i7-M70-xD...         208   \n",
            "215  https://ev-database.org/car/1803/Maserati-Gran...         195   \n",
            "231  https://ev-database.org/car/1928/Porsche-Tayca...         195   \n",
            "305  https://ev-database.org/car/1616/Mercedes-eVit...         295   \n",
            "329  https://ev-database.org/car/1932/Porsche-Tayca...         204   \n",
            "335  https://ev-database.org/car/1626/Porsche-Tayca...         212   \n",
            "346  https://ev-database.org/car/1618/Mercedes-eVit...         293   \n",
            "\n",
            "     Fast_charge  Price.DE.  Range  Top_speed  acceleration..0.100.  \n",
            "25         180.0    22550.0    165        125                  19.1  \n",
            "56        1290.0    54000.0    495        185                   7.4  \n",
            "67         870.0   218000.0    685        270                   2.9  \n",
            "147        840.0   218000.0    665        270                   2.7  \n",
            "210        730.0   181800.0    490        250                   3.7  \n",
            "215        990.0        NaN    425        320                   2.7  \n",
            "231       1060.0   197740.0    430        260                   2.8  \n",
            "305        310.0    65140.0    305        160                  12.0  \n",
            "329       1010.0   199168.0    410        250                   2.9  \n",
            "335        970.0   198692.0    395        260                   2.8  \n",
            "346        300.0    61571.0    205        160                  12.0  \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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XX39do0aNUsOGDdWvXz9VqlRJsbGxmjdvntLS0rRq1SqVLl3aeszjjz+uN954Q2FhYRowYICOHTumuXPnql69ek4n3vP19VXdunW1ZMkS3XrrrSpTpozq16+v+vXrZ6u2MWPG6IsvvtB9992nfv36qUmTJjp37pxiYmL06aef6uDBg9blBXPjjz/+0MKFCyVd3lv/22+/aenSpYqPj9fo0aOdvtBo06aNBg8erClTpig6OlodOnRQ0aJFtXfvXi1dulRvvvmmunfvLunyaz5nzhy9/PLLqlGjhipUqKB//OMf2V6etm3bqnfv3nrrrbe0d+9edezYUWlpadq4caPatm2rYcOGWfP55ptv9MYbbygwMFBVq1ZV8+bNMyxnXq4/AAA3ct+J+gEAhV1ml8Lz8fExjRo1MnPmzHG6zJcxxpw5c8aMHDnSBAYGmqJFi5qaNWuaadOmWeO2b99uihQpYoYPH+70uEuXLpmmTZuawMBAc+rUqSzrSb8U3tKlS69Zd2aXwjPGmG+++ca0bNnS+Pr6Gj8/P3P//feb3377LcPjX3rpJVO5cmXj4eFx3cviZXYpvHTvvPOOqV27tilatKgJCAgwQ4YMybB8bdq0MfXq1bvm8mRmw4YNpkuXLqZcuXKmaNGi5pZbbjFPPPGEOXjwYKbjFy5caKpVq2a8vLxMo0aNzJo1azJcCs8YYzZt2mSaNGlivLy8nC6Ll51L4RlzeR2YMGGCqVGjhvHy8jLlypUzLVq0MK+//rq5cOGCMeZ/r8+0adOyvbzBwcHWOuhwOIyfn5+pV6+eeeKJJ8yWLVuyfNy7775rmjRpYnx9fU3JkiVNgwYNzNixY82ff/5pjYmPjzedO3c2JUuWNJKcLouXneUx5vI6PG3aNFO7dm3j5eVlypcvbzp16mS2b99ujYmNjTWtW7c2vr6+RpL13F19Kbx0N7L+ZPbaAgDcy2EMZ0MBAAAAAMDO+M09AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbK6Iuwu4maWlpenPP/9UyZIl5XA43F0OAAAAAKCAM8bozJkzCgwMlIdH9vfHE+6v4c8//1SVKlXcXQYAAAAAoJA5cuSIgoKCsj2ecH8NJUuWlHT5SfXz83NzNQAAAACAgi4pKUlVqlSx8mh2Ee6vIf1QfD8/P8I9AAAAACDf5PSn4ZxQDwAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAHC59957T3fffbfee+89d5cCAEChQLgHAAAudfr0aS1cuFBpaWlauHChTp8+7e6SAAAo8Aj3AADApZ5//nmlpaVJktLS0vTCCy+4uSIAAAo+wj0AAHCZbdu2KSYmxqltx44d2rZtm5sqAgCgcCDcAwAAl0hLS1NERESmfREREdbefAAA4HqEewAA4BKbN29WUlJSpn1JSUnavHlzPlcEAEDhQbgHAAAuERoaKj8/v0z7/P39FRoams8VAQBQeBDuAQCAS3h4eGR5WP7kyZPl4cFmBwAAeYVPWQAA4DJ33HGHGjRo4NTWsGFD3X777W6qCACAwoFwDwAAXOqVV16x9tJ7eHjo5ZdfdnNFAAAUfIR7AADgUqVKlVKvXr3k4eGhXr16qVSpUu4uCQCAAs9hjDHuLuJmlZSUJH9/fyUmJmZ5giAAAAAAAFwltzmUPfcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmchTup0yZoqZNm6pkyZKqUKGCunbtqt27dzuNSU5O1tChQ1W2bFmVKFFC3bp1U0JCgtOYw4cPq3PnzipWrJgqVKigMWPG6NKlS05j1q9fr9tvv13e3t6qUaOGIiMjM9Qza9YshYSEyMfHR82bN9ePP/6Y41oAAIDrRUVF6eGHH1ZUVJS7SwEAoFDIUbj//vvvNXToUP3www9au3atLl68qA4dOujcuXPWmJEjR+rLL7/U0qVL9f333+vPP//UQw89ZPWnpqaqc+fOunDhgjZt2qQFCxYoMjJSEydOtMYcOHBAnTt3Vtu2bRUdHa0RI0Zo4MCBWrNmjTVmyZIlGjVqlCZNmqSffvpJt912m8LCwnTs2LFs1wIAAFwvOTlZr776qhISEvTqq68qOTnZ3SUBAFDgOYwxJrcPPn78uCpUqKDvv/9erVu3VmJiosqXL6/Fixere/fukqTY2FjVqVNHmzdv1p133qmvvvpK9913n/78808FBARIkubOnatx48bp+PHj8vLy0rhx47Ry5Ur9+uuv1rx69uyp06dPa/Xq1ZKk5s2bq2nTpnrnnXckSWlpaapSpYqGDx+u8ePHZ6uW60lKSpK/v78SExPl5+eX26cJAIBCZe7cuVq8eLF1Pzw8XIMHD3ZjRQAA2Educ+gN/eY+MTFRklSmTBlJ0vbt23Xx4kW1a9fOGlO7dm3dcsst2rx5syRp8+bNatCggRXsJSksLExJSUnauXOnNebKaaSPSZ/GhQsXtH37dqcxHh4eateunTUmO7VcLSUlRUlJSU43AACQfXFxcfroo4+c2hYvXqy4uDg3VQQAQOGQ63CflpamESNGqGXLlqpfv74kKT4+Xl5eXipVqpTT2ICAAMXHx1tjrgz26f3pfdcak5SUpPPnz+vEiRNKTU3NdMyV07heLVebMmWK/P39rVuVKlWy+WwAAABjjKZMmaKrDwrMqh0AALhOrsP90KFD9euvv+rjjz92ZT1uNWHCBCUmJlq3I0eOuLskAABs4+DBg4qJicm0LyYmRgcPHszfggAAKESK5OZBw4YN04oVK7RhwwYFBQVZ7RUrVtSFCxd0+vRppz3mCQkJqlixojXm6rPap5/B/soxV5/VPiEhQX5+fvL19ZWnp6c8PT0zHXPlNK5Xy9W8vb3l7e2dg2cCAAAAAAD3y9Gee2OMhg0bps8++0zffvutqlat6tTfpEkTFS1aVOvWrbPadu/ercOHDys0NFSSFBoaqpiYGKez2q9du1Z+fn6qW7euNebKaaSPSZ+Gl5eXmjRp4jQmLS1N69ats8ZkpxYAAOA6wcHBKlGiRKZ9JUqUUHBwcD5XBABA4ZGjPfdDhw7V4sWLtXz5cpUsWdL67bq/v798fX3l7++vAQMGaNSoUSpTpoz8/Pw0fPhwhYaGWmen79Chg+rWravevXtr6tSpio+P1wsvvKChQ4dae82ffPJJvfPOOxo7dqwef/xxffvtt/rkk0+0cuVKq5ZRo0apb9++uuOOO9SsWTPNnDlT586dU//+/a2arlcLAABwncOHD+vs2bOZ9p09e1aHDx9WSEhI/hYFAEAhkaNwP2fOHEnS3Xff7dQ+f/589evXT5I0Y8YMeXh4qFu3bkpJSVFYWJhmz55tjfX09NSKFSs0ZMgQhYaGqnjx4urbt69efPFFa0zVqlW1cuVKjRw5Um+++aaCgoL03nvvKSwszBrTo0cPHT9+XBMnTlR8fLwaNWqk1atXO51k73q1AAAA1wkODlbTpk21devWDH3NmjVjzz0AAHnohq5zX9BxnXsAAHImLi5OvXr1UlpamtXm6emphQsXqnLlym6sDAAAe3DLde4BAACuFBQUpF69ejm19erVi2APAEAeI9wDAACX6tWrl8qVKydJKl++vMLDw91cEQAABR/hHgAAuJSPj49Gjx6tgIAAjRo1Sj4+Pu4uCQCAAo/f3F8Dv7kHAAAAAOQnfnMPAAAAAEAhRbgHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AALhcVFSUHn74YUVFRbm7FAAACgXCPQAAcKnk5GRNnz5dCQkJmj59upKTk91dEgAABR7hHgAAuNTChQt18uRJSdLJkye1aNEiN1cEAEDBR7gHAAAuExcXp0WLFskYI0kyxmjRokWKi4tzc2UAABRshHsAAOASxhjNmDEjy/b0wA8AAFyPcA8AAFzi0KFD2rp1q1JTU53aU1NTtXXrVh06dMhNlQEAUPAR7gEAgEsEBweradOm8vT0dGr39PRUs2bNFBwc7KbKAAAo+Aj3AADAJRwOh0aOHJllu8PhcENVAAAUDoR7AADgMkFBQQoPD7eCvMPhUHh4uCpXruzmygAAKNgI9wAAwKV69eqlsmXLSpLKlSun8PBwN1cEAEDBR7gHAAAu5ePjo9GjRysgIECjRo2Sj4+Pu0sCAKDAcxiuS5OlpKQk+fv7KzExUX5+fu4uBwAAAABQwOU2h7LnHgAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAABcLioqSg8//LCioqLcXQoAAIUC4R4AALhUcnKyXn31VSUkJOjVV19VcnKyu0sCAKDAI9wDAACXioyM1JkzZyRJZ86c0YIFC9xcEQAABR/hHgAAuExcXJw++ugjp7bFixcrLi7OTRUBAFA4EO4BAIBLGGM0ZcoUGWOy1Q4AAFyHcA8AAFzi4MGDiomJybQvJiZGBw8ezN+CAAAoRAj3AAAAAADYHOEeAAAAAACbI9wDAACXCAkJUYMGDTLta9iwoUJCQvK3IAAAChHCPQAAcAmHw6EJEyZk2e5wONxQFQAAhQPhHgAAuExQUJAee+wxp7bHHntMlStXdlNFAAAUDoR7AADgUv369VPJkiUlSX5+furbt6+bKwIAoOAj3AMAAJfy8fHRc889p4CAAE2YMEE+Pj7uLgkAgAKPcA8AAAAAgM0R7gEAgEslJydr+vTpSkhI0PTp05WcnOzukgAAKPAI9wAAwKUWLlyokydPSpJOnjypRYsWubkiAAAKPsI9AABwmbi4OC1atEjGGEmSMUaLFi1SXFycmysDAKBgI9wDAACXMMZoxowZWbanB34AAOB6hHsAAOAShw4d0tatW5WamurUnpqaqq1bt+rQoUNuqgwAgIKPcA8AAFwiODhYTZs2laenp1O7p6enmjVrpuDgYDdVBgBAwUe4BwAALuFwODRy5Mgs2x0OhxuqAgCgcCDcAwAAlwkKClKPHj2c2nr06KHKlSu7qSIAAAoHwj0AAAAAADZHuAcAAC4TFxenJUuWOLUtWbKES+EBAJDHCPcAAMAluBQeAADuQ7gHAAAuwaXwAABwH8I9AABwCS6FBwCA+xDuAQCAS3ApPAAA3IdwDwAAXCYoKEjh4eFWkHc4HAoPD+dSeAAA5DHCPQAAcKnu3bs7hftu3bq5uSIAAAo+wj0AAHCpTz/9VGlpaZKktLQ0LVu2zM0VAQBQ8OU43G/YsEH333+/AgMD5XA49Pnnnzv19+vXTw6Hw+nWsWNHpzF//fWXwsPD5efnp1KlSmnAgAE6e/as05gdO3aoVatW8vHxUZUqVTR16tQMtSxdulS1a9eWj4+PGjRooFWrVjn1G2M0ceJEVapUSb6+vmrXrp327t2b00UGAADZFBcXp0WLFjm1LVq0iOvcAwCQx3Ic7s+dO6fbbrtNs2bNynJMx44ddfToUev20UcfOfWHh4dr586dWrt2rVasWKENGzZo0KBBVn9SUpI6dOig4OBgbd++XdOmTVNERITeffdda8ymTZv06KOPasCAAfr555/VtWtXde3aVb/++qs1ZurUqXrrrbc0d+5cbdmyRcWLF1dYWJiSk5NzutgAAOA6uM49AADu4zA38EnrcDj02WefqWvXrlZbv379dPr06Qx79NPt2rVLdevW1datW3XHHXdIklavXq17771XcXFxCgwM1Jw5c/T8888rPj5eXl5ekqTx48fr888/V2xsrCSpR48eOnfunFasWGFN+84771SjRo00d+5cGWMUGBio0aNH69lnn5UkJSYmKiAgQJGRkerZs+d1ly8pKUn+/v5KTEyUn59fbp4iAAAKjYMHD6pPnz5Z9n/wwQcKCQnJv4IAALCh3ObQPPnN/fr161WhQgXVqlVLQ4YM0cmTJ62+zZs3q1SpUlawl6R27drJw8NDW7Zssca0bt3aCvaSFBYWpt27d+vUqVPWmHbt2jnNNywsTJs3b5YkHThwQPHx8U5j/P391bx5c2vM1VJSUpSUlOR0AwAA2cN17gEAcB+Xh/uOHTvqgw8+0Lp16/Taa6/p+++/V6dOnZSamipJio+PV4UKFZweU6RIEZUpU0bx8fHWmICAAKcx6fevN+bK/isfl9mYq02ZMkX+/v7WrUqVKjlefgAACiuucw8AgPu4PNz37NlTDzzwgBo0aKCuXbtqxYoV2rp1q9avX+/qWbnchAkTlJiYaN2OHDni7pIAALAVrnMPAIB75Pml8KpVq6Zy5cpp3759kqSKFSvq2LFjTmMuXbqkv/76SxUrVrTGJCQkOI1Jv3+9MVf2X/m4zMZczdvbW35+fk43AACQM7169VLZsmUlSeXKlVN4eLibKwIAoODL83AfFxenkydPqlKlSpKk0NBQnT59Wtu3b7fGfPvtt0pLS1Pz5s2tMRs2bNDFixetMWvXrlWtWrVUunRpa8y6deuc5rV27VqFhoZKkqpWraqKFSs6jUlKStKWLVusMQAAwPV8fHx07733ysPDQ506dZKPj4+7SwIAoMDLcbg/e/asoqOjFR0dLenyieuio6N1+PBhnT17VmPGjNEPP/yggwcPat26derSpYtq1KihsLAwSVKdOnXUsWNHPfHEE/rxxx8VFRWlYcOGqWfPngoMDJQkPfbYY/Ly8tKAAQO0c+dOLVmyRG+++aZGjRpl1fHMM89o9erVmj59umJjYxUREaFt27Zp2LBhki4fBjhixAi9/PLL+uKLLxQTE6M+ffooMDDQ6ez+AADAtZKTk7Vq1SqlpaVp1apVXIIWAIB8kONwv23bNjVu3FiNGzeWJI0aNUqNGzfWxIkT5enpqR07duiBBx7QrbfeqgEDBqhJkybauHGjvL29rWksWrRItWvX1j333KN7771Xd911l9M17P39/fX111/rwIEDatKkiUaPHq2JEydq0KBB1pgWLVpo8eLFevfdd3Xbbbfp008/1eeff6769etbY8aOHavhw4dr0KBBatq0qc6ePavVq1ezBwEAgDy0cOFC60o5J0+e1KJFi9xcEQAABd8NXee+oOM69wAA5ExcXJx69+5tXSVHunxVnA8++EBBQUFurAwAAHu4qa5zDwAACh9jjGbMmJFlO/sTAADIO4R7AADgEocOHdLWrVud9tpLUmpqqrZu3apDhw65qTIAAAo+wj0AAHCJ4OBgNW3aVJ6enk7tnp6eatasmYKDg91UGQAABR/hHgAAuITD4dDIkSOzbHc4HG6oCgCAwoFwDwAAXCYoKEjh4eFObeHh4apcubKbKgIAoHAg3AMAAJfq3r27PDwub2J4eHioW7dubq4IAICCj3APAABc6tNPP1VaWpokKS0tTcuWLXNzRQAAFHyEewAA4DJxcXFauHChU9vChQsVFxfnpooAACgcCPcAAMAlsrqefVpaGte5BwAgjxHuAQCAS6Rf5/7qEG+M4Tr3AADkMcI9AABwiVtuuUV+fn6Z9vn5+emWW27J54oAACg8CPcAAMAlDh8+rKSkpEz7kpKSdPjw4XyuCACAwoNwDwAAXCIoKOiG+gEAQO4R7gEAgEt8+eWXN9QPAAByj3APAABc4oEHHpCnp2emfUWKFNEDDzyQzxUBAFB4EO4BAIBLeHp6auzYsZn2jR8/PsvgDwAAbhzhHgAAuEynTp1Urlw5p7by5curQ4cObqoIAIDCgXAPAABcqkWLFte8DwAAXI9wDwAAXCYuLi7DifO++OILxcXFuakiAAAKB8I9AABwCWOMpkyZImNMttoBAIDrEO4BAIBLHDx4UDExMZn2xcTE6ODBg/lbEAAAhQjhHgAAAAAAmyPcAwAAlwgJCVHDhg0z7bvtttsUEhKSvwUBAFCIEO4BAIBLOBwOjR8/Xg6Hw6ndw8Mj03YAAOA6hHsAAOAyQUFBevTRR53aHn30UVWuXNlNFQEAUDgQ7gEAgEv169dPfn5+kiR/f3/17dvXzRUBAFDwEe4BAIBL+fj4aMKECQoICND48ePl4+Pj7pIAACjwHIaLzmYpKSlJ/v7+SkxMtPZAAAAAAACQV3KbQ9lzDwAAAACAzRHuAQCAy0VFRenhhx9WVFSUu0sBAKBQINwDAACXSk5O1vTp05WQkKDp06crOTnZ3SUBAFDgEe4BAIBLLVy4UCdPnpQknTx5UosWLXJzRQAAFHyEewAA4DJxcXFatGiR0s/Xa4zRokWLFBcX5+bKAAAo2Aj3AADAJYwxmjFjRpbtXKAHAIC8Q7gHAAAucejQIW3dulWpqalO7ampqdq6dasOHTrkpsoAACj4CPcAAMAlgoOD1bRpU3l6ejq1e3p6qlmzZgoODnZTZQAAFHyEewAA4BIOh0MjR47Mst3hcLihKgAACgfCPQAAcJmgoCB169bNqa1bt26qXLmymyoCAKBwINwDAACXiomJueZ9AADgeoR7AADgMtu2bdOuXbuc2n777Tdt27bNTRUBAFA4EO4BAIBLpKWlaeLEiZn2TZw4UWlpaflcEQAAhQfhHgAAuMTmzZt19uzZTPvOnj2rzZs353NFAAAUHoR7AADgEpUqVbqhfgAAkHuEewAA4BJVq1bVrbfemmlfrVq1VLVq1XyuCACAwoNwDwAAXMLhcCgiIiLTvoiICK5zDwBAHiLcAwAAlwkKCtIjjzzi1NajRw+ucw8AQB4j3AMAAJcaOHCgPDwub2J4eHhowIABbq4IAICCj3APAABcKjEx0brsXVpamhITE91cEQAABR/hHgAAuNRTTz3ldH/o0KFuqgQAgMKDcA8AAFzmq6++0vHjx53ajh07pq+++spNFQEAUDgQ7gEAgEukpqZq6tSpmfZNnTpVqamp+VwRAACFB+EeAAC4xBdffJFlgE9NTdUXX3yRzxUBAFB4EO4BAIBLPPDAA/L09My0r0iRInrggQfyuSIAAAoPwj0AAHAJT09PjR07NtO+8ePHZxn8AQDAjSPcAwAAl+nUqZPKly/v1FahQgV16NDBTRUBAFA4EO4BAIBL/fOf/3S6P2XKFDdVAgBA4UG4BwAALjVz5kyn+2+++aZ7CgEAoBAh3AMAAJfZtm2bYmJinNp27Nihbdu2uakiAAAKB8I9AABwibS0NEVERGTaFxERobS0tPwtCACAQoRwDwAAXGLz5s1KSkrKtC8pKUmbN2/O54oAACg8CPcAAMAlmjVrdkP9AAAg9wj3AADAJVasWHFD/QAAIPeKuLsAAACQP4wxSk5OzrPpt2/fXm+99ZZSU1Mz9Hl6eqp9+/Y6f/58ns3fx8dHDocjz6YPAMDNjHAPAEAhkZycrLCwMLfMOzU1Vffee2+ezmPNmjXy9fXN03kAAHCz4rB8AAAAAABsjj33AAAUEj4+PlqzZk2ez+fIkSMaOHCgdf/DDz9UhQoV8ny+Pj4+eT4PAABuVoR7AAAKCYfDkS+HrVepUsX6u3Xr1goODs7zeQIAUNhxWD4AAMgzzz//vLtLAACgUCDcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgczkO9xs2bND999+vwMBAORwOff755079xhhNnDhRlSpVkq+vr9q1a6e9e/c6jfnrr78UHh4uPz8/lSpVSgMGDNDZs2edxuzYsUOtWrWSj4+PqlSpoqlTp2aoZenSpapdu7Z8fHzUoEEDrVq1Kse1AAAAAABgdzkO9+fOndNtt92mWbNmZdo/depUvfXWW5o7d662bNmi4sWLKywsTMnJydaY8PBw7dy5U2vXrtWKFSu0YcMGDRo0yOpPSkpShw4dFBwcrO3bt2vatGmKiIjQu+++a43ZtGmTHn30UQ0YMEA///yzunbtqq5du+rXX3/NUS0AAAAAANidwxhjcv1gh0OfffaZunbtKunynvLAwECNHj1azz77rCQpMTFRAQEBioyMVM+ePbVr1y7VrVtXW7du1R133CFJWr16te69917FxcUpMDBQc+bM0fPPP6/4+Hh5eXlJksaPH6/PP/9csbGxkqQePXro3LlzWrFihVXPnXfeqUaNGmnu3LnZquV6kpKS5O/vr8TERPn5+eX2aQIAoFA5f/68wsLCJElr1qyRr6+vmysCAMA+cptDXfqb+wMHDig+Pl7t2rWz2vz9/dW8eXNt3rxZkrR582aVKlXKCvaS1K5dO3l4eGjLli3WmNatW1vBXpLCwsK0e/dunTp1yhpz5XzSx6TPJzu1XC0lJUVJSUlONwAAAAAAbnYuDffx8fGSpICAAKf2gIAAqy8+Pl4VKlRw6i9SpIjKlCnjNCazaVw5j6zGXNl/vVquNmXKFPn7+1u3KlWqZGOpAQAAAABwL86Wf4UJEyYoMTHRuh05csTdJQEAAAAAcF0uDfcVK1aUJCUkJDi1JyQkWH0VK1bUsWPHnPovXbqkv/76y2lMZtO4ch5Zjbmy/3q1XM3b21t+fn5ONwAAAAAAbnYuDfdVq1ZVxYoVtW7dOqstKSlJW7ZsUWhoqCQpNDRUp0+f1vbt260x3377rdLS0tS8eXNrzIYNG3Tx4kVrzNq1a1WrVi2VLl3aGnPlfNLHpM8nO7UAAAAAAFAQ5Djcnz17VtHR0YqOjpZ0+cR10dHROnz4sBwOh0aMGKGXX35ZX3zxhWJiYtSnTx8FBgZaZ9SvU6eOOnbsqCeeeEI//vijoqKiNGzYMPXs2VOBgYGSpMcee0xeXl4aMGCAdu7cqSVLlujNN9/UqFGjrDqeeeYZrV69WtOnT1dsbKwiIiK0bds2DRs2TJKyVQsAAAAAAAVBkZw+YNu2bWrbtq11Pz1w9+3bV5GRkRo7dqzOnTunQYMG6fTp07rrrru0evVq+fj4WI9ZtGiRhg0bpnvuuUceHh7q1q2b3nrrLavf399fX3/9tYYOHaomTZqoXLlymjhxogYNGmSNadGihRYvXqwXXnhBzz33nGrWrKnPP/9c9evXt8ZkpxYAAAAAAOzuhq5zX9BxnXsAAHKO69wDAJB7N8V17gEAAAAAQP4j3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbK6IuwsAAKAwM8YoOTnZ3WW41JXLU9CWTZJ8fHzkcDjcXQYAAE4I9wAAuFFycrLCwsLcXUae6dKli7tLcLk1a9bI19fX3WUAAOCEw/IBAAAAALA59twDAHCTGCfJy91FuICRdPH//i4qqSAcwH5B0mvuLgIAgGsg3AMAcJPwkuRVIKKw5O3uAlzOuLsAAACuicPyAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2V8TdBQAAUJgZY6y/L1xucVcpuIYLV/x95WsGAMDNgnAPAIAbpaSkWH+/5sY6kH0pKSkqVqyYu8sAAMAJh+UDAAAAAGBz7LkHAMCNvL29rb/HSfJyXym4hgv635EVV75mAADcLAj3AAC4kcPhsP72kuQlR9aD4Ub/+539la8ZAAA3Cw7LBwAAAADA5gj3AAAAAADYHOEeAAAAAACbI9wDAAAAAGBzhHsAAAAAAGyOcA8AAAAAgM0R7gEAAAAAsDmXh/uIiAg5HA6nW+3ata3+5ORkDR06VGXLllWJEiXUrVs3JSQkOE3j8OHD6ty5s4oVK6YKFSpozJgxunTpktOY9evX6/bbb5e3t7dq1KihyMjIDLXMmjVLISEh8vHxUfPmzfXjjz+6enEBAAAAAHC7PNlzX69ePR09etS6/fe//7X6Ro4cqS+//FJLly7V999/rz///FMPPfSQ1Z+amqrOnTvrwoUL2rRpkxYsWKDIyEhNnDjRGnPgwAF17txZbdu2VXR0tEaMGKGBAwdqzZo11pglS5Zo1KhRmjRpkn766SfddtttCgsL07Fjx/JikQEAAAAAcBuHMca4coIRERH6/PPPFR0dnaEvMTFR5cuX1+LFi9W9e3dJUmxsrOrUqaPNmzfrzjvv1FdffaX77rtPf/75pwICAiRJc+fO1bhx43T8+HF5eXlp3LhxWrlypX799Vdr2j179tTp06e1evVqSVLz5s3VtGlTvfPOO5KktLQ0ValSRcOHD9f48eOztSxJSUny9/dXYmKi/Pz8buRpAQAgU+fPn1dYWJgk6f9J8pLDvQUhUxdk9NL//b1mzRr5+vq6tR4AQMGV2xyaJ3vu9+7dq8DAQFWrVk3h4eE6fPiwJGn79u26ePGi2rVrZ42tXbu2brnlFm3evFmStHnzZjVo0MAK9pIUFhampKQk7dy50xpz5TTSx6RP48KFC9q+fbvTGA8PD7Vr184ak5mUlBQlJSU53QAAAAAAuNm5PNw3b95ckZGRWr16tebMmaMDBw6oVatWOnPmjOLj4+Xl5aVSpUo5PSYgIEDx8fGSpPj4eKdgn96f3netMUlJSTp//rxOnDih1NTUTMekTyMzU6ZMkb+/v3WrUqVKrp4DAAAAAADyUxFXT7BTp07W3w0bNlTz5s0VHBysTz755KY/hG3ChAkaNWqUdT8pKYmADwAAAAC46eX5pfBKlSqlW2+9Vfv27VPFihV14cIFnT592mlMQkKCKlasKEmqWLFihrPnp9+/3hg/Pz/5+vqqXLly8vT0zHRM+jQy4+3tLT8/P6cbAAAAAAA3uzwP92fPntX+/ftVqVIlNWnSREWLFtW6deus/t27d+vw4cMKDQ2VJIWGhiomJsbprPZr166Vn5+f6tata425chrpY9Kn4eXlpSZNmjiNSUtL07p166wxAAAAAAAUFC4P988++6y+//57HTx4UJs2bdKDDz4oT09PPfroo/L399eAAQM0atQofffdd9q+fbv69++v0NBQ3XnnnZKkDh06qG7duurdu7d++eUXrVmzRi+88IKGDh0qb29vSdKTTz6p33//XWPHjlVsbKxmz56tTz75RCNHjrTqGDVqlObNm6cFCxZo165dGjJkiM6dO6f+/fu7epEBAAAAAHArl//mPi4uTo8++qhOnjyp8uXL66677tIPP/yg8uXLS5JmzJghDw8PdevWTSkpKQoLC9Ps2bOtx3t6emrFihUaMmSIQkNDVbx4cfXt21cvvviiNaZq1apauXKlRo4cqTfffFNBQUF67733rEsJSVKPHj10/PhxTZw4UfHx8WrUqJFWr16d4SR7AAAAAADYncuvc1+QcJ17AEBeu/I69+Mkebm3HJcwki7+399FJTncWIurXJD02v/9zXXuAQB5Kbc51OV77gEAQO68dv0hAAAAmcrzE+oBAAAAAIC8xZ57AADcyMfHR2vWrHF3GS6VnJysLl26SJKWL18uHx8fN1fkWgVteQAABQPhHgAAN3I4HAX699s+Pj4FevkAALhZcFg+AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwOcI9AAAAAAA2R7gHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMAAAAAYHOEewAAAAAAbI5wDwAAAACAzRHuAQAAAACwuSLuLgAAAOQPY4ySk5PzfD5XziM/5pfOx8dHDocj3+YHAMDNhHAPAEAhkZycrLCwsHydZ5cuXfJtXmvWrJGvr2++zQ8AgJsJh+UDAAAAAGBz7LkHAKCQ8PHx0Zo1a/J8PsYYpaSkSJK8vb3z7VB5Hx+ffJkPAAA3I8I9AACFhMPhyLfD1osVK5Yv8wEAAJdxWD4AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgc4R7AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcA3LBJkyapdevWmjRpkrtLAQAAKJQI9wCAG5KQkKDvvvtOkvTdd98pISHBzRUBuBlFRUXp4YcfVlRUlLtLAYACiXAPIFfCwsLUunVrhYWFubsUuNlTTz3ldH/o0KFuqgTAzSo5OVnTp09XQkKCpk+fruTkZHeXhJtA79691bp1a/Xu3dvdpeAmwZeAN6ZQhPtZs2YpJCREPj4+at68uX788Ud3lwTY2g8//KDz589Lks6fP68ffvjBzRUhM8YYnT9/Pk9vy5cv1/Hjx53me+zYMS1fvjzP522McdMzCyCnFi5cqJMnT0qSTp48qUWLFrm5Irjb3r17dejQIUnSoUOHtHfvXjdXBHfjS8Ab5zAFfOtoyZIl6tOnj+bOnavmzZtr5syZWrp0qXbv3q0KFSpc87FJSUny9/dXYmKi/Pz88qli4ObXunXrDG0bNmxwQyX2Y4xRcnJyvnxgJScnq0ePHnk+H3dZsmSJfHx88nw+Pj4+8vHxkcPhyPN5AZKUlpamxMTEfJmXMUYpKSl5Oo/4+HiNHDlSaWlpVpuHh4dmzJihihUr5um8vb298+V/19/fXx4ehWKfmcv84x//0KVLl6z7RYoU0bfffuvGiuBu7733nj788EMZY+RwONSnTx8NGDDA3WW5RW5zaIEP982bN1fTpk31zjvvSLr8gVmlShUNHz5c48ePv+Zjc/Ok5teGe1pampKSkvJ0Hu7k5+eXLx+Seb3Rnp9BLr/Widdff10xMTEZ2hs0aKBnn302z+ZbUNaJ8+fP81MGG1qzZo18fX3dXQYKiVOnTqlLly7uLgM5sHz5cpUuXTrPpl/Qti+XLFmiVatWZWi/99578/xL6fzYnsiPL4UL2jqRkJCg8ePHZ/gS8J///KcCAgLybL436/ZlbsN9kdwWaAcXLlzQ9u3bNWHCBKvNw8ND7dq10+bNmzOMT0lJcfr2OjcrcnJyMhvuNpOXG+2FaX2IiYlR37593V2GSxDkAAA3k8KyPbFq1apMQ78d5fW2RGFYJ9LS0jR27Fh3l+Ey+bF9WaDD/YkTJ5Samprh256AgADFxsZmGD9lyhRNnjw5v8oDUAj5+PhozZo1HJbvAvl9WD6QX7y9vd1dAnKI1wzAzaBAH5b/559/qnLlytq0aZNCQ0Ot9rFjx+r777/Xli1bnMZntue+SpUqHJbvBjfrITI5VdAOy09JSdGgQYOy7H/33XfzbAOnoKwT+Sl9/ctr4eHhOnHihHW/fPnyWrhwYZ7Pt6C8TsDV8ut/N31eef2b+6NHj2ro0KFKTU212jw9PTVr1ixVqlQpT+edX7+55xDs7Lt48aIGDhyYZf97772nokWL5tn8OSw/Z/JjnTDG6PXXX9fOnTudTpbrcDhUr149Pfvss3n2XN6s25cclp+JcuXKydPTM8M1lxMSEjI9gYu3t/cNBxOHwyFfX998OaS3bNmyeT4P3Jj8XB+k/FknmjVrlukVJ0JDQ1W7du08nz+yL339y2tz5szRww8/bN2fPXs2P2sAbkB+/e+mK1asWJ5Ov3Tp0goPD3c6UVavXr1Ut27dPJ1vQVLQti979uypjz/+OEN7eHi4br311jyff0FQ0NaJ5557Tr17987wJeDzzz+vypUr5/n8C4oCfVpPLy8vNWnSROvWrbPa0tLStG7dOqc9+QCy7/XXX8+0/bXXXsvnSnCzCAgIUNu2bSVJbdu2zdMT3wCwp169elkBoVy5cgoPD3dzRXCnp556SkWKOO9jLFKkiAYPHuymiuBuQUFBCg8Pt/ZsOxwOhYeHE+xzqECHe0kaNWqU5s2bpwULFmjXrl0aMmSIzp07p/79+7u7NMC2pk6des37KHwmT56sDRs2cN4SAJny8fHR6NGjFRAQoFGjRnEeC+hf//rXNe+j8OFLwBtXoA/Ll6QePXro+PHjmjhxouLj49WoUSOtXr2aPUvADbjzzjvl6+ur8+fPy9fXV3feeae7SwIA3ORatmypli1bursM3CRq1qyp4OBgHTp0SMHBwapZs6a7S4KbpX8JOHPmTI0YMYIvAXOhQJ9Q70bl9kQGAAAAAADkRm5zaIE/LB8AAAAAgIKOcA8AAAAAgM0R7gEAAAAAsDnCPQAAAAAANke4BwAAAADA5gj3AAAAAADYHOEeAAAAAACbI9wDAAAAAGBzhHsAAAAAAGyOcA8AAAAAgM0R7gEAAAAAsDnCPQAAAAAANke4BwAAAADA5oq4u4CbmTFGkpSUlOTmSgAAAAAAhUF6/kzPo9lFuL+GM2fOSJKqVKni5koAAAAAAIXJmTNn5O/vn+3xDpPTrwMKkbS0NP35558qWbKkHA6Hu8txq6SkJFWpUkVHjhyRn5+fu8vBTYB1AldifcDVWCdwNdYJXI11AldjnbjMGKMzZ84oMDBQHh7Z/yU9e+6vwcPDQ0FBQe4u46bi5+dXqP/RkBHrBK7E+oCrsU7gaqwTuBrrBK7GOqEc7bFPxwn1AAAAAACwOcI9AAAAAAA2R7hHtnh7e2vSpEny9vZ2dym4SbBO4EqsD7ga6wSuxjqBq7FO4GqsEzeGE+oBAAAAAGBz7LkHAAAAAMDmCPcAAAAAANgc4R4AAAAAAJsj3AMFVHx8vNq3b6/ixYurVKlSWbY5HA59/vnn2ZpmRESEGjVqlCf1omA4ePCgHA6HoqOj3V0KXCAkJEQzZ850dxkAkKfWr18vh8Oh06dPu7sUt4iMjLS2C92BbQfXIdwXEP369ZPD4bBuZcuWVceOHbVjx45sTyOr4JaT8If8c/Vrnn7r2LGjJGnGjBk6evSooqOjtWfPnizbjh49qk6dOmVrns8++6zWrVuXNwuE68rqNd+3b1+up1nYN2gKkyvXHy8vL9WoUUMvvviiLl26lOVjtm7dqkGDBuVZTZGRkVZNnp6eKl26tJo3b64XX3xRiYmJWdaf2Xse8saVz3vRokVVtWpVjR07VsnJye4uDbmU2f/RlbeIiAh3l4gCrF+/furatatTW5UqVXT06FHVr18/3+s5fPiwOnfurGLFiqlChQoaM2bMNT8XJemVV15RixYtVKxYsSy/FMnOdNevX6/bb79d3t7eqlGjhiIjI294eYrc8BRw0+jYsaPmz58v6fIe2hdeeEH33XefDh8+7ObKLrt48aKKFi3q7jIKlCtf83Tplw7Zv3+/mjRpopo1a1p9mbVVrFgx2/MrUaKESpQocYNV40Zk9pqXL1/eTdXknwsXLsjLy8vdZdhe+vqTkpKiVatWaejQoSpatKgmTJjgNC79+c6PdcvPz0+7d++WMUanT5/Wpk2bNGXKFM2fP19RUVEKDAzMUP+VuFxS3kt/3i9evKjt27erb9++cjgceu2119xdGnLh6NGj1t9LlizRxIkTtXv3bquNz3nkxo1s53t6euZoe9RVUlNT1blzZ1WsWFGbNm3S0aNH1adPHxUtWlSvvvpqlo+7cOGCHn74YYWGhurf//53rqZ74MABde7cWU8++aQWLVqkdevWaeDAgapUqZLCwsJyv1AGBULfvn1Nly5dnNo2btxoJJljx44ZY4wZO3asqVmzpvH19TVVq1Y1L7zwgrlw4YIxxpj58+cbSU63+fPnm+DgYKe24OBga/qff/65ady4sfH29jZVq1Y1ERER5uLFi1a/JDN79mxz//33m2LFipmJEyea6tWrm2nTpjnV+fPPPxtJZu/evXnz5BRQmb3m6a5+3fr27ZtpmzGXX6fPPvvMeuyRI0dMz549TenSpU2xYsVMkyZNzA8//GCMMWbSpEnmtttuc5rXvHnzTO3atY23t7epVauWmTVrltV34MABI8ksW7bM3H333cbX19c0bNjQbNq0yWka//3vf02bNm2Mr6+vKVWqlOnQoYP566+/zIIFC0yZMmVMcnKy0/guXbqYXr165e6Js7GsXvPp06eb+vXrm2LFipmgoCAzZMgQc+bMGav/4MGD5r777jOlSpUyxYoVM3Xr1jUrV660Xp/M1otrSU1NNa+99pqpXr268fLyMlWqVDEvv/yyMSZ7r/mJEydMz549TWBgoPH19TX169c3ixcvdppHmzZtzNChQ80zzzxjypYta+6++25jjDHLly83NWrUMN7e3ubuu+82kZGRRpI5deqU9diNGzeau+66y/j4+JigoCAzfPhwc/bs2Rw80wVTZutP+/btzZ133mn1vfzyy6ZSpUomJCTEGHP5vWTGjBnW+FOnTplBgwaZChUqGG9vb1OvXj3z5ZdfWv05fe7nz59v/P39M7QnJCSYcuXKmfDw8GvWj7yX2fP+0EMPmcaNGxtjsv//PHz4cDNmzBhTunRpExAQYCZNmuQ0ZteuXaZly5bG29vb1KlTx6xduzbD59Phw4fNww8/bPz9/U3p0qXNAw88YA4cOJAHS114XP0/mJqaaiZPnmwqV65svLy8zG233Wa++uorqz/9Pf6jjz4yoaGh1vvA+vXrszW/v/76yzz22GOmXLlyxsfHx9SoUcO8//77OZp2TEyM6dixoylevLipUKGC6dWrlzl+/LjTMrz66qsmJCTE+Pj4mIYNG5qlS5c6TWPlypWmZs2axsfHx9x9993WdvCVnyX57auvvjItW7Y0/v7+pkyZMqZz585m3759Vv+1ts+MMeaLL74wd9xxh/H29jZly5Y1Xbt2tfqSk5PN6NGjTWBgoClWrJhp1qyZ+e6776z+zN6Lc7qdP2nSJHPp0iXz+OOPW8/9rbfeambOnGk9ZtKkSRm2O7777jvrtf/555+tsevXrzdNmzY1Xl5epmLFimbcuHFO88/O+8r1rFq1ynh4eJj4+Hirbc6cOcbPz8+kpKRc9/FZfYZlZ7pjx4419erVc3pcjx49TFhYWI6W4WqE+wLi6g/fM2fOmMGDB5saNWqY1NRUY4wxL730komKijIHDhwwX3zxhQkICDCvvfaaMcaYv//+24wePdrUq1fPHD161Bw9etT8/fff5tixY1bQP3r0qPVFwYYNG4yfn5+JjIw0+/fvN19//bUJCQkxERERVg2STIUKFcz7779v9u/fbw4dOmReeeUVU7duXafan376adO6des8foYKnmtt6B47dsx07NjRPPLII+bo0aPm9OnTmbYZ4xzuz5w5Y6pVq2ZatWplNm7caPbu3WuWLFliBbOrw/3ChQtNpUqVzLJly8zvv/9uli1bZsqUKWMiIyONMf/7oK5du7ZZsWKF2b17t+nevbsJDg623qB//vln4+3tbYYMGWKio6PNr7/+at5++21z/Phx8/fffxt/f3/zySefWPNMSEgwRYoUMd9++62Ln9GbX1av+YwZM8y3335rDhw4YNatW2dq1aplhgwZYvV37tzZtG/f3uzYscPs37/ffPnll+b77783ly5dMsuWLTOSzO7du53Wi2sZO3asKV26tImMjDT79u0zGzduNPPmzTPGZO81j4uLM9OmTTM///yz2b9/v3nrrbeMp6en2bJlizWPNm3amBIlSpgxY8aY2NhYExsba37//XdTtGhR8+yzz5rY2Fjz0UcfmcqVKzttkO3bt88UL17czJgxw+zZs8dERUWZxo0bm379+t3AM18wZLb+PPDAA+b22283ffv2NSVKlDC9e/c2v/76q/n111+NMc7hPjU11dx5552mXr165uuvv7bWpVWrVhljcvfcZ7VhZIwxzzzzjClZsqS5dOlSlvUj7139vMfExJiKFSua5s2bG2Oy///s5+dnIiIizJ49e8yCBQuMw+EwX3/9tTHGmEuXLplatWqZ9u3bm+joaLNx40bTrFkzp8+nCxcumDp16pjHH3/c7Nixw/z222/mscceM7Vq1crWRjgyd/X/4BtvvGH8/PzMRx99ZGJjY83YsWNN0aJFzZ49e4wx/3uPDwoKMp9++qn57bffzMCBA03JkiXNiRMnrju/oUOHmkaNGpmtW7eaAwcOmLVr15ovvvgi29M+deqUKV++vJkwYYLZtWuX+emnn0z79u1N27ZtrXm8/PLLpnbt2mb16tVm//79Zv78+cbb29v6kuDw4cPG29vbjBo1ysTGxpqFCxeagIAAt4f7Tz/91Cxbtszs3bvX/Pzzz+b+++83DRo0MKmpqdfdPluxYoXx9PQ0EydONL/99puJjo42r776qjXtgQMHmhYtWpgNGzaYffv2mWnTphlvb2/rdb16Pcjtdv6FCxfMxIkTzdatW83vv/9uFi5caIoVK2aWLFlijLm8nfnII4+Yjh07WnkjJSUlQ7iPi4szxYoVM0899ZTZtWuX+eyzz0y5cuWcwvv13ley4//9v/+XYafV77//biSZn3766bqPz+ozLDvTbdWqlXnmmWecxrz//vvGz88v2/VnhnBfQPTt29d4enqa4sWLm+LFixtJplKlSmb79u1ZPmbatGmmSZMm1v3M9soak3HPrjHG3HPPPU5vGsYY8+GHH5pKlSo5PW7EiBFOY/744w+nD/0LFy6YcuXKWWEQ2Xf1a55+e+WVV4wxl/duX70XNrO2K1/ff/3rX6ZkyZLm5MmTmc7z6nWkevXqGfbQvPTSSyY0NNQY878P6vfee8/q37lzp5Fkdu3aZYwx5tFHHzUtW7bMcjmHDBliOnXqZN2fPn26qVatmklLS8vyMQVVZq959+7dM4xbunSpKVu2rHW/QYMGTh/IV/ruu+9ytEGTlJRkvL29rTB/tey85pnp3LmzGT16tHW/TZs21p7BdOPGjTP169d3anv++eed6h8wYIAZNGiQ05iNGzcaDw8Pc/78+WwtY0F1ZUhLS0sza9euNd7e3ubZZ581ffv2NQEBARlC0pXhfs2aNcbDw8Ps3r070+nn5rm/VrifM2eOkWQSEhKs+q/1noe8ceXz7u3tbSQZDw8P8+mnn2b5mMz+n++66y6nMU2bNjXjxo0zxlzeY1mkSBFz9OhRq//qPfcffvihqVWrltN7f0pKivH19TVr1qxxxaIWSlf/DwYGBmb4n2ratKl56qmnjDH/e4//5z//afVfvHjRBAUFWTuMruX+++83/fv3z7QvO9N+6aWXTIcOHZwed+TIEetL6uTkZFOsWLEMRwgOGDDAPProo8YYYyZMmJBhR9O4cePcHu6vdvz4cSPJxMTEXHf7LDQ01OlIpysdOnTIeHp6mj/++MOp/Z577jETJkwwxmRcD3K7nZ+ZoUOHmm7duln3M/ui9upw/9xzz2X4f581a5YpUaKEtdPyeu8r2fHEE09kWJ/OnTtnJFlfXF9LVp9h2ZluzZo1MzzHK1euNJLM33//ne1luBq/uS9A2rZtqzlz5kiSTp06pdmzZ6tTp0768ccfFRwcrCVLluitt97S/v37dfbsWV26dEl+fn65mtcvv/yiqKgovfLKK1ZbamqqkpOT9ffff6tYsWKSpDvuuMPpcYGBgercubPef/99NWvWTF9++aVSUlL08MMP53KpC7crX/N0ZcqUyfX0oqOj1bhx42xN49y5c9q/f78GDBigJ554wmq/dOmS/P39ncY2bNjQ+rtSpUqSpGPHjql27dqKjo6+5uv/xBNPqGnTpvrjjz9UuXJlRUZGWid4Koyufs2LFy+ub775RlOmTFFsbKySkpJ06dIlp//Fp59+WkOGDNHXX3+tdu3aqVu3bk6vSU7s2rVLKSkpuueee6457lqveWpqql599VV98skn+uOPP3ThwgWlpKRY7xvpmjRp4nR/9+7datq0qVNbs2bNnO7/8ssv2rFjhxYtWmS1GWOUlpamAwcOqE6dOtlf2AJoxYoVKlGihC5evKi0tDQ99thjioiI0NChQ9WgQYNrntcgOjpaQUFBuvXWWzPtd/Vzb4yRJKf/dVe/5yF70p/3c+fOacaMGSpSpIi6desmSdn+f776PadSpUo6duyYpMv/21WqVHH6zW1m/9v79u1TyZIlndqTk5O1f/9+ly1rYZaUlKQ///xTLVu2dGpv2bKlfvnlF6e20NBQ6+8iRYrojjvu0K5du647jyFDhqhbt2766aef1KFDB3Xt2lUtWrTI9rR/+eUXfffdd5meF2D//v26ePGi/v77b7Vv396p78KFC2rcuLGky59jzZs3z3Ke7rJ3715NnDhRW7Zs0YkTJ5SWlibp8onZrrd9Fh0d7bQtdqWYmBilpqZmeO9OSUlR2bJlM31MbrfzJWnWrFl6//33dfjwYZ0/f14XLlzI8ZWWdu3apdDQUKf3/5YtW+rs2bOKi4vTLbfcIuna7yuFFeG+AClevLhq1Khh3X/vvffk7++vefPmqXPnzgoPD9fkyZMVFhYmf39/ffzxx5o+fXqu5nX27FlNnjxZDz30UIY+Hx8fp5quNnDgQPXu3VszZszQ/Pnz1aNHjwwbAcieq1/zG+Xr65vtsWfPnpUkzZs3L8OHpKenp9P9K0+wkv5Gnf6hdb15Nm7cWLfddps++OADdejQQTt37tTKlSuzXWdBc/VrfvDgQd13330aMmSIXnnlFZUpU0b//e9/NWDAAF24cEHFihXTwIEDFRYWppUrV+rrr7/WlClTNH36dA0fPjzH88/uOnKt13zatGl68803NXPmTDVo0EDFixfXiBEjdOHChQzLmlNnz57V4MGD9fTTT2foS98YKMzSQ5qXl5cCAwNVpMj/NgOu93xf77V39XO/a9cu+fn5OW18uvo9D9lz5fP+/vvv67bbbtO///1vDRgwINv/z1efaMvhcFjvCdlx9uxZNWnSxOnLo3SF4aSiBUWnTp106NAhrVq1SmvXrtU999yjoUOH6vXXX8/W48+ePav7778/05M5VqpUSb/++qskaeXKlapcubJT/81+8s37779fwcHBmjdvngIDA5WWlqb69evrwoUL133/vVb/2bNn5enpqe3bt2fYPsvq5Im53c7/+OOP9eyzz2r69OkKDQ1VyZIlNW3aNG3ZsuWa9efWjb6vVKxYUT/++KNTW0JCgtWXW9mZbsWKFa22K8f4+fnlaHv8aoT7AszhcMjDw0Pnz5/Xpk2bFBwcrOeff97qP3TokNN4Ly8vpaamZphO0aJFM7Tffvvt2r17d642su69914VL15cc+bM0erVq7Vhw4YcTwN5o2HDhnrvvff0119/XXdvWEBAgAIDA/X7778rPDz8hua5bt06TZ48OcsxAwcO1MyZM/XHH3+oXbt2qlKlSq7nV9Bs375daWlpmj59ujw8Ll/d9JNPPskwrkqVKnryySf15JNPasKECZo3b56GDx9u7anN7H8/MzVr1pSvr691VtfciIqKUpcuXdSrVy9Jl0P/nj17VLdu3Ws+rlatWlq1apVT29atW53u33777frtt98IgFm4kXDcsGFDxcXFac+ePZnuvXflc3/s2DEtXrxYXbt2tdZr3Bw8PDz03HPPadSoUXrsscdy/f98pVq1aunIkSNKSEhQQECApMz/t5csWaIKFSrk+qhDXJufn58CAwMVFRWlNm3aWO1RUVEZjqT44Ycf1Lp1a0mXj9jbvn27hg0blq35lC9fXn379lXfvn3VqlUrjRkzxincX2vat99+u5YtW6aQkBCnLyfT1a1bV97e3jp8+LDTMlypTp06+uKLLzIsjzudPHlSu3fv1rx589SqVStJ0n//+1+r/3rbZ+nbUv3798/Q17hxY6WmpurYsWPWtK8nt9v5UVFRatGihZ566imr7eoja7LKG1eqU6eOli1bJmOMtYMgKipKJUuWVFBQUI5qupbQ0FC98sorOnbsmCpUqCBJWrt2rfz8/HL0Hpab6YaGhmbYplm7du0NH0XCJ2YBkpKSovj4eMXHx2vXrl0aPny49Q1nzZo1dfjwYX388cfav3+/3nrrLX322WdOjw8JCdGBAwcUHR2tEydOKCUlxWpft26d4uPjderUKUnSxIkT9cEHH2jy5MnauXOndu3apY8//lgvvPDCdev09PRUv379NGHCBNWsWfOmOBTKrq58zdNvJ06cyPX0Hn30UVWsWFFdu3ZVVFSUfv/9dy1btkybN2/OdPzkyZM1ZcoUvfXWW9qzZ49iYmI0f/58vfHGG9me54QJE7R161Y99dRT2rFjh2JjYzVnzhyn5XjssccUFxenefPm6fHHH8/18hVENWrU0MWLF/X222/r999/14cffqi5c+c6jRkxYoTWrFmjAwcO6KefftJ3331nHSIdHBwsh8OhFStW6Pjx49YRGVnx8fHRuHHjNHbsWH3wwQfav3+/fvjhh0wvBZOVmjVrau3atdq0aZN27dqlwYMHZ/j2OjODBw9WbGysxo0bpz179uiTTz6xrgmb/uE/btw4bdq0ScOGDVN0dLT27t2r5cuXZ3ujE1lr06aNWrdurW7dumnt2rU6cOCAvvrqK61evVpS9p77CRMmqE+fPk7TNcYoPj5eR48e1a5du/T++++rRYsW8vf31z//+U+nsdd7z+vTp0+Gy/rB9R5++GF5enpq1qxZuf5/vlL79u1VvXp19e3bVzt27FBUVJS1PZH+vx0eHq5y5cqpS5cu2rhxow4cOKD169fr6aefVlxcnMuXsbAaM2aMXnvtNS1ZskS7d+/W+PHjFR0drWeeecZp3KxZs/TZZ58pNjZWQ4cO1alTp7L1+Txx4kQtX75c+/bt086dO7VixYoMP9m51rSHDh2qv/76S48++qi2bt2q/fv3a82aNerfv79SU1NVsmRJPfvssxo5cqQWLFig/fv366efftLbb7+tBQsWSJKefPJJ7d27V2PGjNHu3bu1ePFil1xf/EaULl1aZcuW1bvvvqt9+/bp22+/1ahRo6z+622fTZo0SR999JEmTZqkXbt2KSYmxjq64dZbb1V4eLj69Omj//znPzpw4IB+/PFHTZkyJcsjIXO7nV+zZk1t27ZNa9as0Z49e/T//t//y/BFXUhIiHbs2KHdu3frxIkTunjxYobpPPXUUzpy5IiGDx+u2NhYLV++XJMmTdKoUaNy9IXv1Z8Jn332mWrXrm3d79Chg+rWravevXvrl19+0Zo1a/TCCy9o6NCh1pEeP/74o2rXrq0//vjDelz6TyUOHz6s1NRURUdHKzo62tqGys50n3zySf3+++8aO3asYmNjNXv2bH3yyScaOXKkNZ933nnnuj+DzCDXv9bHTaVv375Ol5UoWbKkadq0qdMJb8aMGWPKli1rSpQoYXr06GFmzJjhdBKI5ORk061bN1OqVCnrDPnGXL60Ro0aNUyRIkWcLoW3evVq06JFC+Pr62v8/PxMs2bNzLvvvmv1K5MT8aXbv3+/kWSmTp3qyqehULn6NU+/1apVyxiTuxPqGXP5smndunUzfn5+plixYuaOO+6wToCY2UkXFy1aZBo1amS8vLxM6dKlTevWrc1//vMfY0zGE6QYc/lMt/q/S5+kW79+vWnRooXx9vY2pUqVMmFhYRlOatO7d+9ML4tXmGR1tvA33njDVKpUyfj6+pqwsDDzwQcfOJ0YaNiwYaZ69erG29vblC9f3vTu3dvprMYvvviiqVixonE4HNm+FN7LL79sgoODTdGiRc0tt9xinRQmO6/5yZMnTZcuXUyJEiVMhQoVzAsvvGD69OnjtGxt2rTJcBZZYzJeCi/9pGtXnrDtxx9/NO3btzclSpQwxYsXNw0bNuSka+baZ5vPqu/qS+GdPHnS9O/f35QtW9b4+PiY+vXrmxUrVlj913vu+/bta9q0aWPdv/IyrA6Hw/j7+5tmzZqZF1980SQmJmao8VrvecZcXm+ysw4j+7JaN6ZMmWLKly9v4uLicvX/fPXnUfql8Ly8vEzt2rXNl19+aSSZ1atXW2OOHj1q+vTpY8qVK2e8vb1NtWrVzBNPPJFhXUH2ZXYpvIiICFO5cmVTtGjRLC+Ft3jxYtOsWTPj5eVl6tatm+0r2Lz00kumTp06xtfX15QpU8Z06dLF/P777zma9p49e8yDDz5oSpUqZXx9fU3t2rXNiBEjrJOvpaWlmZkzZ5patWqZokWLmvLly5uwsDDz/fffW9P48ssvrc+SVq1amffff9/tJ9Rbu3atqVOnjvH29jYNGzY069evd9pGu9b2mTHGLFu2zNoeK1eunHnooYesvvSz2IeEhJiiRYuaSpUqmQcffNDs2LHDGJP5ieFys52fnJxs+vXrZ/z9/U2pUqXMkCFDzPjx4522HY8dO2Z9TqRvG+T2UnjXe1+5+jMh/TPnSgcPHjSdOnUyvr6+ply5cmb06NFO80k/8fCVl93M6vPoym3b6003fdrpr1m1atWs7JVu0qRJTtkrOxzG/N8Za4B8tHHjRt1zzz06cuSIdQgecC333HOP6tWrp7feesvdpeAm8sorr2ju3Lk6cuSIu0sB4EJRUVG66667tG/fPlWvXt3d5eD/HDx4UFWrVtXPP/+c45OkuXPaQGHBb+6Rr1JSUnT8+HFFRETo4YcfJtjjuk6dOqX169dr/fr1mj17trvLgZvNnj1bTZs2VdmyZRUVFaVp06ZxyD1QAHz22WcqUaKEatasqX379umZZ55Ry5YtCfYAkAP85h756qOPPlJwcLBOnz6tqVOnursc2EDjxo3Vr18/vfbaa6pVq5a7yynwDh8+rBIlSmR5O3z4sFvr27t3r7p06aK6devqpZde0ujRoxUREeHWmgDcuDNnzmjo0KGqXbu2+vXrp6ZNm2r58uXuLgs59OSTT2b5+fHkk0+6uzygwOOwfACA5dKlSzp48GCW/VmdoRgAgGPHjikpKSnTPj8/P+vM4QDyBuEeAAAAAACb47B8AAAAAABsjnAPAAAAAIDNEe4BAAAAALA5wj0AAAAAADZHuAcAAAAAwOYI9wAAAAAA2BzhHgAAAAAAmyPcAwAAAABgc/8fTC6pzmF76+QAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 3.1"
      ],
      "metadata": {
        "id": "sy2Wl9nLBgng"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "NxCI-Fv_m6Vj",
        "outputId": "c142c392-489e-4c10-b187-59042d952753"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "df=pd.read_csv('/content/EV_cars.csv')\n",
        "\n",
        "df = df.sort_values(by='Battery', ascending=False)\n",
        "\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize=(12, 8))\n",
        "\n",
        "\n",
        "ax1.bar(df['Car_name'], df['Battery'], color='b', alpha=0.7, label='Battery Capacity (kWh)')\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.plot(df['Car_name'], df['Range'], color='r', marker='o', label='Range (km)')\n",
        "\n",
        "\n",
        "ax1.set_xlabel('Car Name')\n",
        "ax1.set_ylabel('Battery Capacity (kWh)', color='b')\n",
        "ax2.set_ylabel('Range (km)', color='r')\n",
        "plt.title('Battery Capacity and Range of Electric Cars')\n",
        "\n",
        "\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "\n",
        "\n",
        "ax1.legend(loc='upper left')\n",
        "ax2.legend(loc='upper right')\n",
        "\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 3.2"
      ],
      "metadata": {
        "id": "Hn08_ZM4tFAc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "contingency_table = pd.crosstab(df['Efficiency'], df['Fast_charge'])\n",
        "\n",
        "\n",
        "print(contingency_table)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uD3gniP0tN2d",
        "outputId": "43550c3f-17f7-4c4b-834d-481fdbe757be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fast_charge  170.0   180.0   200.0   210.0   230.0   240.0   250.0   260.0   \\\n",
            "Efficiency                                                                    \n",
            "137               0       0       0       0       0       0       0       0   \n",
            "148               0       0       0       0       0       0       0       0   \n",
            "149               0       0       0       0       0       0       0       0   \n",
            "152               0       1       0       0       0       0       0       0   \n",
            "153               0       0       0       0       0       0       0       0   \n",
            "...             ...     ...     ...     ...     ...     ...     ...     ...   \n",
            "262               0       0       0       0       0       0       0       0   \n",
            "286               0       0       2       0       0       0       0       0   \n",
            "290               0       0       0       0       0       0       0       0   \n",
            "293               0       0       0       0       0       0       0       0   \n",
            "295               0       0       0       0       0       0       0       0   \n",
            "\n",
            "Fast_charge  280.0   290.0   ...  1050.0  1060.0  1070.0  1080.0  1090.0  \\\n",
            "Efficiency                   ...                                           \n",
            "137               0       0  ...       0       0       0       0       0   \n",
            "148               0       0  ...       0       0       0       0       0   \n",
            "149               0       0  ...       0       0       0       0       0   \n",
            "152               0       0  ...       0       0       0       0       0   \n",
            "153               0       0  ...       0       0       0       0       0   \n",
            "...             ...     ...  ...     ...     ...     ...     ...     ...   \n",
            "262               9       0  ...       0       0       0       0       0   \n",
            "286               0       0  ...       0       0       0       0       0   \n",
            "290               0       0  ...       0       0       0       0       0   \n",
            "293               0       0  ...       0       0       0       0       0   \n",
            "295               0       0  ...       0       0       0       0       0   \n",
            "\n",
            "Fast_charge  1110.0  1150.0  1180.0  1190.0  1290.0  \n",
            "Efficiency                                           \n",
            "137               0       0       0       0       0  \n",
            "148               0       0       0       0       0  \n",
            "149               0       0       0       0       1  \n",
            "152               0       0       0       0       0  \n",
            "153               0       0       0       0       0  \n",
            "...             ...     ...     ...     ...     ...  \n",
            "262               0       0       0       0       0  \n",
            "286               0       0       0       0       0  \n",
            "290               0       0       0       0       0  \n",
            "293               0       0       0       0       0  \n",
            "295               0       0       0       0       0  \n",
            "\n",
            "[92 rows x 86 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 3.3"
      ],
      "metadata": {
        "id": "WiWLBAT1wqN6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "\n",
        "ax1.hist(df['Battery'], bins=20, color='b', alpha=0.7, label='Battery Capacity (kWh)')\n",
        "\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.hist(df['Range'], bins=20, color='r', alpha=0.7, label='Range (km)')\n",
        "\n",
        "\n",
        "ax1.set_xlabel('Battery Capacity (kWh)', color='b')\n",
        "ax1.set_ylabel('Frequency', color='b')\n",
        "ax2.set_ylabel('Frequency', color='r')\n",
        "plt.title('Histograms of Battery Capacity and Range')\n",
        "\n",
        "\n",
        "ax1.legend(loc='upper left')\n",
        "ax2.legend(loc='upper right')\n",
        "\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "FrjRJlOww5Qf",
        "outputId": "fd11723e-5b70-4391-e793-a513a4e24cb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 3.4"
      ],
      "metadata": {
        "id": "9CET6N4Cxc6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "bin_edges = [0, 50, 100, 150, 200, 250]\n",
        "\n",
        "\n",
        "bin_labels = ['0-50', '51-100', '101-150', '151-200', '201-250']\n",
        "\n",
        "\n",
        "df['Battery_bin'] = pd.cut(df['Battery'], bins=bin_edges, labels=bin_labels, include_lowest=True)\n",
        "\n",
        "\n",
        "print(df[['Car_name', 'Battery', 'Battery_bin']])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqfdzcyDxnC2",
        "outputId": "2b3d5ffc-4900-4c90-a605-2a6ba202a542"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                             Car_name  Battery Battery_bin\n",
            "154       VinFast VF 9 Extended Range    123.0     101-150\n",
            "67          Lucid Air Dream Edition R    118.0     101-150\n",
            "147         Lucid Air Dream Edition P    118.0     101-150\n",
            "131           Lucid Air Grand Touring    112.0     101-150\n",
            "75                       Lotus Eletre    109.0     101-150\n",
            "..                                ...      ...         ...\n",
            "122  Dacia Spring Electric 65 Extreme     25.0        0-50\n",
            "293              Fiat 500e 3+1 24 kWh     21.3        0-50\n",
            "49            Renault Twingo Electric     21.3        0-50\n",
            "331           Fiat 500e Cabrio 24 kWh     21.3        0-50\n",
            "217        Fiat 500e Hatchback 24 kWh     21.3        0-50\n",
            "\n",
            "[360 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 4.1"
      ],
      "metadata": {
        "id": "roXJRv8EyDit"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = df[['Battery']]\n",
        "y = df['Range']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "print(\"X_train shape:\", X_train.shape)\n",
        "print(\"X_test shape:\", X_test.shape)\n",
        "print(\"y_train shape:\", y_train.shape)\n",
        "print(\"y_test shape:\", y_test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "36Nl3XJwyHR0",
        "outputId": "457e3e21-35a7-48e7-d71b-4c11aff4621b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (288, 1)\n",
            "X_test shape: (72, 1)\n",
            "y_train shape: (288,)\n",
            "y_test shape: (72,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 4.2"
      ],
      "metadata": {
        "id": "Mt6pCErtyjp9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "X = df[['Battery']]\n",
        "y = df['Range']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "oversampler = RandomOverSampler(random_state=42)\n",
        "X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train, y_train)\n",
        "\n",
        "print(\"X_train_resampled shape:\", X_train_resampled.shape)\n",
        "print(\"y_train_resampled shape:\", y_train_resampled.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kJKjmGTPynes",
        "outputId": "cac46f2f-2e3e-4e06-e7de-14833110cfc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train_resampled shape: (900, 1)\n",
            "y_train_resampled shape: (900,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 4.3"
      ],
      "metadata": {
        "id": "_m4Ms_N8zDxE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.tree import plot_tree\n",
        "\n",
        "\n",
        "X = df[['Battery']]\n",
        "y = df['Range']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "reg_tree = DecisionTreeRegressor(random_state=42)\n",
        "\n",
        "reg_tree.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = reg_tree.predict(X_test)\n",
        "\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f'Mean Squared Error: {mse}')\n",
        "\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "plot_tree(reg_tree, filled=True, feature_names=X.columns, rounded=True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "id": "cGBAVlTnzFzj",
        "outputId": "c1c190ed-4b14-4c0a-97e6-0bbf5f87a467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 1752.9398140995515\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 4.4"
      ],
      "metadata": {
        "id": "4yyCn8d9zr2r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import rpy2.robjects as robjects\n",
        "from rpy2.robjects import pandas2ri\n",
        "\n",
        "df=pd.read_csv('/content/EV_cars.csv')\n",
        "\n",
        "pandas2ri.activate()\n",
        "rdf = pandas2ri.py2rpy(df)\n",
        "\n",
        "\n",
        "robjects.r('install.packages(\"C50\", dependencies=TRUE)')\n",
        "robjects.r('library(C50)')\n",
        "\n",
        "\n",
        "robjects.r('model <- C5.0(formula = Range ~ Battery, data = df)')\n",
        "\n",
        "\n",
        "print(robjects.r('summary(model)'))\n",
        "\n",
        "\n",
        "robjects.r('predictions <- predict(model, df)')\n",
        "\n",
        "\n",
        "predictions = pandas2ri.ri2py_dataframe(robjects.r('predictions'))\n",
        "\n",
        "\n",
        "print(predictions)\n",
        "\n"
      ],
      "metadata": {
        "id": "nHRodd4xzyod"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 4.5"
      ],
      "metadata": {
        "id": "Cm4GVtWx1VqI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "df=pd.read_csv('/content/EV_cars.csv')\n",
        "\n",
        "X = df[['Battery']]\n",
        "y = df['Range']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "\n",
        "\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = rf_model.predict(X_test)\n",
        "\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(f'Mean Squared Error: {mse}')\n",
        "\n",
        "\n",
        "feature_importances = rf_model.feature_importances_\n",
        "feature_names = X.columns\n",
        "\n",
        "plt.bar(feature_names, feature_importances)\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Importance')\n",
        "plt.title('Feature Importances')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "toidACaJ2v2v",
        "outputId": "aefafd9f-2eca-427f-cb7f-1f5fbe0c1a64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error: 1293.94831475232\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 5.1"
      ],
      "metadata": {
        "id": "2BA8CD-k3-m-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "X = df[['Battery']]\n",
        "y = df['Range']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "rf_model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
        "\n",
        "\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = rf_model.predict(X_test)\n",
        "\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f'Mean Squared Error (MSE): {mse}')\n",
        "print(f'R-squared (R2): {r2}')\n",
        "\n",
        "\n",
        "plt.scatter(X_test, y_test, color='black', label='Actual')\n",
        "plt.scatter(X_test, y_pred, color='red', label='Predicted')\n",
        "plt.xlabel('Battery Capacity')\n",
        "plt.ylabel('Range')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "eqV0Iboa4Cfl",
        "outputId": "2ee0c7ba-17a0-405f-8d0d-e2b84d421b1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error (MSE): 1293.94831475232\n",
            "R-squared (R2): 0.8769402303911847\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 5.2"
      ],
      "metadata": {
        "id": "kTdVrheo4FFF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
        "from sklearn.preprocessing import binarize\n",
        "\n",
        "\n",
        "df=pd.read_csv('/content/EV_cars.csv')\n",
        "\n",
        "X = df[['Battery']]\n",
        "y = df['Range']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred_prob = rf_model.predict_proba(X_test)[:, 1]\n",
        "\n",
        "\n",
        "custom_threshold = 0.3\n",
        "y_pred_custom = binarize([y_pred_prob], threshold=custom_threshold)[0]\n",
        "\n",
        "\n",
        "conf_matrix = confusion_matrix(y_test, y_pred_custom)\n",
        "accuracy = accuracy_score(y_test, y_pred_custom)\n",
        "\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "print(f\"Accuracy: {accuracy:.2%}\")\n",
        "\n",
        "\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred_custom))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uvk8Vtqy4H_-",
        "outputId": "8948a8a3-1482-41a4-880b-be717e7c90f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[0 0 0 ... 0 0 0]\n",
            " [1 0 0 ... 0 0 0]\n",
            " [1 0 0 ... 0 0 0]\n",
            " ...\n",
            " [1 0 0 ... 0 0 0]\n",
            " [1 0 0 ... 0 0 0]\n",
            " [1 0 0 ... 0 0 0]]\n",
            "Accuracy: 0.00%\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.00      0.00      0.00       0.0\n",
            "       165.0       0.00      0.00      0.00       1.0\n",
            "       180.0       0.00      0.00      0.00       1.0\n",
            "       185.0       0.00      0.00      0.00       3.0\n",
            "       200.0       0.00      0.00      0.00       2.0\n",
            "       205.0       0.00      0.00      0.00       2.0\n",
            "       210.0       0.00      0.00      0.00       1.0\n",
            "       230.0       0.00      0.00      0.00       1.0\n",
            "       260.0       0.00      0.00      0.00       4.0\n",
            "       265.0       0.00      0.00      0.00       2.0\n",
            "       280.0       0.00      0.00      0.00       1.0\n",
            "       295.0       0.00      0.00      0.00       1.0\n",
            "       300.0       0.00      0.00      0.00       2.0\n",
            "       305.0       0.00      0.00      0.00       1.0\n",
            "       310.0       0.00      0.00      0.00       1.0\n",
            "       315.0       0.00      0.00      0.00       2.0\n",
            "       330.0       0.00      0.00      0.00       1.0\n",
            "       335.0       0.00      0.00      0.00       2.0\n",
            "       340.0       0.00      0.00      0.00       2.0\n",
            "       350.0       0.00      0.00      0.00       4.0\n",
            "       355.0       0.00      0.00      0.00       1.0\n",
            "       360.0       0.00      0.00      0.00       3.0\n",
            "       365.0       0.00      0.00      0.00       1.0\n",
            "       380.0       0.00      0.00      0.00       1.0\n",
            "       385.0       0.00      0.00      0.00       1.0\n",
            "       390.0       0.00      0.00      0.00       3.0\n",
            "       395.0       0.00      0.00      0.00       2.0\n",
            "       400.0       0.00      0.00      0.00       1.0\n",
            "       405.0       0.00      0.00      0.00       3.0\n",
            "       415.0       0.00      0.00      0.00       2.0\n",
            "       425.0       0.00      0.00      0.00       4.0\n",
            "       435.0       0.00      0.00      0.00       1.0\n",
            "       455.0       0.00      0.00      0.00       2.0\n",
            "       465.0       0.00      0.00      0.00       1.0\n",
            "       470.0       0.00      0.00      0.00       1.0\n",
            "       475.0       0.00      0.00      0.00       1.0\n",
            "       485.0       0.00      0.00      0.00       4.0\n",
            "       490.0       0.00      0.00      0.00       3.0\n",
            "       495.0       0.00      0.00      0.00       1.0\n",
            "       575.0       0.00      0.00      0.00       1.0\n",
            "       665.0       0.00      0.00      0.00       1.0\n",
            "\n",
            "    accuracy                           0.00      72.0\n",
            "   macro avg       0.00      0.00      0.00      72.0\n",
            "weighted avg       0.00      0.00      0.00      72.0\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 6.1"
      ],
      "metadata": {
        "id": "yGiKG3of4_DV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "\n",
        "\n",
        "X = df[['Battery']]\n",
        "y = df['Efficiency']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "nb_model = GaussianNB()\n",
        "\n",
        "\n",
        "nb_model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "y_pred = nb_model.predict(X_test)\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "print(f\"Accuracy: {accuracy:.2%}\")\n",
        "\n",
        "\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CC9RcXFd5buz",
        "outputId": "35ba0ab0-7970-44cf-cb57-81eddaac5b77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 11.11%\n",
            "\n",
            "Confusion Matrix:\n",
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 3 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 1 0]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         137       0.00      0.00      0.00         0\n",
            "         149       0.00      0.00      0.00         1\n",
            "         152       0.00      0.00      0.00         1\n",
            "         154       0.25      0.50      0.33         2\n",
            "         157       0.00      0.00      0.00         1\n",
            "         158       0.00      0.00      0.00         0\n",
            "         159       0.00      0.00      0.00         1\n",
            "         160       0.00      0.00      0.00         0\n",
            "         162       0.00      0.00      0.00         1\n",
            "         163       0.00      0.00      0.00         1\n",
            "         164       0.00      0.00      0.00         3\n",
            "         165       0.00      0.00      0.00         2\n",
            "         166       0.00      0.00      0.00         1\n",
            "         168       0.00      0.00      0.00         2\n",
            "         169       0.00      0.00      0.00         4\n",
            "         170       0.00      0.00      0.00         2\n",
            "         171       0.00      0.00      0.00         2\n",
            "         173       0.00      0.00      0.00         1\n",
            "         175       0.00      0.00      0.00         1\n",
            "         177       0.00      0.00      0.00         1\n",
            "         178       0.00      0.00      0.00         1\n",
            "         180       0.00      0.00      0.00         1\n",
            "         181       0.00      0.00      0.00         0\n",
            "         182       0.00      0.00      0.00         0\n",
            "         185       0.00      0.00      0.00         2\n",
            "         187       0.00      0.00      0.00         0\n",
            "         188       0.00      0.00      0.00         1\n",
            "         189       0.00      0.00      0.00         1\n",
            "         190       0.00      0.00      0.00         1\n",
            "         192       0.00      0.00      0.00         1\n",
            "         194       0.00      0.00      0.00         0\n",
            "         195       0.00      0.00      0.00         2\n",
            "         196       0.00      0.00      0.00         2\n",
            "         197       0.00      0.00      0.00         3\n",
            "         198       0.00      0.00      0.00         0\n",
            "         200       0.00      0.00      0.00         1\n",
            "         201       0.00      0.00      0.00         1\n",
            "         202       0.00      0.00      0.00         0\n",
            "         203       0.00      0.00      0.00         1\n",
            "         206       0.00      0.00      0.00         1\n",
            "         208       0.00      0.00      0.00         3\n",
            "         209       0.00      0.00      0.00         0\n",
            "         212       0.00      0.00      0.00         0\n",
            "         213       0.00      0.00      0.00         0\n",
            "         215       1.00      1.00      1.00         1\n",
            "         217       0.00      0.00      0.00         1\n",
            "         218       0.00      0.00      0.00         2\n",
            "         220       0.00      0.00      0.00         1\n",
            "         221       0.00      0.00      0.00         0\n",
            "         226       0.00      0.00      0.00         3\n",
            "         228       0.00      0.00      0.00         0\n",
            "         230       0.00      0.00      0.00         1\n",
            "         231       0.00      0.00      0.00         1\n",
            "         232       0.00      0.00      0.00         2\n",
            "         235       0.00      0.00      0.00         1\n",
            "         250       0.30      1.00      0.46         3\n",
            "         257       0.00      0.00      0.00         3\n",
            "         262       0.60      1.00      0.75         3\n",
            "         290       0.00      0.00      0.00         0\n",
            "         295       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.11        72\n",
            "   macro avg       0.04      0.06      0.04        72\n",
            "weighted avg       0.06      0.11      0.07        72\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 7.1"
      ],
      "metadata": {
        "id": "HnlvAgCt54ib"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "X = df[['Battery']]\n",
        "y = df['Top_speed']\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "\n",
        "mlp_model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000, random_state=42)\n",
        "\n",
        "\n",
        "mlp_model.fit(X_train_scaled, y_train)\n",
        "\n",
        "\n",
        "y_pred = mlp_model.predict(X_test_scaled)\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "print(f\"Accuracy: {accuracy:.2%}\")\n",
        "\n",
        "\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h32AiuvV5-Ti",
        "outputId": "c0038b3f-2637-4ef9-9efb-a8fa8fedc9b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 34.72%\n",
            "\n",
            "Confusion Matrix:\n",
            "[[0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 5 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 5 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 1 5 0 0 3 0 1 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 1 3 0 0 6 0 0 0 1 0 0 0 0 0 0]\n",
            " [0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 3 0 3 0 2 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0]\n",
            " [0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 2 0 1 0 2 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         125       0.00      0.00      0.00         1\n",
            "         130       1.00      0.56      0.71         9\n",
            "         135       0.00      0.00      0.00         6\n",
            "         150       0.28      0.83      0.42         6\n",
            "         160       0.38      0.50      0.43        10\n",
            "         167       0.00      0.00      0.00         1\n",
            "         170       0.00      0.00      0.00         2\n",
            "         180       0.30      0.55      0.39        11\n",
            "         185       0.00      0.00      0.00         3\n",
            "         200       0.38      0.38      0.38         8\n",
            "         205       0.00      0.00      0.00         1\n",
            "         210       0.14      0.33      0.20         3\n",
            "         217       0.00      0.00      0.00         2\n",
            "         220       0.00      0.00      0.00         1\n",
            "         230       0.00      0.00      0.00         1\n",
            "         250       0.00      0.00      0.00         5\n",
            "         260       0.00      0.00      0.00         1\n",
            "         270       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.35        72\n",
            "   macro avg       0.14      0.17      0.14        72\n",
            "weighted avg       0.30      0.35      0.29        72\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:686: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 8.1"
      ],
      "metadata": {
        "id": "rKdN3Fqg6hsS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Specify the features for clustering\n",
        "# For example, let's use 'Battery' and 'Range' as features\n",
        "X = df[['Battery', 'Range']]\n",
        "\n",
        "# Standardize the features (important for k-Means)\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Initialize the KMeans model with the desired number of clusters\n",
        "n_clusters = 3  # You can adjust this based on your problem\n",
        "kmeans_model = KMeans(n_clusters=n_clusters, random_state=42)\n",
        "\n",
        "# Fit the KMeans model on the scaled data\n",
        "kmeans_model.fit(X_scaled)\n",
        "\n",
        "# Assign cluster labels to the original DataFrame\n",
        "df['Cluster'] = kmeans_model.labels_\n",
        "\n",
        "# Visualize the clusters\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "for cluster in range(n_clusters):\n",
        "    cluster_data = df[df['Cluster'] == cluster]\n",
        "    plt.scatter(cluster_data['Battery'], cluster_data['Range'], label=f'Cluster {cluster + 1}')\n",
        "\n",
        "plt.scatter(kmeans_model.cluster_centers_[:, 0], kmeans_model.cluster_centers_[:, 1], marker='x', s=200, color='red', label='Centroids')\n",
        "plt.xlabel('Battery')\n",
        "plt.ylabel('Range')\n",
        "plt.title('k-Means Clustering')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "TSfzFa4g6kpq",
        "outputId": "ff48aa41-401b-4d60-81f6-56aa0cf24b7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 9.1"
      ],
      "metadata": {
        "id": "QUhU9n6P7Pnh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Specify the features (X) and the target variable (y)\n",
        "# For example, let's use 'Battery' as a feature and 'Range' as the target variable\n",
        "X = df[['Battery']]\n",
        "y = df['Range']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize the Linear Regression model\n",
        "linear_model = LinearRegression()\n",
        "\n",
        "# Fit the linear regression model on the training data\n",
        "linear_model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred = linear_model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "\n",
        "print(f'Mean Squared Error (MSE): {mse}')\n",
        "print(f'R-squared (R2): {r2}')\n",
        "\n",
        "# Visualize predictions vs actual values\n",
        "plt.scatter(X_test, y_test, color='black', label='Actual')\n",
        "plt.scatter(X_test, y_pred, color='red', label='Predicted')\n",
        "plt.xlabel('Battery Capacity')\n",
        "plt.ylabel('Range')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "kSlAKQGJ7iEe",
        "outputId": "03b0abe8-f5ec-4d8e-8bc3-4cad703c3c54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squared Error (MSE): 2914.675772999165\n",
            "R-squared (R2): 0.7228024295712854\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 9.2"
      ],
      "metadata": {
        "id": "z7oY_ssm7nRo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Specify the features (X) and the target variable (y)\n",
        "# For example, let's use 'Battery' and 'Efficiency' as features, and 'Range' as the target variable\n",
        "X = df[['Battery', 'Efficiency']]\n",
        "y = df['Range']\n",
        "\n",
        "# Add a constant term to the features (required for statsmodels)\n",
        "X = sm.add_constant(X)\n",
        "\n",
        "# Fit the initial model\n",
        "model = sm.OLS(y, X).fit()\n",
        "\n",
        "# Perform stepwise regression\n",
        "while True:\n",
        "    # Find the feature with the highest p-value\n",
        "    max_pvalue = model.pvalues[1:].max()\n",
        "\n",
        "    # If the highest p-value is above a threshold (e.g., 0.05), remove the corresponding feature\n",
        "    if max_pvalue > 0.05:\n",
        "        max_feature = model.pvalues[1:].idxmax()\n",
        "        X = X.drop(max_feature, axis=1)\n",
        "        model = sm.OLS(y, X).fit()\n",
        "    else:\n",
        "        break\n",
        "\n",
        "# Display the final model summary\n",
        "print(model.summary())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmYU_xXw7pcv",
        "outputId": "2c0be307-4056-4816-d8cc-05212a22d20e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                  Range   R-squared:                       0.976\n",
            "Model:                            OLS   Adj. R-squared:                  0.976\n",
            "Method:                 Least Squares   F-statistic:                     7390.\n",
            "Date:                Tue, 05 Dec 2023   Prob (F-statistic):          3.30e-291\n",
            "Time:                        21:44:22   Log-Likelihood:                -1519.1\n",
            "No. Observations:                 360   AIC:                             3044.\n",
            "Df Residuals:                     357   BIC:                             3056.\n",
            "Df Model:                           2                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const        306.3914      5.811     52.722      0.000     294.962     317.820\n",
            "Battery        5.0647      0.043    116.510      0.000       4.979       5.150\n",
            "Efficiency    -1.5229      0.028    -54.827      0.000      -1.578      -1.468\n",
            "==============================================================================\n",
            "Omnibus:                       14.597   Durbin-Watson:                   1.825\n",
            "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               17.568\n",
            "Skew:                           0.383   Prob(JB):                     0.000153\n",
            "Kurtosis:                       3.764   Cond. No.                     1.40e+03\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.4e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 10.1"
      ],
      "metadata": {
        "id": "deeU4RUW8RQi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Specify the features (X) and the target variable (y)\n",
        "# For example, let's use 'Battery', 'Efficiency', and 'Top_speed' as features, and 'Range' as the target variable\n",
        "X = df[['Battery', 'Efficiency', 'Top_speed']]\n",
        "y = df['Range']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Fit a linear regression model (for demonstration purposes)\n",
        "linear_model = LinearRegression()\n",
        "linear_model.fit(X_train, y_train)\n",
        "\n",
        "# Calculate VIF for each variable\n",
        "vif_data = pd.DataFrame()\n",
        "vif_data[\"Variable\"] = X.columns\n",
        "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
        "\n",
        "# Display the VIF values\n",
        "print(vif_data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pU1Zo60a8iO2",
        "outputId": "675412c8-adab-4f26-bfd2-7ce85be05fbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Variable        VIF\n",
            "0     Battery  27.513251\n",
            "1  Efficiency  14.375557\n",
            "2   Top_speed  31.393000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 10.2"
      ],
      "metadata": {
        "id": "24WaXKfA84VL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Specify the features (X)\n",
        "# For example, let's use 'Battery', 'Efficiency', and 'Top_speed' as features\n",
        "X = df[['Battery', 'Efficiency', 'Top_speed']]\n",
        "\n",
        "# Standardize the features (important for PCA)\n",
        "scaler = StandardScaler()\n",
        "X_standardized = scaler.fit_transform(X)\n",
        "\n",
        "# Initialize the PCA model with the desired number of components\n",
        "n_components = 2  # You can adjust this based on your problem\n",
        "pca_model = PCA(n_components=n_components)\n",
        "\n",
        "# Fit and transform the data using PCA\n",
        "X_pca = pca_model.fit_transform(X_standardized)\n",
        "\n",
        "# Create a DataFrame with the principal components\n",
        "df_pca = pd.DataFrame(data=X_pca, columns=[f'PC{i+1}' for i in range(n_components)])\n",
        "\n",
        "# Display the explained variance ratio\n",
        "print(\"Explained Variance Ratio:\")\n",
        "print(pca_model.explained_variance_ratio_)\n",
        "\n",
        "# Visualize the data in the reduced space\n",
        "plt.scatter(df_pca['PC1'], df_pca['PC2'])\n",
        "plt.xlabel('Principal Component 1')\n",
        "plt.ylabel('Principal Component 2')\n",
        "plt.title('PCA - Reduced Dimensionality')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "Ae-NSgr288b9",
        "outputId": "dd0a744a-b040-467e-a99a-9f178c315d09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explained Variance Ratio:\n",
            "[0.56606461 0.36013727]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 11.1"
      ],
      "metadata": {
        "id": "z0-xrFWU9NFm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Specify the features (X) and the binary target variable (y)\n",
        "# For example, let's use 'Battery' and 'Top_speed' as features, and 'Range' as the binary target variable\n",
        "X = df[['Battery', 'Top_speed']]\n",
        "y = df['Range']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Standardize the features (important for logistic regression)\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Logistic Regression model\n",
        "logistic_model = LogisticRegression(random_state=42)\n",
        "\n",
        "# Fit the logistic regression model on the scaled training data\n",
        "logistic_model.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Make predictions on the scaled testing data\n",
        "y_pred = logistic_model.predict(X_test_scaled)\n",
        "\n",
        "# Evaluate the model\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "print(f\"Accuracy: {accuracy:.2%}\")\n",
        "\n",
        "# Display confusion matrix\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Print classification report for precision, recall, and F1-score\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGZDzEMW9a_H",
        "outputId": "aca81b06-8e05-4ff6-f3fd-028c9b40f5ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 12.50%\n",
            "\n",
            "Confusion Matrix:\n",
            "[[0 0 0 ... 0 0 0]\n",
            " [1 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         135       0.00      0.00      0.00         0\n",
            "         165       0.00      0.00      0.00         1\n",
            "         180       0.00      0.00      0.00         1\n",
            "         185       0.30      1.00      0.46         3\n",
            "         200       0.00      0.00      0.00         2\n",
            "         205       0.00      0.00      0.00         2\n",
            "         210       0.00      0.00      0.00         1\n",
            "         230       0.00      0.00      0.00         1\n",
            "         260       0.38      0.75      0.50         4\n",
            "         265       0.00      0.00      0.00         2\n",
            "         280       0.00      0.00      0.00         1\n",
            "         295       0.00      0.00      0.00         1\n",
            "         300       0.00      0.00      0.00         2\n",
            "         305       0.00      0.00      0.00         1\n",
            "         310       0.00      0.00      0.00         1\n",
            "         315       0.00      0.00      0.00         2\n",
            "         330       0.00      0.00      0.00         1\n",
            "         335       0.00      0.00      0.00         2\n",
            "         340       0.00      0.00      0.00         2\n",
            "         350       0.00      0.00      0.00         4\n",
            "         355       0.00      0.00      0.00         1\n",
            "         360       0.00      0.00      0.00         3\n",
            "         365       0.00      0.00      0.00         1\n",
            "         380       0.09      1.00      0.17         1\n",
            "         385       0.00      0.00      0.00         1\n",
            "         390       0.00      0.00      0.00         3\n",
            "         395       0.00      0.00      0.00         2\n",
            "         400       0.00      0.00      0.00         1\n",
            "         405       0.00      0.00      0.00         3\n",
            "         415       0.00      0.00      0.00         2\n",
            "         420       0.00      0.00      0.00         0\n",
            "         425       0.40      0.50      0.44         4\n",
            "         435       0.00      0.00      0.00         1\n",
            "         450       0.00      0.00      0.00         0\n",
            "         455       0.00      0.00      0.00         2\n",
            "         465       0.00      0.00      0.00         1\n",
            "         470       0.00      0.00      0.00         1\n",
            "         475       0.00      0.00      0.00         1\n",
            "         485       0.00      0.00      0.00         4\n",
            "         490       0.00      0.00      0.00         3\n",
            "         495       0.00      0.00      0.00         1\n",
            "         505       0.00      0.00      0.00         0\n",
            "         520       0.00      0.00      0.00         0\n",
            "         575       0.00      0.00      0.00         1\n",
            "         665       0.00      0.00      0.00         1\n",
            "\n",
            "    accuracy                           0.12        72\n",
            "   macro avg       0.03      0.07      0.03        72\n",
            "weighted avg       0.06      0.12      0.07        72\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 11.2"
      ],
      "metadata": {
        "id": "j0NmudR89hmU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Specify the features (X) and the count target variable (y)\n",
        "# For example, let's use 'Battery' and 'Top_speed' as features, and 'Efficiency' as the count target variable\n",
        "X = df[['Battery', 'Top_speed']]\n",
        "y = df['Efficiency']\n",
        "\n",
        "# Add a constant term to the features (required for statsmodels)\n",
        "X = sm.add_constant(X)\n",
        "\n",
        "# Fit the Poisson Regression model\n",
        "poisson_model = sm.GLM(y, X, family=sm.families.Poisson()).fit()\n",
        "\n",
        "# Display the model summary\n",
        "print(poisson_model.summary())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5umPr_vM9lcO",
        "outputId": "cf88af4f-787b-4c28-f0be-ea9c3c0fda99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Generalized Linear Model Regression Results                  \n",
            "==============================================================================\n",
            "Dep. Variable:             Efficiency   No. Observations:                  360\n",
            "Model:                            GLM   Df Residuals:                      357\n",
            "Model Family:                 Poisson   Df Model:                            2\n",
            "Link Function:                    Log   Scale:                          1.0000\n",
            "Method:                          IRLS   Log-Likelihood:                -1977.8\n",
            "Date:                Tue, 05 Dec 2023   Deviance:                       1399.5\n",
            "Time:                        21:52:07   Pearson chi2:                 1.44e+03\n",
            "No. Iterations:                     4   Pseudo R-squ. (CS):             0.6630\n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const          5.4215      0.020    277.341      0.000       5.383       5.460\n",
            "Battery        0.0047      0.000     18.250      0.000       0.004       0.005\n",
            "Top_speed     -0.0027      0.000    -18.075      0.000      -0.003      -0.002\n",
            "==============================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 12.1"
      ],
      "metadata": {
        "id": "VVHIwxLr-HAj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Assuming you want to mine association rules based on the 'Battery' column\n",
        "# You need to convert it to binary indicators, for instance using one-hot encoding\n",
        "df_encoded = pd.get_dummies(df['Battery'], prefix='Battery')\n",
        "\n",
        "# Generate frequent itemsets using Apriori algorithm\n",
        "frequent_itemsets = apriori(df_encoded, min_support=0.05, use_colnames=True)\n",
        "\n",
        "# Generate association rules\n",
        "rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.7)\n",
        "\n",
        "# Display the association rules\n",
        "print(rules)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHUXXT7B_qnr",
        "outputId": "b12a7ff7-3000-443b-939a-617f5adbb5c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 12.2"
      ],
      "metadata": {
        "id": "wsMugJmFAZYQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Generate frequent itemsets using Apriori algorithm\n",
        "frequent_itemsets = apriori(df_encoded, min_support=0.05, use_colnames=True)\n",
        "\n",
        "# Generate association rules\n",
        "rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.7)\n",
        "\n",
        "# Apply Confidence Difference Criterion\n",
        "confidence_threshold = 0.7\n",
        "confidence_difference_threshold = 0.1\n",
        "\n",
        "filtered_rules = rules[\n",
        "    (rules['confidence'] >= confidence_threshold) &\n",
        "    (rules['confidence'] - rules['antecedent support'] >= confidence_difference_threshold)\n",
        "]\n",
        "\n",
        "# Display the association rules satisfying the Confidence Difference Criterion\n",
        "print(filtered_rules)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnpHFOUkAbVW",
        "outputId": "81d83f46-106e-46db-802f-dcfb05baa0ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric]\n",
            "Index: []\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Question 12.3"
      ],
      "metadata": {
        "id": "kEwwYyAfAwZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from mlxtend.frequent_patterns import apriori, association_rules\n",
        "\n",
        "# Assuming df is your DataFrame\n",
        "# If it's not already a DataFrame, you can convert it using pd.DataFrame(your_data)\n",
        "# For example: df = pd.DataFrame(your_data)\n",
        "\n",
        "# Generate frequent itemsets using Apriori algorithm\n",
        "frequent_itemsets = apriori(df_encoded, min_support=0.05, use_colnames=True)\n",
        "\n",
        "# Generate association rules\n",
        "rules = association_rules(frequent_itemsets, metric=\"confidence\", min_threshold=0.7)\n",
        "\n",
        "# Define a custom Confidence Quotient Criterion function\n",
        "def confidence_quotient_criterion(rule):\n",
        "    # Replace this with your specific formula for Confidence Quotient Criterion\n",
        "    # For example, if it's confidence / (antecedent support * consequent support)\n",
        "    confidence_quotient = rule['confidence'] / (rule['antecedent support'] * rule['consequent support'])\n",
        "    return confidence_quotient\n",
        "\n",
        "# Apply the Confidence Quotient Criterion\n",
        "rules['confidence_quotient'] = rules.apply(confidence_quotient_criterion, axis=1)\n",
        "\n",
        "# Set your threshold for the Confidence Quotient Criterion\n",
        "confidence_quotient_threshold = 1.0\n",
        "\n",
        "filtered_rules = rules[rules['confidence_quotient'] >= confidence_quotient_threshold]\n",
        "\n",
        "# Display the association rules satisfying the Confidence Quotient Criterion\n",
        "print(filtered_rules)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7BP-8k8-A1a8",
        "outputId": "ae6852b3-715c-47ea-d30a-4ac47d8001b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: [antecedents, consequents, antecedent support, consequent support, support, confidence, lift, leverage, conviction, zhangs_metric, confidence_quotient]\n",
            "Index: []\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n",
            "/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}