Cleaning_Data.ipynb

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        "<a href=\"https://colab.research.google.com/github/shivi13102/Vehicles_Efficiency_Rate_Prediction/blob/main/Cleaning_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 81,
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        "id": "SdeJJT9NCu1t"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.impute import SimpleImputer\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the datasets\n",
        "train = pd.read_csv(\"train.csv\")\n",
        "test = pd.read_csv(\"test.csv\")"
      ],
      "metadata": {
        "id": "-dHjmj-uFhV5"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TRAIN DATA CLEANING"
      ],
      "metadata": {
        "id": "y_TGDYZ_LpDJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(train)"
      ],
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        "outputId": "d115c677-25a9-4c8a-8668-a0ce1befe1f9"
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      "execution_count": 62,
      "outputs": [
        {
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            "text/plain": [
              "        ID  PERFORMANCE_INDEX  EMISSIONS_CO2  URBAN_CONSUMPTION  DISPLACEMENT  \\\n",
              "0        0          74.595588          149.0                8.3           2.0   \n",
              "1        1          62.338357          262.0               22.5           3.6   \n",
              "2        2          72.105592          204.0               11.7           2.8   \n",
              "3        3          86.706104          301.0               23.4           5.7   \n",
              "4        4         104.793985          375.0               20.5           2.2   \n",
              "...    ...                ...            ...                ...           ...   \n",
              "4995  4995         113.241839          311.0               16.1           5.1   \n",
              "4996  4996         103.952918          286.0               18.5           3.5   \n",
              "4997  4997          64.741667          225.0               10.5           1.4   \n",
              "4998  4998          78.366368          233.0                9.8           3.1   \n",
              "4999  4999          63.077954          264.0                9.2           3.3   \n",
              "\n",
              "      CAPACITY  PISTON_COUNT  BLENDED_CONSUMPTION GEARBOX_TYPE  OUTPUT  \\\n",
              "0          5.0           4.0                  7.9       Manual    87.0   \n",
              "1         15.0           6.0                 16.5    Automatic   163.0   \n",
              "2          5.0           6.0                 10.0    Automatic   172.0   \n",
              "3         14.0           8.0                 13.9    Automatic   286.0   \n",
              "4          5.0           4.0                 11.9       Manual   118.0   \n",
              "...        ...           ...                  ...          ...     ...   \n",
              "4995       7.0           6.0                  9.6    Automatic   264.0   \n",
              "4996       7.0           8.0                 15.1    Automatic   266.0   \n",
              "4997       4.0           4.0                  6.1    Automatic    87.0   \n",
              "4998       5.0           4.0                  8.7    Automatic   115.0   \n",
              "4999       5.0           6.0                 12.2    Automatic   146.0   \n",
              "\n",
              "                   BODY_STYLE  EFFICIENCY_RATIO  YEARS_IN_SERVICE  MILEAGE  \\\n",
              "0                     COMPACT              1.60              10.0     17.0   \n",
              "1                 SUV - SMALL              1.52              10.0     19.0   \n",
              "2                    MID-SIZE              1.59              10.0     18.0   \n",
              "3     PICKUP TRUCK - STANDARD              1.42              10.0     19.0   \n",
              "4                    MID-SIZE              1.54              10.0     19.0   \n",
              "...                       ...               ...               ...      ...   \n",
              "4995                 MID-SIZE              1.31              10.0     19.0   \n",
              "4996                FULL-SIZE              1.47              10.0     17.0   \n",
              "4997                 MID-SIZE              1.33              10.0     17.0   \n",
              "4998                 MID-SIZE              1.63              10.0     16.0   \n",
              "4999                  COMPACT              1.51              10.0     17.0   \n",
              "\n",
              "      HIGHWAY_USAGE ENERGY_SOURCE  EFFICIENCY_RATING  \n",
              "0               5.6        Petrol               39.0  \n",
              "1              12.3        Petrol               23.0  \n",
              "2              10.4           Gas               30.0  \n",
              "3              13.6        Petrol               28.0  \n",
              "4               9.7        Petrol               26.0  \n",
              "...             ...           ...                ...  \n",
              "4995            7.3        Petrol               27.0  \n",
              "4996           12.9        Petrol               20.0  \n",
              "4997            6.8        Diesel               31.0  \n",
              "4998            7.0           Gas               33.0  \n",
              "4999           11.7        Petrol               12.0  \n",
              "\n",
              "[5000 rows x 17 columns]"
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              "      <th>ID</th>\n",
              "      <th>PERFORMANCE_INDEX</th>\n",
              "      <th>EMISSIONS_CO2</th>\n",
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              "      <th>ENERGY_SOURCE</th>\n",
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              "      <th>0</th>\n",
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              "      <td>74.595588</td>\n",
              "      <td>149.0</td>\n",
              "      <td>8.3</td>\n",
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              "      <td>7.9</td>\n",
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              "      <td>87.0</td>\n",
              "      <td>COMPACT</td>\n",
              "      <td>1.60</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>5.6</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>62.338357</td>\n",
              "      <td>262.0</td>\n",
              "      <td>22.5</td>\n",
              "      <td>3.6</td>\n",
              "      <td>15.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>16.5</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>163.0</td>\n",
              "      <td>SUV - SMALL</td>\n",
              "      <td>1.52</td>\n",
              "      <td>10.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>12.3</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>23.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>72.105592</td>\n",
              "      <td>204.0</td>\n",
              "      <td>11.7</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>172.0</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>1.59</td>\n",
              "      <td>10.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>Gas</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>86.706104</td>\n",
              "      <td>301.0</td>\n",
              "      <td>23.4</td>\n",
              "      <td>5.7</td>\n",
              "      <td>14.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>13.9</td>\n",
              "      <td>Automatic</td>\n",
              "      <td>286.0</td>\n",
              "      <td>PICKUP TRUCK - STANDARD</td>\n",
              "      <td>1.42</td>\n",
              "      <td>10.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>13.6</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>28.0</td>\n",
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              "      <th>4</th>\n",
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              "      <td>20.5</td>\n",
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              "      <td>5.0</td>\n",
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              "      <td>11.9</td>\n",
              "      <td>Manual</td>\n",
              "      <td>118.0</td>\n",
              "      <td>MID-SIZE</td>\n",
              "      <td>1.54</td>\n",
              "      <td>10.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>9.7</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>26.0</td>\n",
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              "      <td>4995</td>\n",
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              "      <td>16.1</td>\n",
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              "      <td>9.6</td>\n",
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              "      <th>4996</th>\n",
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              "      <td>6.1</td>\n",
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              "      <td>87.0</td>\n",
              "      <td>MID-SIZE</td>\n",
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              "      <td>COMPACT</td>\n",
              "      <td>1.51</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>11.7</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>12.0</td>\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-55c5a9d0-1241-4fca-9dad-ec99751d54e1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_1b21df1d-d0ed-4cd3-a80b-64c50249b547\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_1b21df1d-d0ed-4cd3-a80b-64c50249b547 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1443,\n        \"min\": 0,\n        \"max\": 4999,\n        \"num_unique_values\": 5000,\n        \"samples\": [\n          1501,\n          2586,\n          2653\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PERFORMANCE_INDEX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.26374074758491,\n        \"min\": 41.0,\n        \"max\": 212.0,\n        \"num_unique_values\": 4970,\n        \"samples\": [\n          62.166497,\n          92.03035562,\n          87.17687999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EMISSIONS_CO2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 67.33104705148756,\n        \"min\": 108.0,\n        \"max\": 470.0,\n        \"num_unique_values\": 338,\n        \"samples\": [\n          453.0,\n          120.0,\n          288.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"URBAN_CONSUMPTION\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.308522517088625,\n        \"min\": 4.6,\n        \"max\": 30.2,\n        \"num_unique_values\": 252,\n        \"samples\": [\n          7.2,\n          17.7,\n          23.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DISPLACEMENT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3573990015742607,\n        \"min\": 1.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          2.0,\n          2.4,\n          3.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAPACITY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.8491194222755576,\n        \"min\": 2.0,\n        \"max\": 18.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          5.0,\n          15.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PISTON_COUNT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8333729133770318,\n        \"min\": 3.0,\n        \"max\": 12.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          12.0,\n          6.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BLENDED_CONSUMPTION\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.534511523541121,\n        \"min\": 4.7,\n        \"max\": 25.8,\n        \"num_unique_values\": 211,\n        \"samples\": [\n          8.9,\n          22.0,\n          25.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GEARBOX_TYPE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Manual\",\n          \"Automatic\",\n          \"eMr\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OUTPUT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79.85478439268138,\n        \"min\": 50.0,\n        \"max\": 378.0,\n        \"num_unique_values\": 318,\n        \"samples\": [\n          115.0,\n          260.0,\n          122.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BODY_STYLE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"COMPACT\",\n          \"SUV - SMALL\",\n          \"FULL-SIZE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EFFICIENCY_RATIO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15689474604854917,\n        \"min\": 0.87,\n        \"max\": 1.77,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          1.55,\n          1.46,\n          1.07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YEARS_IN_SERVICE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 10.0,\n        \"max\": 10.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MILEAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.8593622933884073,\n        \"min\": 10.0,\n        \"max\": 30.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HIGHWAY_USAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.220253561283335,\n        \"min\": 4.9,\n        \"max\": 20.5,\n        \"num_unique_values\": 156,\n        \"samples\": [\n          11.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ENERGY_SOURCE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Gas\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EFFICIENCY_RATING\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.255603654649734,\n        \"min\": 11.0,\n        \"max\": 60.0,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training Data Info:\")\n",
        "print(train.info())\n",
        "print(\"\\nMissing Values:\\n\", train.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgcx17SbGvuL",
        "outputId": "872c7eb8-0d0d-45bb-d0aa-dd5592a6abff"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5000 entries, 0 to 4999\n",
            "Data columns (total 17 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   ID                   5000 non-null   int64  \n",
            " 1   PERFORMANCE_INDEX    4981 non-null   float64\n",
            " 2   EMISSIONS_CO2        4995 non-null   float64\n",
            " 3   URBAN_CONSUMPTION    4996 non-null   float64\n",
            " 4   DISPLACEMENT         4994 non-null   float64\n",
            " 5   CAPACITY             4999 non-null   float64\n",
            " 6   PISTON_COUNT         4993 non-null   float64\n",
            " 7   BLENDED_CONSUMPTION  4996 non-null   float64\n",
            " 8   GEARBOX_TYPE         5000 non-null   object \n",
            " 9   OUTPUT               4998 non-null   float64\n",
            " 10  BODY_STYLE           5000 non-null   object \n",
            " 11  EFFICIENCY_RATIO     4996 non-null   float64\n",
            " 12  YEARS_IN_SERVICE     4998 non-null   float64\n",
            " 13  MILEAGE              4995 non-null   float64\n",
            " 14  HIGHWAY_USAGE        4995 non-null   float64\n",
            " 15  ENERGY_SOURCE        5000 non-null   object \n",
            " 16  EFFICIENCY_RATING    5000 non-null   float64\n",
            "dtypes: float64(13), int64(1), object(3)\n",
            "memory usage: 664.2+ KB\n",
            "None\n",
            "\n",
            "Missing Values:\n",
            " ID                      0\n",
            "PERFORMANCE_INDEX      19\n",
            "EMISSIONS_CO2           5\n",
            "URBAN_CONSUMPTION       4\n",
            "DISPLACEMENT            6\n",
            "CAPACITY                1\n",
            "PISTON_COUNT            7\n",
            "BLENDED_CONSUMPTION     4\n",
            "GEARBOX_TYPE            0\n",
            "OUTPUT                  2\n",
            "BODY_STYLE              0\n",
            "EFFICIENCY_RATIO        4\n",
            "YEARS_IN_SERVICE        2\n",
            "MILEAGE                 5\n",
            "HIGHWAY_USAGE           5\n",
            "ENERGY_SOURCE           0\n",
            "EFFICIENCY_RATING       0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling Missing Values:"
      ],
      "metadata": {
        "id": "HgknFfEOMBYy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Columns for different imputation strategies\n",
        "mean_cols = ['PERFORMANCE_INDEX', 'CAPACITY', 'EFFICIENCY_RATIO']\n",
        "median_cols = ['EMISSIONS_CO2', 'URBAN_CONSUMPTION', 'DISPLACEMENT',\n",
        "               'BLENDED_CONSUMPTION', 'OUTPUT', 'YEARS_IN_SERVICE',\n",
        "               'MILEAGE', 'HIGHWAY_USAGE']\n",
        "mode_cols = ['PISTON_COUNT']\n",
        "\n",
        "# Mean imputation\n",
        "mean_imputer = SimpleImputer(strategy='mean')\n",
        "train[mean_cols] = mean_imputer.fit_transform(train[mean_cols])\n",
        "\n",
        "# Median imputation\n",
        "median_imputer = SimpleImputer(strategy='median')\n",
        "train[median_cols] = median_imputer.fit_transform(train[median_cols])\n",
        "\n",
        "# Mode imputation\n",
        "mode_imputer = SimpleImputer(strategy='most_frequent')\n",
        "train[mode_cols] = mode_imputer.fit_transform(train[mode_cols])\n",
        "\n",
        "# Verifying missing values after imputation\n",
        "print(\"Missing values after imputation:\\n\", train.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29zK9zq1Gvqs",
        "outputId": "02cb799f-e254-45db-dc4d-d7071a5d40e0"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values after imputation:\n",
            " ID                     0\n",
            "PERFORMANCE_INDEX      0\n",
            "EMISSIONS_CO2          0\n",
            "URBAN_CONSUMPTION      0\n",
            "DISPLACEMENT           0\n",
            "CAPACITY               0\n",
            "PISTON_COUNT           0\n",
            "BLENDED_CONSUMPTION    0\n",
            "GEARBOX_TYPE           0\n",
            "OUTPUT                 0\n",
            "BODY_STYLE             0\n",
            "EFFICIENCY_RATIO       0\n",
            "YEARS_IN_SERVICE       0\n",
            "MILEAGE                0\n",
            "HIGHWAY_USAGE          0\n",
            "ENERGY_SOURCE          0\n",
            "EFFICIENCY_RATING      0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the mode of the 'ENERGY_SOURCE' column\n",
        "mode_value = train['ENERGY_SOURCE'].mode()[0]\n",
        "\n",
        "# Replace occurrences of \"247234.3039\" with the mode value\n",
        "train['ENERGY_SOURCE'] = train['ENERGY_SOURCE'].replace('\"247234.3039\"', mode_value)\n",
        "\n",
        "# Verify the change by checking the unique values again\n",
        "print(\"Updated unique values in 'ENERGY_SOURCE':\", train['ENERGY_SOURCE'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8EXayodOztq",
        "outputId": "85b16a96-45c7-45a9-e75c-ce50a73e1b8e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated unique values in 'ENERGY_SOURCE': ['Petrol' 'Gas' 'Diesel']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### One-Hot Encoding:"
      ],
      "metadata": {
        "id": "iGYpHbzuMDnq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display unique values for specified columns\n",
        "print(\"Unique values in 'GEARBOX_TYPE':\", train['GEARBOX_TYPE'].unique())\n",
        "print(\"Unique values in 'BODY_STYLE':\", train['BODY_STYLE'].unique())\n",
        "print(\"Unique values in 'ENERGY_SOURCE':\", train['ENERGY_SOURCE'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0A8jXLG0NbrJ",
        "outputId": "aa1b36b7-730a-4f40-e228-b5ca092f30c1"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in 'GEARBOX_TYPE': ['Manual' 'Automatic' 'eMr']\n",
            "Unique values in 'BODY_STYLE': ['COMPACT' 'SUV - SMALL' 'MID-SIZE' 'PICKUP TRUCK - STANDARD' 'TWO-SEATER'\n",
            " 'FULL-SIZE' 'MINICOMPACT' 'MINIVAN' 'SUBCOMPACT' 'SUV - STANDARD'\n",
            " 'SPECIAL PURPOSE VEHICLE' 'STATION WAGON - SMALL' 'VAN - CARGO'\n",
            " 'VAN - PASSENGER' 'STATION WAGON - MID-SIZE' 'PICKUP TRUCK - SMALL']\n",
            "Unique values in 'ENERGY_SOURCE': ['Petrol' 'Gas' 'Diesel']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_cols = ['GEARBOX_TYPE', 'BODY_STYLE', 'ENERGY_SOURCE']\n",
        "\n",
        "# Encode categorical variables\n",
        "encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')\n",
        "encoded_train = pd.DataFrame(encoder.fit_transform(train[categorical_cols]))\n",
        "\n",
        "# Add back column names to the encoded dataframe\n",
        "encoded_train.columns = encoder.get_feature_names_out(categorical_cols)\n",
        "\n",
        "# Drop the original categorical columns and concatenate the encoded ones\n",
        "train = train.drop(columns=categorical_cols).reset_index(drop=True)\n",
        "train = pd.concat([train, encoded_train], axis=1)\n",
        "\n",
        "display(\"Data after one-hot encoding:\", train.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "llxbGResMDZx",
        "outputId": "db2f2be7-d8df-4865-f61c-906db0b63fee"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'Data after one-hot encoding:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   ID  PERFORMANCE_INDEX  EMISSIONS_CO2  URBAN_CONSUMPTION  DISPLACEMENT  \\\n",
              "0   0          74.595588          149.0                8.3           2.0   \n",
              "1   1          62.338357          262.0               22.5           3.6   \n",
              "2   2          72.105592          204.0               11.7           2.8   \n",
              "3   3          86.706104          301.0               23.4           5.7   \n",
              "4   4         104.793985          375.0               20.5           2.2   \n",
              "\n",
              "   CAPACITY  PISTON_COUNT  BLENDED_CONSUMPTION  OUTPUT  EFFICIENCY_RATIO  ...  \\\n",
              "0       5.0           4.0                  7.9    87.0              1.60  ...   \n",
              "1      15.0           6.0                 16.5   163.0              1.52  ...   \n",
              "2       5.0           6.0                 10.0   172.0              1.59  ...   \n",
              "3      14.0           8.0                 13.9   286.0              1.42  ...   \n",
              "4       5.0           4.0                 11.9   118.0              1.54  ...   \n",
              "\n",
              "   BODY_STYLE_STATION WAGON - SMALL  BODY_STYLE_SUBCOMPACT  \\\n",
              "0                               0.0                    0.0   \n",
              "1                               0.0                    0.0   \n",
              "2                               0.0                    0.0   \n",
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              "4                               0.0                    0.0   \n",
              "\n",
              "   BODY_STYLE_SUV - SMALL  BODY_STYLE_SUV - STANDARD  BODY_STYLE_TWO-SEATER  \\\n",
              "0                     0.0                        0.0                    0.0   \n",
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              "2                     0.0                        0.0                    0.0   \n",
              "3                     0.0                        0.0                    0.0   \n",
              "4                     0.0                        0.0                    0.0   \n",
              "\n",
              "   BODY_STYLE_VAN - CARGO  BODY_STYLE_VAN - PASSENGER  ENERGY_SOURCE_Diesel  \\\n",
              "0                     0.0                         0.0                   0.0   \n",
              "1                     0.0                         0.0                   0.0   \n",
              "2                     0.0                         0.0                   0.0   \n",
              "3                     0.0                         0.0                   0.0   \n",
              "4                     0.0                         0.0                   0.0   \n",
              "\n",
              "   ENERGY_SOURCE_Gas  ENERGY_SOURCE_Petrol  \n",
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              "2                1.0                   0.0  \n",
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    {
      "cell_type": "markdown",
      "source": [
        "### Normalization"
      ],
      "metadata": {
        "id": "aexaZ0bqT9d9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Columns to normalize\n",
        "columns_to_normalize = [\n",
        "    'PERFORMANCE_INDEX', 'EMISSIONS_CO2', 'URBAN_CONSUMPTION',\n",
        "    'DISPLACEMENT', 'CAPACITY', 'BLENDED_CONSUMPTION', 'OUTPUT',\n",
        "    'EFFICIENCY_RATIO', 'MILEAGE', 'HIGHWAY_USAGE'\n",
        "]\n",
        "\n",
        "# Initialize the MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Apply Min-Max scaling to the specified columns\n",
        "train[columns_to_normalize] = scaler.fit_transform(train[columns_to_normalize])\n",
        "\n",
        "# Verify the result by checking the first few rows\n",
        "display(train[columns_to_normalize].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "WtK52UaQMDWS",
        "outputId": "24b464ed-264c-41e0-dc48-7f3afcb26565"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   PERFORMANCE_INDEX  EMISSIONS_CO2  URBAN_CONSUMPTION  DISPLACEMENT  \\\n",
              "0           0.196465       0.113260           0.144531      0.166667   \n",
              "1           0.124786       0.425414           0.699219      0.433333   \n",
              "2           0.181904       0.265193           0.277344      0.300000   \n",
              "3           0.267287       0.533149           0.734375      0.783333   \n",
              "4           0.373064       0.737569           0.621094      0.200000   \n",
              "\n",
              "   CAPACITY  BLENDED_CONSUMPTION    OUTPUT  EFFICIENCY_RATIO  MILEAGE  \\\n",
              "0    0.1875             0.151659  0.112805          0.811111     0.35   \n",
              "1    0.8125             0.559242  0.344512          0.722222     0.45   \n",
              "2    0.1875             0.251185  0.371951          0.800000     0.40   \n",
              "3    0.7500             0.436019  0.719512          0.611111     0.45   \n",
              "4    0.1875             0.341232  0.207317          0.744444     0.45   \n",
              "\n",
              "   HIGHWAY_USAGE  \n",
              "0       0.044872  \n",
              "1       0.474359  \n",
              "2       0.352564  \n",
              "3       0.557692  \n",
              "4       0.307692  "
            ],
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PERFORMANCE_INDEX</th>\n",
              "      <th>EMISSIONS_CO2</th>\n",
              "      <th>URBAN_CONSUMPTION</th>\n",
              "      <th>DISPLACEMENT</th>\n",
              "      <th>CAPACITY</th>\n",
              "      <th>BLENDED_CONSUMPTION</th>\n",
              "      <th>OUTPUT</th>\n",
              "      <th>EFFICIENCY_RATIO</th>\n",
              "      <th>MILEAGE</th>\n",
              "      <th>HIGHWAY_USAGE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.196465</td>\n",
              "      <td>0.113260</td>\n",
              "      <td>0.144531</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.1875</td>\n",
              "      <td>0.151659</td>\n",
              "      <td>0.112805</td>\n",
              "      <td>0.811111</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.044872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.124786</td>\n",
              "      <td>0.425414</td>\n",
              "      <td>0.699219</td>\n",
              "      <td>0.433333</td>\n",
              "      <td>0.8125</td>\n",
              "      <td>0.559242</td>\n",
              "      <td>0.344512</td>\n",
              "      <td>0.722222</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.474359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.181904</td>\n",
              "      <td>0.265193</td>\n",
              "      <td>0.277344</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.1875</td>\n",
              "      <td>0.251185</td>\n",
              "      <td>0.371951</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.352564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.267287</td>\n",
              "      <td>0.533149</td>\n",
              "      <td>0.734375</td>\n",
              "      <td>0.783333</td>\n",
              "      <td>0.7500</td>\n",
              "      <td>0.436019</td>\n",
              "      <td>0.719512</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.557692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.373064</td>\n",
              "      <td>0.737569</td>\n",
              "      <td>0.621094</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.1875</td>\n",
              "      <td>0.341232</td>\n",
              "      <td>0.207317</td>\n",
              "      <td>0.744444</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.307692</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(train[columns_to_normalize]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"PERFORMANCE_INDEX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09533770297992919,\n        \"min\": 0.12478571432748536,\n        \"max\": 0.3730642409356724,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.12478571432748536,\n          0.3730642409356724,\n          0.18190404777777774\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EMISSIONS_CO2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.24062819051522777,\n        \"min\": 0.11325966850828731,\n        \"max\": 0.7375690607734806,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.425414364640884,\n          0.7375690607734806,\n          0.2651933701657459\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"URBAN_CONSUMPTION\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2669772564634486,\n        \"min\": 0.14453125,\n        \"max\": 0.734375,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.69921875,\n          0.62109375,\n          0.27734375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DISPLACEMENT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2498888641865509,\n        \"min\": 0.16666666666666669,\n        \"max\": 0.7833333333333335,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.43333333333333346,\n          0.2000000000000001,\n          0.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAPACITY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.32596012026013244,\n        \"min\": 0.1875,\n        \"max\": 0.8125,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.1875,\n          0.8125,\n          0.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BLENDED_CONSUMPTION\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1583817901361549,\n        \"min\": 0.15165876777251183,\n        \"max\": 0.5592417061611374,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5592417061611374,\n          0.34123222748815163,\n          0.2511848341232227\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OUTPUT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23111887732309155,\n        \"min\": 0.11280487804878048,\n        \"max\": 0.7195121951219511,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.34451219512195114,\n          0.20731707317073164,\n          0.37195121951219506\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EFFICIENCY_RATIO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07996912984642934,\n        \"min\": 0.611111111111111,\n        \"max\": 0.8111111111111112,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7222222222222222,\n          0.7444444444444446,\n          0.8000000000000002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MILEAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04472135954999579,\n        \"min\": 0.3500000000000001,\n        \"max\": 0.45000000000000007,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.3500000000000001,\n          0.45000000000000007,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HIGHWAY_USAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1958750322804681,\n        \"min\": 0.044871794871794816,\n        \"max\": 0.5576923076923076,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.4743589743589744,\n          0.3076923076923076,\n          0.35256410256410253\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Outliers:"
      ],
      "metadata": {
        "id": "oUywaQTFU5wA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Columns to check for outliers\n",
        "columns_to_check = ['PERFORMANCE_INDEX', 'EMISSIONS_CO2', 'URBAN_CONSUMPTION',\n",
        "                   'DISPLACEMENT', 'CAPACITY', 'PISTON_COUNT',\n",
        "                   'BLENDED_CONSUMPTION', 'OUTPUT', 'EFFICIENCY_RATIO',\n",
        "                   'YEARS_IN_SERVICE', 'MILEAGE', 'HIGHWAY_USAGE']\n",
        "\n",
        "# Create a box plot for each column to visualize outliers\n",
        "plt.figure(figsize=(12, 8))\n",
        "for i, col in enumerate(columns_to_check, 1):\n",
        "    plt.subplot(3, 4, i)  # Create subplots in a grid of 3 rows and 4 columns\n",
        "    sns.boxplot(x=train[col])  # Create a boxplot for the column\n",
        "    plt.title(f'Box Plot of {col}')  # Title for each plot\n",
        "\n",
        "plt.tight_layout()  # Adjust layout for better spacing\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "KI7tWF46MDTq",
        "outputId": "a34005a8-b11f-47b1-cdf6-4f1b378b42a7"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 12 Axes>"
            ],
            "image/png": 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NN5hu3bpZU9k8eNqG8+fPm2bNmplOnTqZDRs2mKSkJJOQkGASExMtrvn/eNqG+Ph4ExwcbOLj401SUpJZu3atiY6ONo8//rjFNf+fVatWmbFjx5oPPvjASDLLly/Pt/zevXtNWFiYGT58uPn111/N66+/bvz9/c2aNWusqfAlZMuWLaZmzZrmmmuuMcOGDSvt6ly048ePm9jYWDNw4EDz7bffmr1795q1a9ea3bt3l3bVLtqECRNMxYoVzcqVK01SUpJZunSpCQ8PN9OnTy/tqnmsoHN60qRJJjIy0qxYscJs377ddO3a1dSqVcukpaWVToU9kF/bTp48aTp06GCWLFlifvvtN7N582bTokUL07Rp09KrsBv0pbyjL+UL/Shj6EvRlyp+xCjviFHG+EacIkYRo4wxptgSUi1atDBDhw51vM/KyjLVqlUzEydOdFu+R48e5vbbb3ea1rJlSzNkyJDiqpLHPG1DbpmZmSYiIsK8/fbbJVXFQilKOzIzM03r1q3Nf/7zHzNgwIBSD1CetmHmzJmmdu3aJj093aoqFsjTNgwdOtTcfPPNTtOGDx9u2rRpU6L1LKzCBKiRI0eaBg0aOE3r2bOnufXWW0uwZpee1NRUU69ePfPZZ5+Zdu3a+URCatSoUaZt27alXY0Scfvtt5v77rvPadqdd95p+vbtW0o1Kh65z+ns7GxTtWpV89JLLzmmnTx50gQHB5tFixaVQg2LrjDxasuWLUaS2b9/vzWVKgT6Ut7Rl/KFfpQx9KXs6EsVH2KUd8QoY3wjThGjLrjcY1Sx3LKXnp6u77//Xh06dHBM8/PzU4cOHbR582a3y2zevNmpvCTdeuuteZYvaUVpQ25nz55VRkaGKlSoUFLVLFBR2/Hcc8+pSpUquv/++62oZr6K0oaPPvpIrVq10tChQxUVFaWGDRvqxRdfVFZWllXVdlKUNrRu3Vrff/+94zLPvXv3atWqVerUqZMldS4O3nZee6uhQ4fq9ttvd9lXl7KPPvpIzZo1U/fu3VWlShVde+21mjNnTmlXq1i0bt1a69at086dOyVJ27dv14YNG9SxY8dSrlnxSkpK0pEjR5w+l5GRkWrZsqVPnsMpKSmy2WwqV65caVdFEn0pu9LuS/lCP0qiL0VfqvgRoy4o7Rgl+UacIkYRo+wCiqNSf/31l7KyshQVFeU0PSoqSr/99pvbZY4cOeK2/JEjR4qjSh4rShtyGzVqlKpVq1aqPzKL0o4NGzZo7ty5SkxMtKCGBStKG/bu3asvvvhCffv21apVq7R79249/PDDysjI0Pjx462otpOitKFPnz7666+/1LZtWxljlJmZqQcffFBjxoyxosrFIq/z+tSpU0pLS1NoaGgp1cx7LF68WNu2bdPWrVtLuyrFau/evZo5c6aGDx+uMWPGaOvWrXr00UcVFBSkAQMGlHb1Lsro0aN16tQpxcXFyd/fX1lZWZowYYL69u1b2lUrVvbvX2/6bi4p586d06hRo9S7d2+VLVu2tKsjib6UXWn3pXyhHyXRl6IvVfyIUReUdoySfCNOEaOIUXY8Za+YTJo0SYsXL9by5csVEhJS2tUptNTUVPXv319z5sxRpUqVSrs6RZadna0qVapo9uzZatq0qXr27KmxY8dq1qxZpV21QktISNCLL76oN998U9u2bdMHH3ygTz75RM8//3xpVw3F5MCBAxo2bJji4+MvqThRGNnZ2bruuuv04osv6tprr9XgwYM1aNCgS+oczMt7772n+Ph4vfvuu9q2bZvefvttTZ06VW+//XZpVw1FkJGRoR49esgYo5kzZ5Z2dZDDpdiX8pV+lERfCijIpRijJN+JU8Qo31QsV0hVqlRJ/v7+Sk5OdpqenJysqlWrul2matWqHpUvaUVpg93UqVM1adIkff7557rmmmtKspoF8rQde/bs0b59+9SlSxfHtOzsbElSQECAfv/9d9WpU6dkK51LUY5FdHS0AgMD5e/v75h21VVX6ciRI0pPT1dQUFCJ1jm3orTh3//+t/r3768HHnhAktSoUSOdOXNGgwcP1tixY+Xn5/3547zO67Jly3J1lKTvv/9eR48e1XXXXeeYlpWVpfXr1+uNN97Q+fPnnT7Dl5Lo6GhdffXVTtOuuuoqvf/++6VUo+IzYsQIjR49Wr169ZJ04dzcv3+/Jk6ceMlf/ZWTPTYlJycrOjraMT05OVlNmjQppVoVL3syav/+/friiy+85uooib6Ut/SlfKEfJdGXoi9V/IhR3hGjJN+IU8QoYpRdsbQ4KChITZs21bp16xzTsrOztW7dOrVq1crtMq1atXIqL0mfffZZnuVLWlHaIElTpkzR888/rzVr1qhZs2ZWVDVfnrYjLi5OP/30kxITEx2vrl27qn379kpMTFRMTIyV1ZdUtGPRpk0b7d692xFcJWnnzp2Kjo62PDhJRWvD2bNnXYKQPeCaS+Tx8t52Xnub//u//3M535o1a6a+ffsqMTHxkk1GSRfOwd9//91p2s6dOxUbG1tKNSo+eZ2bOeONL6hVq5aqVq3qdA6fOnVK3377rU+cw/Zk1K5du/T555+rYsWKpV0lJ/SlvKMv5Qv9KIm+VE70pYoHMco7YpTkG3GKGPU/l32MKtJQ6G4sXrzYBAcHmwULFphff/3VDB482JQrV84cOXLEGGNM//79zejRox3lN27caAICAszUqVPNjh07zPjx473iMaCetGHSpEkmKCjILFu2zBw+fNjxSk1NLa0mGGM8b0du3vDUBU/b8Mcff5iIiAjzyCOPmN9//92sXLnSVKlSxbzwwgul1QSP2zB+/HgTERFhFi1aZPbu3Ws+/fRTU6dOHdOjR4/SaoJJTU01P/zwg/nhhx+MJPPKK6+YH374wfFUqtGjR5v+/fs7ytsfAzpixAizY8cOM2PGDK95VLG38pWn7G3ZssUEBASYCRMmmF27dpn4+HgTFhZmFi5cWNpVu2gDBgww1atXNytXrjRJSUnmgw8+MJUqVTIjR44s7ap5rKBzetKkSaZcuXLmww8/ND/++KPp1q2bqVWrlklLSyvlmhcsv7alp6ebrl27mho1apjExESn7+zz58+XdtUd6Et5R1/KF/pRxtCXoi9V/IhR3hGjjPGNOEWMIkYZY0yxJaSMMeb11183V1xxhQkKCjItWrQw33zzjWNeu3btzIABA5zKv/fee6Z+/fomKCjINGjQwHzyySfFWZ0i8aQNsbGxRpLLa/z48dZXPBdPj0VO3hCgjPG8DZs2bTItW7Y0wcHBpnbt2mbChAkmMzPT4lo786QNGRkZ5plnnjF16tQxISEhJiYmxjz88MPmxIkT1lf8//vyyy/dfsbt9R4wYIBp166dyzJNmjQxQUFBpnbt2mb+/PmW1/tS4isJKWOM+fjjj03Dhg1NcHCwiYuLM7Nnzy7tKhWLU6dOmWHDhpkrrrjChISEmNq1a5uxY8d6VSKjsAo6p7Ozs82///1vExUVZYKDg83//d//md9//710K11I+bUtKSnJ7TxJ5ssvvyztqjuhL+UdfSlf6EcZQ1+KvlTxI0Z5R4wyxjfiFDGKGGUz5hK5NgwAAAAAAAA+wftHzQIAAAAAAIBPISEFAAAAAAAAS5GQAgAAAAAAgKVISAEAAAAAAMBSJKQAAAAAAABgKRJSAAAAAAAAsBQJKQAAAAAAAFiKhBQAAAAAAAAsRULKxx05ckT/+te/VLt2bQUHBysmJkZdunTRunXrnMpNnDhR/v7+eumll1zWsWDBAtlsNtlsNvn5+alGjRq69957dfTo0UKvo7B1qVmzpqZNm6aEhATHNvN6Pf/884qOjtbx48edtrN9+3YFBwdr5cqVRd1tAHIZOHCg49wLCgpS3bp19dxzzykzM9Nxvp48edJRfs6cOWrcuLHCw8NVrlw5XXvttZo4caKkC+d5fuf2wIEDHetZuXKl2rVrp4iICIWFhal58+ZasGCBU9327dsnm82mKlWqKDU11WlekyZN9MwzzxS6nbt379a9996rGjVqKDg4WLVq1VLv3r313XffOZUrTL3c7Rc7e6yzs9lsCgkJ0f79+53K3XHHHY79UVBM9KSdQHHJGRtsNpsqVqyo2267TT/++KOjjM1m04oVK9wun9/3/ZEjRyRJzzzzjGw2mx588EGnZRMTE2Wz2bRv3z5J/4sF9ldERIQaNGigoUOHateuXU7L5uzb5HyFhIS4bVtgYKCioqJ0yy23aN68ecrOzvZoP/3www/q3r27oqKiFBISonr16mnQoEHauXOnU7m3335bzZs3V1hYmCIiItSuXTuX/ox9nzVo0EBZWVlO88qVK+cUi7Zv366uXbuqSpUqCgkJUc2aNdWzZ09HH87TOGWz2fTNN984lTt//rwqVqwom82mhIQEl/I2m02RkZFq06aNvvjiC5d5ecUz+/FMTEws0X0E+KIDBw7ovvvuU7Vq1RQUFKTY2FgNGzZMf//9t6NM7nPc7plnnlGTJk0keXau5vwe+Mc//qEffvih2LcF30NCyoft27dPTZs21RdffKGXXnpJP/30k9asWaP27dtr6NChTmXnzZunkSNHat68eW7XVbZsWR0+fFgHDx7UnDlztHr1avXv37/Q6/CkLpLUunVrHT582PHq0aOHbrvtNqdpo0aNUkxMjNPyGRkZGjBggPr166fOnTsXZbcByIP9HNy1a5eeeOIJPfPMM24T0PPmzdNjjz2mRx99VImJidq4caNGjhyp06dPS5K2bt3qOI/ff/99SdLvv//umDZ9+nRJ0uuvv65u3bqpTZs2+vbbb/Xjjz+qV69eevDBB/Xkk0+6bDc1NVVTp04tcvu+++47NW3aVDt37tRbb72lX3/9VcuXL1dcXJyeeOIJRzlP61VYNptN48aNy3N+zvg3bdo0R1y2vy5m28DFyPn9vG7dOgUEBHj8HZwzBthfVapUccwPCQnR3LlzXRJL7nz++ec6fPiwtm/frhdffFE7duxQ48aNXf4Yl/scOnz4sEtS2N62ffv2afXq1Wrfvr2GDRumzp07KzMzs1BtW7lypa6//nqdP39e8fHx2rFjhxYuXKjIyEj9+9//dpR78sknNWTIEPXs2VM//vijtmzZorZt26pbt2564403XNa7d+9e/fe//81zu8eOHdP//d//qUKFClq7dq127Nih+fPnq1q1ajpz5kyh6p5bTEyM5s+f7zRt+fLlCg8Pd1t+/vz5Onz4sDZu3KhKlSqpc+fO2rt3b5HjWXHvI8AX7d27V82aNdOuXbu0aNEi7d69W7NmzdK6devUqlUrlz/m58eTc9Uee9euXavTp0+rY8eObpPdxbEt+BADn9WxY0dTvXp1c/r0aZd5J06ccPw/ISHBVK9e3aSnp5tq1aqZjRs3OpWdP3++iYyMdJo2YcIE4+fnZ86ePVuodRS2LrGxsebVV191KTNgwADTrVs3l+k7duwwISEhZunSpcYYY8aPH29iY2NNSkqKS1kARefuHLzlllvM9ddfb7788ksjyXEud+vWzQwcOLBQ6829rN0ff/xhAgMDzfDhw12Wee2114wk88033xhjjElKSjKSzIgRI0x4eLhJTk52lG3cuLEZP358gfXIzs42DRo0ME2bNjVZWVku8+3186ReebXNGNdYJ8k8+eSTxs/Pz/z000+O6d26dTMDBgxwWd5dXAZKg7vY8PXXXxtJ5ujRo8aYC5/v5cuXu10+v/PEbvz48aZx48bmlltuMd27d3dM/+GHH4wkk5SUZIz5Xyz44YcfnJbPysoyN910k4mNjTWZmZnGmMKdQ3n1PdatW2ckmTlz5uS7vDHGnDlzxlSqVMnccccdbufb271582Yjybz22msuZYYPH24CAwPNH3/8YYz53z4bMWKEiYmJMefOnXOUjYyMNPPnzzfGGLN8+XITEBBgMjIy8qyfp3Hq6aefNmXLlnX0/4y58F3w73//20gyX375pVP5nMf9zz//NJLMrFmznLaT17HIfTxLYh8Bvui2224zNWrUcDpPjTHm8OHDJiwszDz44IPGmLx/d9ljbm6FPVeNMWbjxo1GklmzZk2xbgu+hyukfNTx48e1Zs0aDR06VGXKlHGZX65cOcf/586dq969eyswMFC9e/fW3LlzC1x/aGiosrOzHX8dzG8dntTFU3FxcZo4caIeeughrV27VhMnTtT8+fNVtmzZIq8TQOGEhoYqPT3dZXrVqlX1zTffuFxp4Illy5YpIyPD7V/DhgwZovDwcC1atMhpeu/evR23EnoqMTFRv/zyi5544gn5+bl+NdrjVFHqVVht2rRR586dNXr06CItD3iD06dPa+HChapbt64qVqxYrOueNGmS3n//fZdbaAvi5+enYcOGaf/+/fr+++8vuh4333yzGjdurA8++KDAsmvXrtVff/2lkSNHup1vjy2LFi1SeHi4hgwZ4lLmiSeeUEZGhuOKUrvHHntMmZmZev31192uu2rVqsrMzNTy5ctljCmwroXRtGlT1axZ01GXP/74Q+vXr3e5at6d0NBQSXL7vVEYJbGPAF9z/PhxrV27Vg8//LDjnLOrWrWq+vbtqyVLlhRbTMjLxZ7vuHyQkPJRu3fvljFGcXFx+ZY7deqUli1bpn79+kmS+vXrp/fee89xa407u3bt0qxZs9SsWTNFREQUuI7C1qWohg0bpoYNG6pTp0566KGH1L59+xLZDoALjDH6/PPPtXbtWt18880u88ePH69y5cqpZs2auvLKKzVw4EC99957Ho25snPnTkVGRio6OtplXlBQkGrXru0y9orNZtOkSZM0e/Zs7dmzx6M22W8DKihOFaVenpg4caLWrFmjr7/+usjrAKy2cuVKhYeHKzw8XBEREfroo4+0ZMkSt8ndvNSoUcOxjvDwcDVo0MClzHXXXacePXpo1KhRHtfRfm7bx5uSpJSUFKdthoeHq2PHjoVeX8515cWT2FKnTh0FBQW5zKtWrZrKli3rElvCwsI0fvx4TZw4USkpKS7LXX/99RozZoz69OmjSpUqqWPHjnrppZeUnJxcYL3zc9999zmGZ1iwYIE6deqkypUr57vM2bNn9fTTT8vf31/t2rUr0nZLYh8BvmbXrl0yxuiqq65yO/+qq67SiRMndOzYsRKrw8mTJ/X8888rPDxcLVq0KLHtwDeQkPJRhc16L1q0SHXq1FHjxo0lXRgAODY2VkuWLHEqZ++0hYWF6corr1RUVJTi4+MLtY6SzsDbbDaNHTtW2dnZevrpp0t0W8DlzP6jMyQkRB07dlTPnj3dDjAZHR2tzZs366efftKwYcOUmZmpAQMG6LbbbvN4IGBP3XrrrWrbtq3TuCyFUdJxqrCuvvpq3XPPPVwlhUtK+/btlZiYqMTERG3ZskW33nqrOnbs6NFVkl9//bVjHYmJiVq1apXbci+88IK+/vprffrppx7V0X6O22w2x7SIiAinbSYmJuo///lPodeXc10FbdeTOnri/vvvV8WKFTV58mS38ydMmKAjR45o1qxZatCggWbNmqW4uDj99NNPHm/Lrl+/ftq8ebP27t2rBQsW6L777suzbO/evR2Jyvfff19z587VNddcU+Rtl8Q+AnxRafRrWrdurfDwcJUvX17bt2/XkiVLFBUVZXk9cGkhIeWj6tWrJ5vNpt9++y3fcnPnztUvv/yigIAAx+vXX391GZjc3mn7+eefdebMGa1fv17169cv1DoKW5eLERAQ4PQvgOJn/9G5a9cupaWl6e2333Z7G65dw4YN9fDDD2vhwoX67LPP9Nlnn+mrr74q1Lbq16+vlJQUHTp0yGVeenq69uzZ44hBuU2aNElLlixxerpLYbYnqcA45Um97LcOu/ur/MmTJxUZGel2G88++6y2bduW51PJAG9TpkwZ1a1bV3Xr1lXz5s31n//8R2fOnNGcOXMKvY5atWo51lG3bl3Fxsa6LVenTh0NGjRIo0eP9ugH144dOxzbsfPz83PaZt26dVW9evVCry/nuvLiSWzZu3ev29tbDh06pFOnTrmNeQEBAZowYYKmT5/uNi5JUsWKFdW9e3dNnTpVO3bsULVq1RwPgChKnKpYsaI6d+6s+++/X+fOncv3qrJXX31ViYmJOnLkiI4cOaIBAwbkWbYgJbmPAF9Rt25d2Ww2R8zLbceOHSpfvrwqV66ssmXLetxHyc+SJUu0fft2nThxQnv27FGnTp0c84p7W/AdJKR8VIUKFXTrrbdqxowZbp+kcvLkSf3000/67rvvlJCQ4PTXwYSEBG3evNmp82TvtNWuXdvpfuTCrKMwdQHg/ew/Oq+44gqPk79XX321JBX6yU533XWXAgMD9fLLL7vMmzVrls6cOaPevXu7XbZFixa68847PbrKqEmTJrr66qv18ssvu72Kyx6nPKlXvXr15Ofn5zJmzd69e5WSkpJnQi0mJkaPPPKIxowZ4/K4cuBSYLPZ5Ofnp7S0tBJZ/7hx47Rz504tXry4UOWzs7P12muvqVatWrr22msvevtffPGFfvrpJ911110Flv3HP/6hSpUqacqUKW7n22NLr169dPr0ab311lsuZaZOnarAwMA8t9e9e3c1aNBAzz77bIH1CQoKUp06dRyxuKhx6r777lNCQoLuuece+fv757m9qlWrqm7dugXe0lcYVu0j4FJWsWJF3XLLLXrzzTddYvCRI0cUHx+vnj17ymaz6corr3Q7rt62bdvyPPfzExMTozp16rgdH7i4twXfweUkPmzGjBlq06aNWrRooeeee07XXHONMjMz9dlnn2nmzJm69dZb1aJFC914440uyzZv3lxz5851+0j3nObOnVuodRRUl7yy+AAuPQ899JCqVaumm2++WTVq1NDhw4f1wgsvqHLlymrVqlWh1nHFFVdoypQpeuKJJxQSEqL+/fsrMDBQH374ocaMGaMnnnhCLVu2zHP5CRMmqEGDBoVOnNlsNs2fP18dOnTQDTfcoLFjxyouLk6nT5/Wxx9/rE8//VRfffWVR/WKiIjQAw88oCeeeEIBAQFq1KiRDhw4oFGjRun6669X69at86zPU089pTlz5igpKUk9e/YsVBuA0nL+/HkdOXJEknTixAm98cYbOn36tLp06eIok5SUpMTERKfl6tWr5/j/0aNHde7cOaf5FStWVGBgoMv2oqKiNHz48Dz7KH///beOHDmis2fP6ueff9a0adO0ZcsWffLJJ07JE2OMo945ValSxTH+lb1tWVlZSk5O1po1azRx4kR17txZ99xzTwF75kIi/z//+Y+6d++url276tFHH1XdunX1119/6b333tMff/yhxYsXq1WrVho2bJhGjBih9PR03XHHHcrIyNDChQs1ffp0TZs2TTExMXluZ9KkSbr11ludpq1cuVKLFy9Wr169VL9+fRlj9PHHH2vVqlWaP3++pKLHqdtuu03Hjh2z9CEyJbGPAF/0xhtvqHXr1rr11lv1wgsvqFatWvrll180YsQIVa9eXRMmTJAkPf7447rhhhs0YcIE3XnnncrKytKiRYu0efNmvfnmm8VaJyu3hUuM1Y/1g7UOHTpkhg4damJjY01QUJCpXr266dq1q1m7dq2pWLGimTJlitvlJk+ebKpUqWLS09PzfOzm+fPnC72O/OqS8xHBeT0SNK9HL9sV5rHRAIouv3Mw9/m3bNky06lTJxMdHW2CgoJMtWrVzF133WV+/PHHApfN7cMPPzQ33HCDKVOmjAkJCTFNmzY18+bNcyqT16PeBw8ebCSZ8ePHF7qdv//+u7nnnntMtWrVTFBQkImNjTW9e/c227Zt87hexhiTlpZmxo8fb+Li4kxoaKipVauWGTx4sDl27JhTOeV6PLoxxrz44otGkhkwYIDLenkcMrzFgAEDjCTHKyIiwjRv3twsW7bMUSbn/Jyvr7/+2hED3L02b95sjHH/WPCUlBRTqVIlI8kkJSUZY/4XC+yvsLAwc9VVV5mHH37Y7Nq1y2n5+fPn57ndw4cPu7QtICDAVK5c2XTo0MHMmzfPZGVlebSftm7dau68805TuXJlExwcbOrWrWsGDx7sUq+5c+eapk2bmpCQEFOmTBlzww03mI8++sipTF5x8x//+IeRZObPn2+MMWbPnj1m0KBBpn79+iY0NNSUK1fONG/e3DHf7mLilN2JEyeMJKc+XX7lc/LkUfLGFO8+AnzVvn37zIABA0xUVJQJDAw0MTEx5l//+pf566+/nMqtXbvWtGnTxpQvX95UrFjR3HTTTearr75yu05Pz9XcimNb8D02Y7xkJFcAAAAAAABcFhhDCgAAAAAAAJYiIQUA8Hlff/21wsPD83wBQFHEx8fnGVcaNGhQ2tUDAMCrccseAMDnpaWl6c8//8xzft26dS2sDQBfkZqaquTkZLfzAgMDFRsba3GNAAC4dJCQAgAAAAAAgKW4ZQ8AAAAAAACWIiEFAAAAAAAAS5GQAgAAAAAAgKVISAEAAAAAAMBSJKQAAAAAAABgKRJSAAAAAAAAsBQJKQAAAAAAAFiKhBQAAAAAAAAsRUIKAAAAAAAAliIhBQAAAAAAAEuRkAIAAAAAAIClSEgBAAAAAADAUiSkAAAAAAAAYCkSUgAAALjs7du3TzabTQsWLCjtqjhZs2aNmjRpopCQENlsNp08ebK0qwQAQLEgIVVIdFIAeCNiEwBvs2DBAtlsNqdXlSpV1L59e61evdry+iQkJDjVJTAwULVr19Y999yjvXv3Fss2Nm3apGeeeabYY93ff/+tHj16KDQ0VDNmzNA777yjMmXKuC2bc79v2LDBZb4xRjExMbLZbOrcubPTPJvNpkceecTx3v7dMnXq1HzrV7NmTZdjbX/ddtttbpcZOXKkbDabevbsme+6k5KS9Mgjj6h+/foKCwtTWFiYrr76ag0dOlQ//vijU9lnnnkmz3rYbDYdOXIk320B7tDHAkqe5QkpOinFp6idFHevb775xlE2rzJVq1Z1lMnvi3/WrFlO68rZwbE7deqUnn32WTVu3Fjh4eEKDQ1Vw4YNNWrUKB06dMhRbuDAgQoPD3da9qabbspz23FxcS5tDgkJ0Z9//ulSh5tuukkNGzZ0mZ6VlaX58+frpptuUoUKFRQcHKyaNWvq3nvv1XfffaeTJ08qOjpabdq0kTHGZflvvvlGfn5+GjFihNtjkZv9y87+8vPzU4UKFdSxY0dt3rw5z+V27NjhaF/Oz9bAgQPzPdb218CBA/PdDxkZGXrttdfUvHlzRUREKDw8XM2bN9drr72mjIyMQrXtUkJsKj6FjU0dO3ZU+fLllZyc7DIvJSVF0dHRatmypbKzs132R+7X4sWLXdaRlZWlatWqyWaz5XkMc8eywMBA1axZU48++qjb/ZKenq7p06fr2muvVdmyZVWuXDk1aNBAgwcP1m+//ebRfsrrB6DNZtP777+fZ13/+usvj7azb98+3XvvvapTp45CQkJUtWpV3XjjjRo/frxTOU9jq/0VEBCg6tWra+DAgY5Ym5GRoUqVKqlt27Z51sv+4/i6666T9L/P/LJly1zK7tmzR0OGDFHt2rUVEhKismXLqk2bNpo+fbrS0tIc5Yry4xgl57nnntM777yj//73vxo5cqSOHTumTp06aeXKlaVSn0cffVTvvPOOZs+erdtvv11LlixR8+bNnfodRbVp0yY9++yzxR5Pt27dqtTUVD3//PO6//771a9fPwUGBua7TEhIiN59912X6V999ZUOHjyo4ODgYq1jkyZN9M4777i8Ro4c6VLWGKNFixapZs2a+vjjj5Wamup2nStXrlTDhg31zjvvqEOHDnr11Vc1ffp0dezYUatWrVKTJk20f/9+l+Vmzpzpti7lypUr1jbDM/Sxik9Rfv999913bue7+w1Qs2ZNl4S1JJ0/f16vv/662rZtq/LlyysoKEjVqlVT165dtWjRImVlZTnKFpTQzt2fefjhh+Xn56fjx487lTt+/Lj8/PwUHBysc+fOOc3bu3evbDabxowZ4zT95MmTjkTdjh07HNM3b94sPz8/PfXUU27rNHnyZNlsNn3yySdu5xfUhtwaNmyom266yWnasWPHNGzYMMXFxSk0NFRVqlRRixYtNGrUKJ0+fdrtet58803ZbDa1bNky3/ocPXpUo0ePVqNGjRQeHq6QkBDVrVtX9957r8sfKDzJC1ghwNKt5fDcc8+pVq1aMsYoOTlZCxYsUKdOnfTxxx+7PQlK2qOPPqrmzZsrIyND27Zt0+zZs/XJJ5/op59+UrVq1S5q3fZOysCBA4v1CzFnJ6VDhw6FWsa+33OrW7eu0/tbbrlF99xzj9O00NBQl+VmzpzpkjAq6ITZu3evOnTooD/++EPdu3fX4MGDFRQUpB9//FFz587V8uXLtXPnznzXUaNGDU2cONFlemRkpMu08+fPa9KkSXr99dfzXackpaWl6c4779SaNWt04403asyYMapQoYL27dun9957T2+//bb++OMPTZs2Tb169dKcOXM0ePBgx/KZmZl68MEHFRsbq2effbbA7eXUu3dvderUSVlZWdq5c6fefPNNtW/fXlu3blWjRo1cyi9cuFBVq1bViRMntGzZMj3wwAOSpCFDhjh9HpKSkjRu3DgNHjxYN9xwg2N6nTp18qzLmTNndPvtt+urr75S586dNXDgQPn5+WnNmjUaNmyYPvjgA33yySd5fgFeyohNF6+wsenNN99Uw4YN9fjjj7v8eBozZoz++usvrVmzRn5+//vbiX1/5NaqVSuXaV988YUOHz6smjVrKj4+Xh07dsyzLvZYdubMGa1bt06vv/66tm3b5vIlftddd2n16tXq3bu3Bg0apIyMDP32229auXKlWrdu7ZS4uRjPPfec7rzzTtlstotaz+7du9W8eXOFhobqvvvuU82aNXX48GFt27ZNkydPdolTnsRW+7ly7tw5ffPNN1qwYIE2bNign3/+WSEhIerevbveeust7d+/X7GxsS7Lr1+/XgcPHtTjjz+ebxs++eQTde/eXcHBwbrnnnvUsGFDpaena8OGDRoxYoR++eUXzZ4921G+SZMmeuKJJ1zWc7HnCzzXsWNHNWvWzPH+/vvvV1RUlBYtWlQq8fSGG27Q3XffLUm69957Vb9+fT366KN6++238/yRUtqOHj0qSR7F6E6dOmnp0qV67bXXFBDwv67+u+++q6ZNm3qc1C5I9erV1a9fv0KVTUhI0MGDB/XFF1/o1ltv1QcffKABAwY4ldmzZ4969eql2NhYrVu3TtHR0U7zJ0+erDfffNPpu8Hu7rvvVqVKlYreGJQo+lgXryi//y7WsWPH1LFjR33//fe69dZb9fTTT6tChQo6cuSIPv/8c/Xp00e7d+/Wv//97yKtv23btpo5c6Y2btyoLl26OKZv2rRJfn5+ysjI0Hfffef0R66NGzc6ls1p6dKljgsp4uPj9cILL0i60E8cMmSIXn75ZfXr108NGjRwLLN//34999xz6t69u26//fYitaEgx48fV7NmzXTq1Cndd999iouL099//60ff/xRM2fO1EMPPeTym1qS4uPjVbNmTW3ZskW7d+92+c0uSVu2bNHtt9+u1NRU9erVSw8++KCCg4OVlJSkFStWaMGCBfrqq6904403Oi1X2LxAiTMWmz9/vpFktm7d6jT9+PHjJjAw0PTp08fS+nz55ZdGklm6dKnT9Ndee81IMi+++KIxxpikpCQjycyfP9/jbbz00ktGkklKSiqGGv/P22+/7XZfupPXfndHkhk6dGi+ZcaPH28kmWPHjnm0royMDNO4cWMTFhZmvv76a5fyKSkpZsyYMY73AwYMMGXKlHEq065dO9OgQYMC22Fvc5MmTUxwcLD5888/C1zP0KFDjSTz6quvuqwvMzPTvPTSS+bAgQPGGGM6duxoypcvb44cOeIoM3XqVCPJrFq1qsD62dk/Wy+99JLT9NWrVxtJ5qGHHnJZJjs729SsWdMMHz7c/POf/zQ33XRTnuvfunVrvp9dd/th8ODBRpJ5/fXXXcq/8cYbRpJ58MEHC9G6Swexqfh4EpsmT55sJJm1a9c6pm3ZssX4+fmZkSNHOqbltT/yc88995jrrrvOTJ8+3ZQpU8acPn3apUxesaxnz55Gkvn222+d6iXJTJgwwWU9mZmZ5q+//ip03YxxjY/2Y9mkSRMjybz//vuFqmt+Hn74YRMQEGD27dvnMi85OdnpvaexNffxHTVqlJFklixZYowx5uuvvzaSzMSJE92uZ/DgwcbPz88Rm90d471795rw8HATFxdnDh065LKOXbt2mWnTpjnex8bGmttvv73ANqBk5fUZyc7ONmXLljX33HOP0/TTp0+b4cOHmxo1apigoCBTv35989JLL5ns7GxjjDFnz541V155pbnyyivN2bNnHcv9/fffpmrVqqZVq1YmMzMzz/rkFT9+/vlnI8kMGjTIGJN3PF23bp1p27atCQsLM5GRkaZr167m119/dcy3n5u5XwXF1vfee89cd911JiQkxFSsWNH07dvXHDx40DG/Xbt2LuscMGBAnuuz7/elS5cam83m1Bc5f/68KV++vHn55Zfdnid5xaPcfZPcPD3n7r//fnP11VcbYy70o2655RaXMvY+yDfffFPo9RYlPsI69LGKT3H+/nP3ve/unL711luNn5+fS7/EbuvWrWbhwoWO9wXFj9zn6/79+40kp36fMcaMHj3aXHvttSYuLs6lL2HvQ5w4ccJp+o033mjuvPNO8/jjj5tatWo5zTt58qSJjo42bdq0cXy/GGNMly5dTGRkpNt+Rl4KijkNGjQw7dq1c7yfMmWKkWQ2btzoUjYlJcWkpaW5TN+7d6+RZD744ANTuXJl88wzz7iUOX78uImOjjZVq1Y1O3bscJmfnZ1t3n33XbNlyxbHNE/yAlbwmjGkypUrp9DQUKe/5EgXrtR44oknFBMTo+DgYF155ZWaOnWq41aptLQ0xcXFKS4uzumy/ePHjys6OlqtW7d2uoSwsG6++WZJF64uyc8XX3yhG264QWXKlFG5cuXUrVs3p8sDn3nmGcetW7Vq1XJcCrdv375817t06VI1bdpUoaGhqlSpkvr16+d029lNN93k+ItS8+bNnW7B8mbvv/++tm/frrFjx7q9laNs2bKaMGFCsW5zzJgxysrK0qRJk/Itd/DgQb311lu65ZZb9Nhjj7nM9/f315NPPqkaNWpIunB1x/nz5zV8+HBJ0oEDB/TMM8+oZ8+e+V6JUVj2q5n27NnjMm/jxo3at2+fevXqpV69ejmuNCgOBw8e1Ny5c3XzzTe7vd1y6NChat++vf7zn/8U2za9GbHJWXHHpuHDh+uaa67Rww8/rHPnzikrK8txlWHuW8o8kZaWpuXLl6tXr17q0aOH0tLS9OGHHxZ6eXfnn/3/bdq0cSnv7++vihUrFrm+OfXq1Uv169fXc8895/a2YE/s2bNHNWrUcHuFUpUqVS5q3bnl3mdt2rRRzZo13d46lJGRoWXLlql9+/b5/hV6ypQpOn36tObOnetylYR04a94w4YNK6YWoLilpKTor7/+0rFjx/TLL7/ooYce0unTp52upjHGqGvXrnr11Vd122236ZVXXtGVV16pESNGOL5fQ0ND9fbbb2v37t0aO3asY9mhQ4cqJSVFCxYskL+/v8f1s39W8zt3P//8c9166606evSonnnmGQ0fPlybNm1SmzZtHPHyzjvvVO/evSVJr776quM2scqVK+e53gULFqhHjx7y9/fXxIkTNWjQIH3wwQdq27at4xafsWPHOq7Ctt/+OGTIkALbVbNmTbVq1UqLFi1yTFu9erVSUlLUq1evApf3VEZGhv766y+XV87vPunCFevvv/++Y1/17t1bX3zxhcv4TitXrlTdunULvOLenePHj7vUg/F1vBd9LGfe+Ptv8+bNWrt2rQYPHqw777zTbZlmzZqpb9++Rd7GFVdcoZiYGMdVT3YbN25UmzZt1Lp1a7fzGjRo4HT12R9//KGvv/7a8fsoKSlJmzZtcsyPjIzU9OnTtXHjRv3nP/+RJC1fvlwff/yxJk2a5LafUVz27Nkjf39/XX/99S7zypYtq5CQEJfp8fHxKl++vG6//Xbdfffdio+Pdykza9YsHT58WNOmTXN7lb7NZlPv3r3d3l3gLUotIUUnpXQ6Kfb9nvP1999/u5Q7d+6cS7nz58+7lMv9xX/ixIl8t//RRx9Jkvr3719gXfOTlZXltvNz5swZl7K1atXSPffcozlz5uQ7TsTq1auVmZlZ6LrVrFlTzz77rN5991199tlnevTRRxUQEKBp06YVtVlO7J+h8uXLu8yLj49XnTp11Lx5c3Xp0kVhYWFOHc+LsXr1amVlZbncspnTPffco8zMTK1Zs6ZYtulNiE3WxqaAgADNnj1bSUlJev755/XGG29o27ZtmjlzpsLCwlzKp6amuj33cyduPvroI50+fVq9evVS1apVddNNN7n9Is+Lu/PPntSJj49XZmZmodflKX9/fz399NPavn27li9fflHrio2N1YEDB/TFF18UqrwnsTW33PvMZrOpT58++umnn/TLL784lV2zZo2OHz9eYAf2448/Vu3atdW6detC1V8q/I9jlLwOHTqocuXKqlKliho2bKgFCxZo3rx5uuWWWxxlPvroI33xxRd6/vnnNWfOHA0dOlQfffSR7r77bk2fPt0R81q2bKmRI0dq+vTp+vrrr7Vs2TItXrxYEydOVP369QtVH3v8OHz4sFatWqVhw4bJZrPprrvuynOZESNGqEKFCtq8ebNGjBihcePGad26dUpJSXEkza+55hrHWGh33HGH+vXrp379+uV5W3tGRoZGjRqlhg0bav369Xrsscc0ceJELV26VPv27dOrr74q6cLwCfbbkTt27Kh+/fq5vT3ZnT59+mjFihWOz318fLzatWtXIreufvrpp6pcubLLa/r06U7lVq5cqZMnTzqSYnfccYcCAwOdxgA8deqUDh065HZsy5MnTxZ4Tl955ZUu9XD3AxClgz6W9/z+++uvvwo1JuzHH38sSYW+LTens2fPut3u2bNnXcq2bdtW3333neP3Znp6urZu3arWrVurdevW2rRpk6Ovd+LECf36668uFzcsWrRIZcqUUefOndWiRQvVqVPHpe9nvy1v1KhR2rt3r4YNG6bWrVsXaj9ejNjYWGVlZemdd94p9DLx8fG68847FRQUpN69e2vXrl3aunWrU5mPP/5YoaGheSYL81PYvECJs/qSLPslYrlfwcHBZsGCBU5lV6xYYSSZF154wWn63XffbWw2m9m9e7dj2lNPPWX8/PzM+vXrzdKlS40kp8v482K/ZHPevHnm2LFj5tChQ+aTTz4xNWvWNDabzXEpm7tLNps0aWKqVKli/v77b8e07du3Gz8/P6fL0T25ZDM9Pd1UqVLFNGzY0OnSvZUrVxpJZty4cY5pnlxul9d+t+/7nPIql7PteV2eHhsb67KunJeAX3vttSYyMrLA+trldcteXnUcMmSI2/2zZ88eExAQYB599FGn9eS8TPXxxx83kswPP/xQ6PplZGSYJk2amAoVKhhJ5q233ir0snb2z9azzz5rjh07Zo4cOWK+/vpr07x5c7eXE6enp5uKFSuasWPHOqb16dPHNG7c2O36Pb1l77HHHitwP2zbts1IMsOHDy90O70dsSl/JRWb7B555BETGBhowsPDTe/evfPcH3m9Dh8+7FS+c+fOpk2bNo73s2fPNgEBAebo0aNO5eyx7PfffzfHjh0z+/btM/PmzTOhoaGmcuXK5syZM46y2dnZjvgTFRVlevfubWbMmGH2799f6HbmlDs+5rzEPTMz09SrV880btzYcVl5UW5J+fnnn01oaKjjVsBhw4aZFStWOLXLztPY+vnnn5tjx46ZAwcOmGXLlpnKlSub4OBgx23Nxhjzyy+/GEnmqaeectpWr169TEhIiElJSXFMy30LRUpKipFkunXrVuj2xsbG5tmGvG4dRPGzf0ZmzJhhPvvsM/PZZ5+ZhQsXmttuu80EBAQ43fYxePBg4+/vb06dOuW0js2bNxvJ+dbx8+fPm0aNGplatWqZypUrm3bt2jnddpGXvOJH5cqVzX//+19Hudzx9NChQ25vIzHmwi0slSpVcrz3JJ5u2rTJSDJvvvmmy7y4uDjTtGlTx/ui9PW2bt1qjh49agICAsx7771nTp06ZUJDQ82cOXOMMe5vyckvHuUnNjbWtGzZ0nGcc75y3yr8z3/+0zRr1sxp2l133eU07cCBA0aS6devn8u2Gjdu7HT8ctbNHh/ff/99l3ps2rSpgD2HkkYfK3+l8fvP/irolr1//vOfRpI5efKkU7m0tDRz7NgxxyvnrXP2/VbQK2d/ZsaMGUaSY0gX+3fA/v37za+//mokmV9++cVpv8THxzvVqVGjRqZv376O92PGjDGVKlUyGRkZTuX27dtnypQpYypUqGACAwPNTz/9VOC+zM3TW/aOHDliKleubCSZuLg48+CDD5p3333XZb/afffdd0aS+eyzz4wxF/qgNWrUMMOGDXMqV758edOkSROX5U+dOuV0fHIOW+FJXsAKpTao+YwZMxx/0UpOTtbChQv1wAMPKCIiwpHhW7Vqlfz9/fXoo486LfvEE09o2bJlWr16teOWomeeeUYrV67UgAEDdPr0abVr185lufzcd999Tu8rV66st99+22kwzpwOHz6sxMREjRw5UhUqVHBMv+aaa3TLLbdo1apVhd52Tt99950j457z0r3bb79dcXFx+uSTTzweLDunnPvdzt1fELp16+Zyu1bOwd/s3n//fZUtW9bx3t3A5zmdOnVKERERnlTZrZo1a2rOnDku0+230+VWu3Zt9e/fX7Nnz9bo0aPdXpJ56tQpSfKofvarO1q0aKHrr79egwYNKvSyuY0fP97pFqXw8HC9/PLLjgFY7VavXq2///7b8ZcX6cJl7126dNEvv/zi9jh5wv7Em/z2g32efZ/5EmKTeyUdmyZMmKBly5bp7NmzjqsD3Bk3bpzT4Px2Odv6999/a+3atU7rueuuuzR06FC99957Gjp0qMvyV155pdP7Ro0aaf78+U5XadlsNq1du1ZTp07VwoULtWjRIi1atEhDhw5Vjx499NZbbxXbwKX2q6QGDBigFStW6J///GeR1tOgQQMlJibq+eef18qVK5WYmKjp06crPDxcr7zyikvM8iS25h5MtWbNmlq4cKFT2auvvlrXXnutFi9erBdffFHShVsxPvroI3Xu3Nnp+yO3osRk6cKVNPZBTHOqV6+eR+vBxWvRooVTrOrdu7euvfZaPfLII+rcubOCgoK0f/9+VatWzeU4X3XVVZLk9CS1oKAgzZs3T82bN1dISIjmz5/v0cD/9vjh7++vSpUq6aqrrnK5VSgn+7Zzxwd7/dauXaszZ854/ICP/NYbFxfn8jCFoqhcubI6dOigd999V2fPnlVWVpZLf6K4VKpUqcDBlU+ePKlVq1bpkUce0e7dux3T27Rpo/fff187d+5U/fr1HZ8Dd0+ceuutt5Samqrk5OQ8r9a48cYbGdTci9HHcq80fv9JF/ZpQbc22r+Lcw+4PWvWLKeHkjRo0EA///yzU5nBgwere/fuLuv873//63KlkP1qpw0bNqht27bauHGjqlevriuuuELGGFWoUEEbN27U1Vdf7XZA8x9//FE//fST04NZevfurRdffFFr1651GqzcPizEyJEjHVerlrSoqCht375dzz33nJYvX65Zs2Zp1qxZCgoK0tNPP62nn37a6fssPj5eUVFRat++vaQLfdCePXtq4cKFevnllx2/30+dOuV2MPT+/fs7DVUxdOhQvfHGG05lCpsXKGmllpCik+L5eoujk5J7v+elRo0ahXpyg6df/GXLli2Wx6mWKVPG4ydLPP3003rnnXc0adIkl8vI7XWTlOcjiPNivye3adOmF/VELHvQPnfunL744gu99tprbr8kFi5cqFq1aik4ONjRqatTp47CwsIUHx/v+NFXVPbzLb/9UJik1aWK2OT5eosjNpUtW1ZXXnml/vrrL0VFReVZrlGjRgWe+0uWLFFGRoauvfZapx8+LVu2VHx8vNuElD25fuzYMb322mtKSkpym2APDg7W2LFjNXbsWB0+fFhfffWVpk+frvfee0+BgYFauHChB63OX9++ffX888/rueee0x133FHk9dSvX1/vvPOOsrKy9Ouvv2rlypWaMmWKBg8erFq1ajntT09iq70jk5KSonnz5mn9+vVuHyfft29fPfnkk9q0aZNat26tFStW6OzZswXerlfUmFyYH8coHX5+fmrfvr2mT5+uXbt2FekPKGvXrpV0YWiBXbt2uX1CUF4KEz98SZ8+fTRo0CAdOXJEHTt2LNYnfXlq6dKlOn/+vF5++WW9/PLLLvPj4+P17LPPKjIyUtHR0S4/bKX/PcW5oHF44L3oY3m+3pL8/Ve+fPkCn7qZM0mc84m7d911lyORk1diq169em5jrrv2NGzYUOXKlXMkm+zjR0kXkjGtWrXSxo0bNWjQIG3cuFExMTG64oorHMsvXLhQZcqUUe3atR19v5CQEMeTlnM/Pc/++60wv4uLKvdnMTo6WjNnztSbb76pXbt2ae3atZo8ebLGjRun6OhoxxPTs7KytHjxYrVv395pPLOWLVvq5Zdf1rp16/SPf/xD0oXj4y6B/9xzzzkStzlvk8+psHmBkuY1g5rbOymHDx/Wrl27irSO3J0UT9g7Ke3bt1ejRo3yDUYouri4OKWkpOjAgQOWb7t27drq16+fZs+ercOHD7utmyT99NNPVldN0v+CdufOnfXKK6/o8ccf1+jRo/Xdd985ypw6dUoff/yxkpKSVK9ePcfr6quv1tmzZ/Xuu+9e9CDI9i/8H3/8Mc8y9nlXX331RW3rUkBsuvTYxwto06aN03myYcMGbd682W1S/MYbb1SHDh3Uu3dvffbZZwoNDVXfvn2VnZ2d53aio6MdDxWoV6+e3nvvvWIdW8p+lVRiYqJHA7Lnt75GjRrpqaeecoxN5cm4Wrm1aNFCHTp00F133aWPPvpIDRs2VJ8+fVw6Rr1795afn59jcPN3331X5cuXV6dOnfJdf9myZVWtWjW3P0xx6bKfI/bPSWxsrA4dOuSSePztt98c8+1+/PFHPffcc7r33nt17bXX6oEHHlBKSkqJ1dW+7d9//91l3m+//aZKlSo5fnh68iM4v/X+/vvvbh9CUBT//Oc/5efnp2+++UZ9+vQplnUWVXx8vBo2bKilS5e6vOxXctndfvvt2r17t7Zs2VKKNYYV6GN5P/vvo9zfxTExMerQoYM6dOjgdrxbT/n5+alVq1aOsaI2btzoNH5k69attWHDBsfYUjmvjjLGaNGiRTpz5oyuvvpqp77fvn379OGHH7pN2lwM+5VseY1PefbsWbcDlUsXvi/q16+vf/3rX1q/fr38/Pyc+mNffPGFDh8+rMWLFzu1pUePHpKc+25xcXH6/fffXcYDu+aaaxzHx9t5TUJKopNS0HqLs5NSWrp06SJJxXoVgSeefvppZWZmavLkyS7zOnbsKH9//1KrW25jx45VRESEnn76ace0Dz74QOfOndPMmTNdOnQvvPCC9u/f7/IUCk/Z90N+g+7997//VUBAgG677baL2talgth06cQm+xNVHnnkEZdzZMmSJQoKCnL71LecwsPDNX78eCUmJuq9994rcJuBgYG65pprHINpF6d+/fqpbt26evbZZy862ZyT/S9i7pLzRWEfhPXQoUMul4RXq1ZN7du319KlS5WcnKzPPvtMd999t4KCggpcb+fOnbVnzx5t3ry5WOqJ0pWRkaFPP/1UQUFBjj9+dOrUSVlZWS6fm1dffVU2m83x1NqMjAwNHDhQ1apV0/Tp07VgwQIlJyc73TJS3KKjo9WkSRO9/fbbTk9q+/nnn/Xpp586JVXtcbUwT3Rr1qyZqlSpolmzZjk9MGb16tXasWOHy1/yiyo8PFwzZ87UM8884+h/lYYDBw5o/fr16tGjh+6++26X17333qvdu3fr22+/lSSNHDlSYWFhuu+++5ScnOyyvuKMhSh99LG8u4/VuXNnSRf3B6zCatu2rY4fP66PPvpIR48edXqqcevWrbVnzx6tWrVKaWlpTgmpr776SgcPHtRzzz3n0vebPXu2zp49qxUrVhRrXfM7ZmfPntWBAwcKddxq166t8uXLO/XH4uPjVaVKFbcJ/N69e2v58uWORFjnzp0dT5a+VHlNQopOygVWdVJKy913361GjRppwoQJbn9gpKamOj0to7jVqVNH/fr101tvveXymOGYmBgNGjRIn376qV5//XWXZbOzs/Xyyy/r4MGDJVa/nMqVK6chQ4Zo7dq1SkxMlHQhkVe7dm09+OCDLh26J598UuHh4Rf9hRETE6N7771Xn3/+uWbOnOkyf9asWfriiy90//335zlmly8hNl1wqcQm++d/5MiRLudIjx491K5du0KdI3379lWNGjWckte7du3SH3/84VL25MmT2rx5s8qXL5/vE3SKIudVUvanlHri66+/dvsUHfs4F+5uDyiqm266SS1atNC0adN07tw5p3l9+/bV0aNHNWTIEGVkZBT68dAjR45UmTJl9MADD7j9Ybpnzx63t2DDO6xevVoLFy7UwoUL9corr6hVq1batWuXhg8f7rgls0uXLmrfvr3Gjh2rIUOG6M0339Qdd9yhJUuWaNiwYapTp44k6YUXXlBiYqLmzZuniIgIXXPNNRo3bpzmz59f5HFbCuOll17S33//rVatWmnq1Kl6/vnndfPNNysyMlLPPPOMo1zTpk0lXfhj0jvvvKPFixfn+XTKwMBATZ48WT/++KPatWun6dOna8yYMbr77rtVs2bNYv2OGDBggMaPH1/gGJ95WbdunV544QWXV86rJf7880/Hcc75sv8AtF+93bVrV7fb6NSpkwICAhyxuV69enr33Xe1d+9eXXnllRo6dKhmz56tt956S6NHj1a7du3k5+fntg+ybNkyt3VxFz9Q+uhjXeDNfaw2bdrolltu0ezZs/O8Wru4ksT2JNPkyZMVFhamJk2aOOa1aNFCAQEBmjJlilNZ6X+3640YMcKl7zdo0CDVq1ev2BNq//d//6egoCDNnDnT5Wr62bNnKzMz0/FZlaRvv/3W7XfCli1b9Pfffzv6Y2lpafrggw/UuXNntwn8Rx55RKmpqY4+4UMPPaSoqCg9/vjj2rlzp8v6L4UEfqldl7h69WpHpvvo0aN69913tWvXLo0ePdptJ2Xfvn1q3LixPv30U3344Yd67LHHXDop69atc+qkPP3007r77rsLvC2gqF566SV17NhRrVq10v3336+0tDS9/vrr+XZSevXqpcDAQHXp0sXt/cX2Tsq9996rdu3aqXfv3kpOTtb06dOLpZOSc7/n1Lp1a9WuXfui1l0YgYGB+uCDD9ShQwfdeOON6tGjh9q0aaPAwED98ssvjls5JkyYkO96UlJS8rySqaDHkto7i7///rvL+BUvv/yy9uzZo0cffdQRDMqXL68//vhDS5cu1W+//eZ4XLEVhg0bpmnTpmnSpEl65ZVX9OWXX+Y5WGNwcLBuvfVWLV26VK+99poCAwOLvN1XX31Vv/32mx5++GGtWbPGcSXU2rVr9eGHH6pdu3Zux4DwBcSm0olNhfX111+7JDukC5cmX3PNNYqPj1eTJk0UExPjdvmuXbvqX//6l7Zt2+Z4TLs7gYGBGjZsmEaMGOE4B7Zv364+ffqoY8eOuuGGG1ShQgX9+eefevvtt3Xo0CFNmzatRAaDtI8lZU9Me2Ly5Mn6/vvvdeedd+qaa66RJG3btk3//e9/VaFCBT322GNO5S8mtkrSiBEj1L17dy1YsEAPPvigY/pdd92lhx9+WB9++KFiYmJ04403Fqr+derU0bvvvquePXvqqquu0j333KOGDRsqPT1dmzZt0tKlSzVw4ECnZew/jnMLDw+/qLG44Llx48Y5/h8SEqK4uDjNnDnT6fHafn5++uijjzRu3DgtWbJE8+fPV82aNfXSSy/piSeekHThM/viiy/qkUcecQzwKkmjR4/Whx9+qEGDBumXX34pkTGSOnTooDVr1mj8+PEaN26cAgMD1a5dO02ePNlp/KrmzZvr+eef16xZs7RmzRplZ2crKSkpz7FkBg4cqLCwME2aNEmjRo1SmTJl9M9//lOTJ08u1bGecluzZo3WrFnjMr1mzZqO8WMSExPVv39/lzKxsbG64447FB8fryuuuEKNGzd2u41y5cqpbdu2WrJkiV555RUFBASoW7du+umnn/Tyyy/r008/1bx582Sz2RQbG6vbb79dDz74oNv1PfTQQ2638eWXX+Y7PiGsQR/Lu/tYeVm4cKFuu+023XHHHerYsaPjNr0jR47o888/1/r1652SL0XVokULBQUFafPmzbrpppucbqEMCwtT48aNtXnzZpUrV84Rf86fP6/3339ft9xyS563yHXt2lXTp0/X0aNHVaVKlYuupyRVqVLF8Xm78cYb1bVrV4WFhWnTpk1atGiR/vGPfzhdmfrOO+8oPj5e//znP9W0aVMFBQVpx44dmjdvnkJCQjRmzBhJ0kcffaTU1NQ8E/jXX3+9KleurPj4ePXs2VMVKlTQ8uXL1aVLFzVu3Fi9evVS8+bNFRgYqAMHDmjp0qWS5DTell1p5wUcrH6sn7vHDIaEhJgmTZqYmTNnujy+NzU11Tz++OOmWrVqJjAw0NSrV8+89NJLjnLff/+9CQgIMP/617+clsvMzDTNmzc31apVc3oMZW65HzOdF3eP/TTGmM8//9y0adPGhIaGmrJly5ouXbqYX3/91WX5559/3lSvXt34+fkV6hGgS5YsMddee60JDg42FSpUMH379jUHDx50KlOcj/3M2S7levSvO4V9/Hhe6zpx4oQZN26cadSokQkLCzMhISGmYcOG5qm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FCxaUeD29RWpqqh577DHFxsYqNDRUrVu31tatW/Nd5vz58xo7dqxiY2MVHBysmjVrat68eRbV2FVR2hAfH6/GjRsrLCxM0dHRuu+++/T3339bVOOCP6fGGI0bN07R0dEKDQ1Vhw4dtGvXrgLXW9Q4WhQl0YaJEyeqefPmioiIUJUqVXTHHXfo999/L7E2SCV3LOwmTZokm82mxx57rHgr7kXoS3lHX8oX+lESfSn6UsWPGOUdMUryjThFjCJGyRSTxYsXm6CgIDNv3jzzyy+/mEGDBply5cqZ5ORkt+U3btxo/P39zZQpU8yvv/5qnn76aRMYGGh++umn4qqSxzxtQ58+fcyMGTPMDz/8YHbs2GEGDhxoIiMjzcGDBy2uuTNP22GXlJRkqlevbm644QbTrVs3ayqbB0/bcP78edOsWTPTqVMns2HDBpOUlGQSEhJMYmKixTX/H0/bEB8fb4KDg018fLxJSkoya9euNdHR0ebxxx+3uOb/s2rVKjN27FjzwQcfGElm+fLl+Zbfu3evCQsLM8OHDze//vqref31142/v79Zs2aNNRUuZT169DBXX321+eqrr8yuXbvM+PHjTdmyZfONCV27djUtW7Y0n332mUlKSjKbNm0yGzZssLDWzjxtw4YNG4yfn5+ZPn262bt3r/n6669NgwYNzD//+U/L6lzQ53TSpEkmMjLSrFixwmzfvt107drV1KpVy6SlpeW5zqLGUW9qw6233mrmz59vfv75Z5OYmGg6depkrrjiCnP69OkSaUNJtcNuy5YtpmbNmuaaa64xw4YNK5kGlDL6Ut7Rl/KFfpQx9KXoSxU/YpR3xChjfCNOEaOIUcYYU2wJqRYtWpihQ4c63mdlZZlq1aqZiRMnui3fo0cPc/vttztNa9mypRkyZEhxVcljnrYht8zMTBMREWHefvvtkqpioRSlHZmZmaZ169bmP//5jxkwYECpByhP2zBz5kxTu3Ztk56eblUVC+RpG4YOHWpuvvlmp2nDhw83bdq0KdF6FlZhAtTIkSNNgwYNnKb17NnT3HrrrSVYM+9w9uxZ4+/vb1auXOk0/brrrjNjx451u8zq1atNZGSk+fvvv62oYoGK0oaXXnrJ1K5d22naa6+9ZqpXr15i9cxP7s9pdna2qVq1qnnppZcc006ePGmCg4PNokWL8lzPxX4fXIziakNuR48eNZLMV199VZzVzVNxtiM1NdXUq1fPfPbZZ6Zdu3Y+m5CiL+UdfSlf6EcZQ1/Kjr5U8SFGeUeMMsY34hQx6oLLPUYVyy176enp+v7779WhQwfHND8/P3Xo0EGbN292u8zmzZudykvSrbfemmf5klaUNuR29uxZZWRkqEKFCiVVzQIVtR3PPfecqlSpovvvv9+KauarKG346KOP1KpVKw0dOlRRUVFq2LChXnzxRWVlZVlVbSdFaUPr1q31/fffOy7z3Lt3r1atWqVOnTpZUufi4G3ntZUyMzOVlZWlkJAQp+mhoaHasGGD22U++ugjNWvWTFOmTFH16tVVv359Pfnkk0pLS7Oiyi6K0oZWrVrpwIEDWrVqlYwxSk5O1rJly7zmc5uUlKQjR444fS4jIyPVsmXLPD+XxfF9UJyK0gZ3UlJSJKnUvqMuph1Dhw7V7bff7hJffAl9qQtKuy/lC/0oib4UfaniR4y6oLRjlOQbcYoYRYyyCyiOSv3111/KyspSVFSU0/SoqCj99ttvbpc5cuSI2/JHjhwpjip5rChtyG3UqFGqVq1aqXaYi9KODRs2aO7cuUpMTLSghgUrShv27t2rL774Qn379tWqVau0e/duPfzww8rIyND48eOtqLaTorShT58++uuvv9S2bVsZY5SZmakHH3xQY8aMsaLKxSKv8/rUqVNKS0tTaGhoKdWs5EVERKhVq1Z6/vnnddVVVykqKkqLFi3S5s2bVbduXbfL7N27Vxs2bFBISIiWL1+uv/76Sw8//LD+/vtvzZ8/3+IWFK0Nbdq0UXx8vHr27Klz584pMzNTXbp00YwZMyyuvXv27xRPvm+K4/ugOBWlDbllZ2frscceU5s2bdSwYcNir2NhFLUdixcv1rZt2wocy+xSR1/qgtLuS/lCP0qiL0VfqvgRoy4o7Rgl+UacIkYRo+x4yl4xmTRpkhYvXqzly5e7XF3gzVJTU9W/f3/NmTNHlSpVKu3qFFl2draqVKmi2bNnq2nTpurZs6fGjh2rWbNmlXbVCi0hIUEvvvii3nzzTW3btk0ffPCBPvnkEz3//POlXTUU0jvvvCNjjKpXr67g4GC99tpr6t27t/z83Ifa7Oxs2Ww2xcfHq0WLFurUqZNeeeUVvf3226V2lZSnbfj11181bNgwjRs3Tt9//73WrFmjffv26cEHH7S45sjP0KFD9fPPP2vx4sWlXRWPHDhwQMOGDVN8fPwl9d2KorkU+1K+0o+S6EsBBbkUY5TkO3GKGOWbiuUKqUqVKsnf31/JyclO05OTk1W1alW3y1StWtWj8iWtKG2wmzp1qiZNmqTPP/9c11xzTUlWs0CetmPPnj3at2+funTp4piWnZ0tSQoICNDvv/+uOnXqlGylcynKsYiOjlZgYKD8/f0d06666iodOXJE6enpCgoKKtE651aUNvz73/9W//799cADD0iSGjVqpDNnzmjw4MEaO3ZsngkBb5LXeV22bFmfvjrKrk6dOvrqq6905swZnTp1StHR0erZs6dq167ttnx0dLSqV6+uyMhIx7SrrrpKxhgdPHhQ9erVs6rqDp62YeLEiWrTpo1GjBghSbrmmmtUpkwZ3XDDDXrhhRcUHR1tZfVd2M+35ORkp7okJyerSZMmbpe5mO+DklCUNuT0yCOPaOXKlVq/fr1q1KhRUtUsUFHa8f333+vo0aO67rrrHNOysrK0fv16vfHGGzp//rxT3L+U0Zfyjr6UL/SjJPpS9KWKHzHKO2KU5BtxihhFjLIrlhYHBQWpadOmWrdunWNadna21q1bp1atWrldplWrVk7lJemzzz7Ls3xJK0obJGnKlCl6/vnntWbNGjVr1syKqubL03bExcXpp59+UmJiouPVtWtXtW/fXomJiYqJibGy+pKKdizatGmj3bt3O4KrJO3cuVPR0dGWByepaG04e/asSxCyB9wLY8x5P287r0tLmTJlFB0drRMnTmjt2rXq1q2b23Jt2rTRoUOHdPr0ace0nTt3ys/Pr1QTB1Lh2+Dtn9tatWqpatWqTp/LU6dO6dtvv83zc1nU74OSUpQ2SBf2/yOPPKLly5friy++UK1atayobp6K0o7/+7//c/mOatasmfr27avExESfSUZJ9KW8pS/lC/0oib5UTt70nVQY3nZe2xGjvCNGSb4Rp4hR/3PZx6giDYXuxuLFi01wcLBZsGCB+fXXX83gwYNNuXLlzJEjR4wxxvTv39+MHj3aUX7jxo0mICDATJ061ezYscOMHz/eKx4D6kkbJk2aZIKCgsyyZcvM4cOHHa/U1NTSaoIxxvN25OYNT13wtA1//PGHiYiIMI888oj5/fffzcqVK02VKlXMCy+8UFpN8LgN48ePNxEREWbRokVm79695tNPPzV16tQxPXr0KK0mmNTUVPPDDz+YH374wUgyr7zyivnhhx/M/v37jTHGjB492vTv399R3v4Y0BEjRpgdO3aYGTNmeM2jiq2wZs0as3r1asfxa9y4sWnZsqXjaSC591dqaqqpUaOGufvuu80vv/xivvrqK1OvXj3zwAMPlFYTPG7D/PnzTUBAgHnzzTfNnj17zIYNG0yzZs1MixYtLKtzQZ/TSZMmmXLlypkPP/zQ/Pjjj6Zbt26mVq1aJi0tzbGOm2++2bz++uuO9wWdv5dCGx566CETGRlpEhISnL6jzp49WyJtKKl25ObLT9mjL+UdfSlf6EcZQ1+KvlTxI0Z5R4wyxjfiFDGKGGWMMcWWkDLGmNdff91cccUVJigoyLRo0cJ88803jnnt2rUzAwYMcCr/3nvvmfr165ugoCDToEED88knnxRndYrEkzbExsYaSS6v8ePHW1/xXDw9Fjl5Q4AyxvM2bNq0ybRs2dIEBweb2rVrmwkTJpjMzEyLa+3MkzZkZGSYZ555xtT5f+3deVRTVx4H8G/YxRg4LCWoKCCBCLjV0iJaEWGKW6scUUCpMFiEcSHgguWoFZex2lMFwXGKFYgMFJC6VauUcYRxQbRVtCKInanF0cHRKqsbEe784eQdHkkgIKa28/uck3Pk3vvu+72H7+aXy3s3Q4YwExMTZmdnxxYuXMjq6up0H/j/FBcXq/0/row7LCyMeXt7q2wzcuRIZmRkxBwdHVlmZqbO4/6l5OfnM0dHR2ZkZMTEYjFbtGgRq6+v5+rVna+qqirm5+fH+vTpwwYOHMiWLl36UicMutKTY0hJSWGurq6sT58+zNbWls2dO5fdunVLZzF39f+0ra2NrVmzhtnY2DBjY2Pm6+vLqqureX0MHjxYZezu7Pr9NRyDuv4AvNRr8mX9Ltr7LU9IMUa51KuSS/0W8ijGKJeiXKr30Rj1aoxRjP02xikao2iMEjD2K7k3jBBCCCGEEEIIIYT8Jrz6q2YRQgghhBBCCCGEkN8UmpAihBBCCCGEEEIIITpFE1KEEEIIIYQQQgghRKdoQooQQgghhBBCCCGE6BRNSBFCCCGEEEIIIYQQnaIJKUIIIYQQQgghhBCiUzQhRQghhBBCCCGEEEJ0iiakCCGEEEIIIYQQQohO0YQUIYQQQgghhBBCCNEpmpB6ycLDwyEQCFRekyZNAgDY29ur1A0cOJDbXpv65ORk3j7Ly8sxa9Ys2NjYwMTEBBKJBJGRkbh+/ToA4KeffoJAIMClS5d4P6t7lZWVAQDkcjkvbqX6+noIBAKUlJTwyouLizFlyhRYWlrC1NQUrq6uWLZsGW7fvo358+dj2LBhaGlp4W1z9OhRGBkZ4eLFi52e047xWlhYwNvbG6dOnVLbPioqCvr6+igoKODKNB2v8pWYmKhynpT27NkDDw8PmJqaol+/fvD29saRI0c6jZmQVwljDH5+fvD391ep27lzJ8zNzZGdna3x+rhz5w5vm1u3bsHIyAju7u5q99d+W5FIBA8PDxw6dIjXprW1FZs3b4ZUKkWfPn1gYWGBt956C7t379bqmMLDwzFjxgzezwKBAJs3b+a1O3jwIAQCgVZ9AsDnn3+OESNGQCgUwtzcHKNGjcLHH3/M1ScmJqo9R1KplGszYcIErtzExATOzs74+OOPwRhDS0sLrKysVOJU2rBhA2xsbKBQKCCXy2Fubs6rb2lpwSeffIIRI0bA1NQUVlZWGDt2LDIzM6FQKHjnQtP7ECGka8rrKDo6WqVu0aJFEAgECA8P59p2HI/a/9yRulxP3fgFAP7+/tDX18e3336rtq/y8nIEBQXB1tYWxsbGGDx4MKZNm4bDhw+DMQZAu7yPENJzmq75kpISCAQC1NfX8/6txBjD559/jjFjxkAkEkEoFMLNzQ0ymQz/+Mc/uHaJiYkYOXKkSv8dP7vY2tqqjCMffvih2s9uEyZMwPvvv88r6zjePH36FG5ubliwYIHKvuPj4+Hg4ICmpqZOzoz2sQNd52DtSaVSGBsbq+SoSsXFxZg2bRqsra1hYmKCIUOGICgoCCdPnuTaKH8n2uS+pPfQhJQOTJo0CbW1tbxXbm4uV79+/XpeXXl5OW/7rurbO3LkCDw9PfH06VPk5OSgqqoK2dnZMDMzw5o1azqN8/jx4ypxjh49mqs3MDDA8ePHUVxc3Gk/aWlp8PPzg1gsxr59+1BZWYnPPvsMDQ0N2Lp1K5KSktDU1IS1a9dy29TX1yMyMhJr1qzB66+/3mn/HeM9efIk+vfvj2nTpuE///kPr82jR4+Ql5eH+Ph4ZGRkcOXtjzE5ORkikYhXtnz5crX7XL58OaKiohAUFITvv/8e58+fx7hx4zB9+nTs2LFDq7gJ+aUJBAJkZmbi3LlzSEtL48pv3LiB+Ph4pKamchPf1dXVKuPCa6+9xutPLpdj9uzZaGxsxLlz59TuMzMzE7W1tfjuu+8wduxYBAYG4sqVK1z9unXrkJSUhA0bNqCyshLFxcVYsGABL1HrLhMTE2zZsgV1dXU92j4jIwOxsbGIiYnBpUuXcObMGcTHx6O5uZnXzs3NTeUcnT59mtcmMjIStbW1qK6uRkJCAj766CN89tlnMDIyQmhoKDIzM1X2zxiDXC7HvHnzYGhoqFLf0tICf39/bN68GQsWLEBpaSnOnz+PRYsWITU1FVevXuXadvU+RAjpmp2dHfLy8vD48WOu7MmTJ/jiiy8waNCgF+q7Y65XW1uLJUuW8NrcvHkTpaWlWLx4MS+nUTp06BA8PT3R3NyMPXv2oKqqCoWFhQgICMDq1avR0NDAa99V3kcI0R3GGObMmYOYmBhMmTIFRUVFqKysRHp6OkxMTLBx48Zu9zlhwgS1Nw3Y2dnxyp88eYKysjJMnDiRK1M33hgbGyMrKwtyuRzffPMN17asrAxJSUmQy+Xo169ft+NUR9scDABOnz6Nx48fIzAwEHv27FGp37lzJ3x9fWFpaYn8/HxUV1fjwIED8PLyQlxcnEp7bXJf0osYeanCwsLY9OnTNdYPHjyYJSUl9Ur9w4cPmZWVFZsxY4batnV1dYwxxm7cuMEAsPLycrU/q5OZmcnMzMxYZGQke/PNN3l9AmDFxcWMMcb+9a9/MSMjIxYbG9tpDCdOnGCGhoasrKyMMfb8PHl4eDCFQqExBiV18X7//fcMADt06BCvrVwuZ56enqy+vp6Zmpqymzdvajy2rvZz9uxZBoClpKSotF26dCkzNDRU2z8hryq5XM6EQiH78ccfWVtbG/Px8WEBAQGMMcaKi4sZAO6a1aStrY05OjqywsJCtnLlShYZGanSBgA7cOAA93NjYyMDwLZv386VjRgxgiUmJvb4WDqOtWFhYWzatGlMKpWyFStWcOUHDhxg2r71TZ8+nYWHh3faZu3atWzEiBGdtvH29mYymYxX9vrrr3PnWjl+nTp1itdG+TuoqqpijKmOVVu2bGF6enrs4sWLKvtsaWlhzc3NjLGu34cIIV1TXkfu7u4sOzubK8/JyWHDhw9n06dPZ2FhYby2HbfVpKtcTykxMZEFBwezqqoqZmZmxh49esTVNTc3M0tLS25cUaetrY0xpl3eRwjpOU3XfPvcqmOelZubq/azjJLy+mVMc+7R8dpOS0tjQqGQ+3zV2NjIDA0N2Y4dO5i3tze33YkTJxgAduPGDa6ss/EmMTGRDRgwgNXV1bHHjx8zqVTK4uLitDo32sauTQ6mFB4ezj788EN27Ngx5uzszKurqalhhoaGGuNrf161zX1J76I7pH5DvvnmG/z888+Ij49XW9/xUY+eSExMxJUrV/Dll1+qrS8oKEBLS0uXMfj4+GDhwoUICwtDQUEB9u7di6ysLBgYGHQ7psePHyMrKwsAYGRkxKtLT09HaGgozMzMMHnyZMjl8m73r5SbmwuhUIioqCiVumXLlkGhUGDfvn097p8QXQsLC4Ovry8iIiKwY8cOVFRU8O6Y0kZxcTEePXoEPz8/hIaGIi8vDw8fPtTY/tmzZ0hPTwfAv17FYjFOnDiBe/fu9exg1NDX18emTZuQmpqKW7dudXt7sViMsrIy1NTU9FpMjDGcOnUK165d445/2LBh8PDwULnjITMzE15eXrzH/9rLycmBn58fRo0apVJnaGiIvn379lrchJDnIiIieHc0ZmRk4Pe///1L3y9jDJmZmQgNDYVUKoWTkxMvFysqKsL9+/c15l8AuvW4MiFEt3Jzc+Hi4oL33ntPbX1Prl8fHx80Nzdzj9ydOnUKzs7OmDlzJs6dO4cnT54AeJ7L2dvbw97eHkDX482qVasgFosRExOD1atXQyAQYNOmTd2OrzPa5mBNTU0oKChAaGgofve736GhoYG3jMu+ffugUCg0jo00Lv7yaEJKB44cOQKhUMh7tb9oV65cyatLSUnhbd9VvdIPP/wAABo/vHTFy8tLJc6O+vfvD5lMhlWrVuHZs2dqYxCJRLC1te1yf8pngIODg7Fp06Zux62Mt2/fvvj0008xevRo+Pr68mIpKytDUFAQAHCPxbD/raHQXdevX8eQIUNUJr2A5+dFJBJx63QR8muxa9cuVFRUIDY2Frt27YK1tTWvfuDAgbwxwc3NjVefnp6O4OBg6Ovrw93dHY6Ojrz12pRCQkIgFAphbGyMuLg42NvbY/bs2Vz9tm3bcO/ePYjFYgwfPhzR0dE4duzYCx9fQEAARo4cyXtEWFtr166Fubk57O3t4eLigvDwcOzduxdtbW28dleuXFEZOzuuM7Nz507u+MePH4+2tjbExMRw9fPnz0dBQQF3K3pTUxO+/PJLREREaIzvhx9+0Hrc7Op9iBCindDQUJw+fRo1NTWoqanBmTNnEBoa+sL9dsz1hEIh70PV8ePH8ejRI27tv9DQUG5yHwCXf7i4uHBl3377La+/jutdapP3EUJ6Rt377uTJkzW2v379Ou/6BYDY2Fhu2/ZrCAPqc4+OOZpEIsGAAQO4x/NKSkrg7e0NsViMQYMG4ezZs1y5j48Pt11X442BgQGysrJQUFCA1NRUZGVlwcTEpPsnqRPa5mB5eXmQSCRwc3ODvr4+goODVcZGkUgEsVjMle3bt4933tovIQF0nfuS3tX921FIt/n4+ODPf/4zr8zCwoL794oVK7iFMAHAysqK17areqWeTrQo5efnY+jQoV22W7lyJdLS0pCRkcH7QKmMQduZ5j59+mD58uWIi4uDTCbrUbxSqRQVFRWIj4+HXC7nrbOSkZEBf39/7nxNmTIF8+fPx4kTJ3gTV93xoueYkFfNa6+9hqioKBw8eFDtApynTp3irQfQ/hqrr6/H/v37eeslKZOW9mMWACQlJcHPzw8//vgj4uLikJKSwhsHXV1dUVFRgQsXLuDMmTM4efIk3n33XYSHh2u9sLkmW7ZswcSJEzWuDaeJra0tzp49i4qKCpw8eRKlpaUICwvD7t27UVhYCD2953/TcXFxwVdffcXbViQS8X6eO3cuVq1ahbq6OqxduxZeXl7w8vLi6kNCQhAXF4e9e/ciIiIC+fn50NPT4ybU1enOeNTV+xAhRDvW1taYOnUq5HI5GGOYOnWqxrysOzrmegAwYMAA7t8ZGRkICgri7iQPCQnBihUr8M9//hNDhgxR2+fw4cO5BYIlEonKHxK1zfsIId2n7n333Llz3ZrAXrVqFRYvXoz9+/er/BFJXe5x+/ZtTJgwgVemXEcqISEBJSUlWLFiBQDA29sbJSUl8PT0xLlz5xAZGclto8144+rqipkzZ6K+vh5vvPGG1sekLW1zsIyMDN45DQ0Nhbe3N1JTU7n8teNnU39/f1y6dIk7X62trbz6znJf0vtoQkoH+vbtCycnJ431VlZWL1Sv5OzsDAC4du0axowZ0+047ezstNqPubk5EhISsG7dOkybNk0lhoaGBtTW1mp1l5SBgQH09fV7dLuknZ0dJBIJl2QFBASgoqICxsbGaG1txZ49e3Dnzh3eY4Ctra3IyMjo0YSUs7MzTp8+jZaWFpW7pP7973+jsbGR+x0Q8mtiYGCg8XFZBwcHjY/7fvHFF3jy5Aneeustrowxhra2Nly/fp13PYjFYjg5OcHJyQmZmZmYMmUKKisreYtE6unpwcPDAx4eHoiNjUV2djbef/99rFq1Cg4ODj0+vvHjx8Pf3x8JCQkqH/i04e7uDnd3dyxcuBDR0dF4++238fe//537a6KRkVGXY6eZmRnXZu/evXBycoKnpyf8/PwAPJ/ACgwMRGZmJvdI0OzZszu9Y8HZ2RnXrl3T6hi6eh8ihGgvIiICixcvBgD86U9/6pU+O8v1Hjx4gAMHDkChUPA+4Cpzmj/+8Y+QSCQAni/G6+npCeD5AsSdXffa5n2EkO5T977b2fIBEokE1dXVvDJra2tYW1urXVBbXe6hLpfz8fGBTCbD/fv3UV5eDm9vbwDPJ6TS0tIwfvx4tLS0cAuaazPetN9fd5dbEYlEKl+wAID7EhszMzNeeWc5WGVlJcrKynD+/HmsXLmSF2teXh4iIyMhkUjQ0NCAO3fucHdJCYVCODk59Sj3Jb2PHtn7DXnnnXdgZWWFTz75RG39i3xbVUdLliyBnp4etm/fzisPDAyEkZGRTmLouF8DAwPs3LkTAHD06FE0NTWhvLwcly5d4l65ubnYv39/j+IIDg5Gc3Oz2jV2Pv30UxgaGmLmzJkveiiE/Gqkp6dj2bJlvGvs8uXLePvtt9V+A5TSm2++idGjR/OSGnVcXV0BoNM1qbS1efNmHD58mLs9vad6IyahUAiZTIbly5fz7nKaP38+Tp8+jSNHjqC0tBTz58/vtJ85c+bg+PHjar95VaFQ9Mp5I4SomjRpElpaWqBQKLhHWl6mnJwcDBw4EJcvX+aNt1u3boVcLkdrayveeecdWFhYYMuWLS89HkJI7wsJCUF1dTUOHTrUq/36+Pjg4cOH2LZtGyQSCTe5NX78eJw/fx7Hjh3jHu0DtBtvXoSLiwtu3bql8s3oFy9ehImJSaffWNoxB0tPT8f48eNVYl26dCn32F5gYCAMDQ1pbHyF0R1SOvD06VPcuXOHV2ZgYNArt3i317dvX+zevRuzZs3Ce++9h5iYGDg5OeHnn3/G3r17cfPmTeTl5Wnc/v79+ypxmpubq30m2MTEBOvWrcOiRYt45XZ2dkhKSsLixYvR2NiIefPmwd7eHrdu3UJWVhaEQiG2bt3aOwfcjkAgQExMDBITExEVFYX09HRMnToVI0aM4LVzdXVFXFwccnJyVGLvypgxYyCTybBixQq0tLRgxowZUCgUyM7Oxvbt25GcnAw7O7vePCxCfnF3797lFr1UsrS0xNWrV3Hx4kXk5OSorGMUEhKC9evXY+PGjRr/+hQbG4uAgADEx8djwIABCAwMxNixY+Hl5QWxWIwbN24gISEBzs7OPV4Xr71hw4Zh7ty5GtfgU+cPf/gD+vfvj4kTJ2LgwIGora3Fxo0bYW1tzbsL9dmzZypjp0AggI2Njca+o6KisGHDBuzbtw+BgYEAnieHTk5OmDdvHqRSKe+RPnViY2Px9ddfw9fXFxs2bMC4cePQr18/fPfdd9iyZQvS09MxcuRIALp7HyLk/4G+vj6qqqq4f2ujoaGBe3xOydLSkssbmpqaVK5RU1NTiEQipKenIzAwEO7u7rx6Ozs7JCQkoLCwEFOnTsXu3bsRFBSEqVOnIiYmBhKJBM3NzSgsLFQba3fyPkLIyxUcHIz9+/cjODgYCQkJ8Pf3h42NDWpqapCfn6/1WNORo6MjBg0ahNTUVMydO5crt7OzQ//+/bFr1y6EhIRw5dqONz3l7+8PFxcXhISEYOPGjRCLxbh48SJWr14NmUzGHWdXOZhCocBf/vIXrF+/XiXWDz74ANu2bcPVq1fh5uaGrVu3QiaT4cGDBwgPD4eDgwMePHiA7OxsAKpjo6bclx7deznoDikdKCwshK2tLe81bty4l7Kv6dOno7S0FIaGhpgzZw6kUilCQkLQ0NCAjRs3drqtn5+fSpwHDx7U2D4sLAyOjo4q5QsXLkRRURFu376NgIAASKVSfPDBBxCJRN1ew6U7wsLCoFAokJqaiq+//lrt3Up6enoICAjgLXbXHcnJydi5cydyc3Ph7u6ON954AydPnsTBgwexZMmSFz0EQl45Li4uKuPChQsXkJ6eDldXV7WTRQEBAbh79y6OHj2qsd9JkybBwcGBu0vK398fhw8fxrvvvgtnZ2eEhYVBKpWiqKioR9++qc769etVFsPsjJ+fH8rKyjBr1izuW2lMTEzwt7/9DZaWlly7q1evqpyjwYMHd9q3hYUF5s2bh8TERC4mgUCAiIgI1NXVdbqYuZKxsTH++te/Ij4+HmlpafD09ISHhwdSUlIQExPDS9B0+T5EyP8DkUikslZcZ0pKSjBq1Cjea926dVz9Rx99pHKNxsfH48KFC7h8+bLanMbMzAy+vr5cThMQEIDS0lKYmppi3rx5cHFxwcSJE3HixAnk5eWpLLPQ3byPEPLyCAQC5OfnIzk5GUePHoWvry9cXFwQEREBOzs73nqd3eXj44OmpiaV9aW8vb3R1NTELUHQnfGmpwwMDFBUVIRBgwYhJCQE7u7uWLt2LWQyGTZs2MC16yoH++qrr3D//n0EBASo7GPo0KEYOnQoF+uSJUtQVFSEe/fuITAwEBKJBFOmTMGNGzdQWFiIYcOG8bbXlPuSl0PAaJVmQgghhBBCCCGEEKJDdIcUIYQQQgghhBBCCNEpmpAir5zo6GgIhUK1r+jo6F86PEKIjt28eVPjmCAUCnHz5s0e9Tt58mSNfXb8emVCCCGEENI5Nzc3jblVTk7OLx0eeQXRI3vklXP37l00NjaqrROJRGq/+pQQ8tv17Nkz/PTTTxrr7e3te7TO1O3bt/H48WO1dRYWFrCwsOh2n4QQQggh/69qamqgUCjU1tnY2KBfv346joi86mhCihBCCCGEEEIIIYToFD2yRwghhBBCCCGEEEJ0iiakCCGEEEIIIYQQQohO0YQUIYQQQgghhBBCCNEpmpAihBBCCCGEEEIIITpFE1KEEEIIIYQQQgghRKdoQooQQgghhBBCCCGE6BRNSBFCCCGEEEIIIYQQnaIJKUIIIYQQQgghhBCiU/8FqH+hpoS7CjsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to detect and count outliers using IQR method\n",
        "def count_outliers(df, columns):\n",
        "    outlier_counts = {}\n",
        "    for col in columns:\n",
        "        # Calculate Q1, Q3 and IQR\n",
        "        Q1 = df[col].quantile(0.25)\n",
        "        Q3 = df[col].quantile(0.75)\n",
        "        IQR = Q3 - Q1\n",
        "\n",
        "        # Define outlier range\n",
        "        lower_bound = Q1 - 1.5 * IQR\n",
        "        upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "        # Count outliers\n",
        "        outlier_count = df[(df[col] < lower_bound) | (df[col] > upper_bound)].shape[0]\n",
        "\n",
        "        # Store outlier count for the column\n",
        "        outlier_counts[col] = outlier_count\n",
        "\n",
        "    return outlier_counts\n",
        "\n",
        "# Count outliers in the specified columns\n",
        "outlier_counts = count_outliers(train, columns_to_check)\n",
        "\n",
        "# Display the number of outliers in each column\n",
        "for col, count in outlier_counts.items():\n",
        "    print(f\"Number of outliers in '{col}': {count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I3HHrLlyMDRE",
        "outputId": "7673ca88-7089-401f-faad-74a62930d970"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers in 'PERFORMANCE_INDEX': 154\n",
            "Number of outliers in 'EMISSIONS_CO2': 88\n",
            "Number of outliers in 'URBAN_CONSUMPTION': 150\n",
            "Number of outliers in 'DISPLACEMENT': 9\n",
            "Number of outliers in 'CAPACITY': 890\n",
            "Number of outliers in 'PISTON_COUNT': 0\n",
            "Number of outliers in 'BLENDED_CONSUMPTION': 250\n",
            "Number of outliers in 'OUTPUT': 0\n",
            "Number of outliers in 'EFFICIENCY_RATIO': 216\n",
            "Number of outliers in 'YEARS_IN_SERVICE': 0\n",
            "Number of outliers in 'MILEAGE': 76\n",
            "Number of outliers in 'HIGHWAY_USAGE': 156\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the cleaned data to a CSV file\n",
        "train.to_csv('cleaned_train_data.csv', index=False)"
      ],
      "metadata": {
        "id": "PQrroimfX4n6"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# TEST DATA CLEANING"
      ],
      "metadata": {
        "id": "esDCy_OkLDFZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training Data Info:\")\n",
        "print(test.info())\n",
        "print(\"\\nMissing Values:\\n\", test.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gJXtha1GvoT",
        "outputId": "c5e9acb7-2868-44b1-e500-91ad9a40a7db"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2000 entries, 0 to 1999\n",
            "Data columns (total 16 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   ID                   2000 non-null   int64  \n",
            " 1   PERFORMANCE_INDEX    1997 non-null   float64\n",
            " 2   EMISSIONS_CO2        2000 non-null   float64\n",
            " 3   URBAN_CONSUMPTION    1998 non-null   float64\n",
            " 4   DISPLACEMENT         1997 non-null   float64\n",
            " 5   CAPACITY             2000 non-null   float64\n",
            " 6   PISTON_COUNT         1999 non-null   float64\n",
            " 7   BLENDED_CONSUMPTION  2000 non-null   float64\n",
            " 8   GEARBOX_TYPE         2000 non-null   object \n",
            " 9   OUTPUT               1999 non-null   float64\n",
            " 10  BODY_STYLE           2000 non-null   object \n",
            " 11  EFFICIENCY_RATIO     1996 non-null   float64\n",
            " 12  YEARS_IN_SERVICE     1999 non-null   float64\n",
            " 13  MILEAGE              2000 non-null   float64\n",
            " 14  HIGHWAY_USAGE        1997 non-null   float64\n",
            " 15  ENERGY_SOURCE        2000 non-null   object \n",
            "dtypes: float64(12), int64(1), object(3)\n",
            "memory usage: 250.1+ KB\n",
            "None\n",
            "\n",
            "Missing Values:\n",
            " ID                     0\n",
            "PERFORMANCE_INDEX      3\n",
            "EMISSIONS_CO2          0\n",
            "URBAN_CONSUMPTION      2\n",
            "DISPLACEMENT           3\n",
            "CAPACITY               0\n",
            "PISTON_COUNT           1\n",
            "BLENDED_CONSUMPTION    0\n",
            "GEARBOX_TYPE           0\n",
            "OUTPUT                 1\n",
            "BODY_STYLE             0\n",
            "EFFICIENCY_RATIO       4\n",
            "YEARS_IN_SERVICE       1\n",
            "MILEAGE                0\n",
            "HIGHWAY_USAGE          3\n",
            "ENERGY_SOURCE          0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling Missing Values:"
      ],
      "metadata": {
        "id": "5-XrwMlcL53S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Columns for different imputation strategies\n",
        "mean_cols = ['PERFORMANCE_INDEX', 'EFFICIENCY_RATIO']\n",
        "median_cols = ['URBAN_CONSUMPTION', 'DISPLACEMENT', 'OUTPUT', 'YEARS_IN_SERVICE', 'HIGHWAY_USAGE']\n",
        "mode_cols = ['PISTON_COUNT']\n",
        "\n",
        "# Mean imputation\n",
        "mean_imputer = SimpleImputer(strategy='mean')\n",
        "test[mean_cols] = mean_imputer.fit_transform(test[mean_cols])\n",
        "\n",
        "# Median imputation\n",
        "median_imputer = SimpleImputer(strategy='median')\n",
        "test[median_cols] = median_imputer.fit_transform(test[median_cols])\n",
        "\n",
        "# Mode imputation\n",
        "mode_imputer = SimpleImputer(strategy='most_frequent')\n",
        "test[mode_cols] = mode_imputer.fit_transform(test[mode_cols])\n",
        "\n",
        "# Verifying missing values after imputation\n",
        "print(\"Missing values after imputation:\\n\", test.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1aXp02aGvle",
        "outputId": "5742f4f7-5d97-411a-c7cd-40ed452292c0"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values after imputation:\n",
            " ID                     0\n",
            "PERFORMANCE_INDEX      0\n",
            "EMISSIONS_CO2          0\n",
            "URBAN_CONSUMPTION      0\n",
            "DISPLACEMENT           0\n",
            "CAPACITY               0\n",
            "PISTON_COUNT           0\n",
            "BLENDED_CONSUMPTION    0\n",
            "GEARBOX_TYPE           0\n",
            "OUTPUT                 0\n",
            "BODY_STYLE             0\n",
            "EFFICIENCY_RATIO       0\n",
            "YEARS_IN_SERVICE       0\n",
            "MILEAGE                0\n",
            "HIGHWAY_USAGE          0\n",
            "ENERGY_SOURCE          0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the mode value of the 'ENERGY_SOURCE' column\n",
        "mode_value = test['ENERGY_SOURCE'].mode()[0]\n",
        "\n",
        "# Replace specific values with the mode\n",
        "test['ENERGY_SOURCE'] = test['ENERGY_SOURCE'].replace([\"18748'\", \"14235'\"], mode_value)\n",
        "\n",
        "# Verify the changes by displaying the unique values in 'ENERGY_SOURCE'\n",
        "print(\"Unique values in 'ENERGY_SOURCE' after replacement:\", test['ENERGY_SOURCE'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_-Lgk_m-QAqs",
        "outputId": "8f107fe6-56f2-43e6-c6ad-3ae5e0845633"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in 'ENERGY_SOURCE' after replacement: ['Petrol' 'Gas' 'Diesel']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### One-Hot Encoding:"
      ],
      "metadata": {
        "id": "sGfaTSt8MNDA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Display unique values for specified columns\n",
        "print(\"Unique values in 'GEARBOX_TYPE':\", test['GEARBOX_TYPE'].unique())\n",
        "print(\"Unique values in 'BODY_STYLE':\", test['BODY_STYLE'].unique())\n",
        "print(\"Unique values in 'ENERGY_SOURCE':\", test['ENERGY_SOURCE'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7UY6IkvyGvgg",
        "outputId": "1da25271-715c-4dbb-a3e5-3eb603506708"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in 'GEARBOX_TYPE': ['Automatic' 'Manual' 'eMr']\n",
            "Unique values in 'BODY_STYLE': ['SUV - SMALL' 'COMPACT' 'SUV - STANDARD' 'SUBCOMPACT' 'MINICOMPACT'\n",
            " 'MID-SIZE' 'PICKUP TRUCK - STANDARD' 'TWO-SEATER' 'FULL-SIZE' 'MINIVAN'\n",
            " 'STATION WAGON - SMALL' 'VAN - CARGO' 'PICKUP TRUCK - SMALL'\n",
            " 'VAN - PASSENGER' 'STATION WAGON - MID-SIZE' 'SPECIAL PURPOSE VEHICLE']\n",
            "Unique values in 'ENERGY_SOURCE': ['Petrol' 'Gas' 'Diesel']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_cols = ['GEARBOX_TYPE', 'BODY_STYLE', 'ENERGY_SOURCE']\n",
        "\n",
        "# Encode categorical variables\n",
        "encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')\n",
        "encoded_test = pd.DataFrame(encoder.fit_transform(test[categorical_cols]))\n",
        "\n",
        "# Add back column names to the encoded dataframe\n",
        "encoded_test.columns = encoder.get_feature_names_out(categorical_cols)\n",
        "\n",
        "# Drop the original categorical columns and concatenate the encoded ones\n",
        "test = test.drop(columns=categorical_cols).reset_index(drop=True)\n",
        "test = pd.concat([test, encoded_train], axis=1)\n",
        "\n",
        "display(\"Data after one-hot encoding:\", test.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "GOjxRNMeGvdo",
        "outputId": "0b551540-62da-4056-c1e0-7a7f97edd258"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'Data after one-hot encoding:'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       ID  PERFORMANCE_INDEX  EMISSIONS_CO2  URBAN_CONSUMPTION  DISPLACEMENT  \\\n",
              "0  3100.0          94.076083          225.0               25.8           3.6   \n",
              "1  3101.0         112.582723          224.0               11.6           1.4   \n",
              "2  3102.0          84.515804          254.0               13.0           3.6   \n",
              "3  3103.0         202.285949          232.0               10.3           2.4   \n",
              "4  3104.0          63.658044          203.0               10.2           1.6   \n",
              "\n",
              "   CAPACITY  PISTON_COUNT  BLENDED_CONSUMPTION  OUTPUT  EFFICIENCY_RATIO  ...  \\\n",
              "0       4.0           4.0                 16.2   156.0              1.38  ...   \n",
              "1       5.0           4.0                  7.7    97.0              1.37  ...   \n",
              "2       7.0           6.0                 10.1   156.0              1.30  ...   \n",
              "3      13.0           4.0                 10.9   111.0              1.36  ...   \n",
              "4       4.0           6.0                  8.8   171.0              1.31  ...   \n",
              "\n",
              "   BODY_STYLE_STATION WAGON - SMALL  BODY_STYLE_SUBCOMPACT  \\\n",
              "0                               0.0                    0.0   \n",
              "1                               0.0                    0.0   \n",
              "2                               0.0                    0.0   \n",
              "3                               0.0                    0.0   \n",
              "4                               0.0                    0.0   \n",
              "\n",
              "   BODY_STYLE_SUV - SMALL  BODY_STYLE_SUV - STANDARD  BODY_STYLE_TWO-SEATER  \\\n",
              "0                     0.0                        0.0                    0.0   \n",
              "1                     1.0                        0.0                    0.0   \n",
              "2                     0.0                        0.0                    0.0   \n",
              "3                     0.0                        0.0                    0.0   \n",
              "4                     0.0                        0.0                    0.0   \n",
              "\n",
              "   BODY_STYLE_VAN - CARGO  BODY_STYLE_VAN - PASSENGER  ENERGY_SOURCE_Diesel  \\\n",
              "0                     0.0                         0.0                   0.0   \n",
              "1                     0.0                         0.0                   0.0   \n",
              "2                     0.0                         0.0                   0.0   \n",
              "3                     0.0                         0.0                   0.0   \n",
              "4                     0.0                         0.0                   0.0   \n",
              "\n",
              "   ENERGY_SOURCE_Gas  ENERGY_SOURCE_Petrol  \n",
              "0                0.0                   1.0  \n",
              "1                0.0                   1.0  \n",
              "2                1.0                   0.0  \n",
              "3                0.0                   1.0  \n",
              "4                0.0                   1.0  \n",
              "\n",
              "[5 rows x 35 columns]"
            ],
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              "\n",
              "  <div id=\"df-482cc391-042f-4279-9979-f8614630775f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>PERFORMANCE_INDEX</th>\n",
              "      <th>EMISSIONS_CO2</th>\n",
              "      <th>URBAN_CONSUMPTION</th>\n",
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              "      <th>EFFICIENCY_RATIO</th>\n",
              "      <th>...</th>\n",
              "      <th>BODY_STYLE_STATION WAGON - SMALL</th>\n",
              "      <th>BODY_STYLE_SUBCOMPACT</th>\n",
              "      <th>BODY_STYLE_SUV - SMALL</th>\n",
              "      <th>BODY_STYLE_SUV - STANDARD</th>\n",
              "      <th>BODY_STYLE_TWO-SEATER</th>\n",
              "      <th>BODY_STYLE_VAN - CARGO</th>\n",
              "      <th>BODY_STYLE_VAN - PASSENGER</th>\n",
              "      <th>ENERGY_SOURCE_Diesel</th>\n",
              "      <th>ENERGY_SOURCE_Gas</th>\n",
              "      <th>ENERGY_SOURCE_Petrol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3100.0</td>\n",
              "      <td>94.076083</td>\n",
              "      <td>225.0</td>\n",
              "      <td>25.8</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>16.2</td>\n",
              "      <td>156.0</td>\n",
              "      <td>1.38</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3101.0</td>\n",
              "      <td>112.582723</td>\n",
              "      <td>224.0</td>\n",
              "      <td>11.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.7</td>\n",
              "      <td>97.0</td>\n",
              "      <td>1.37</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3102.0</td>\n",
              "      <td>84.515804</td>\n",
              "      <td>254.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>10.1</td>\n",
              "      <td>156.0</td>\n",
              "      <td>1.30</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3103.0</td>\n",
              "      <td>202.285949</td>\n",
              "      <td>232.0</td>\n",
              "      <td>10.3</td>\n",
              "      <td>2.4</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.9</td>\n",
              "      <td>111.0</td>\n",
              "      <td>1.36</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3104.0</td>\n",
              "      <td>63.658044</td>\n",
              "      <td>203.0</td>\n",
              "      <td>10.2</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>8.8</td>\n",
              "      <td>171.0</td>\n",
              "      <td>1.31</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 35 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-482cc391-042f-4279-9979-f8614630775f')\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-482cc391-042f-4279-9979-f8614630775f button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-482cc391-042f-4279-9979-f8614630775f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "    }\n",
              "  }\n",
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              "\n",
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    {
      "cell_type": "markdown",
      "source": [
        "### Normalization"
      ],
      "metadata": {
        "id": "0T1IwbCcUBNg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Columns to normalize\n",
        "columns_to_normalize = [\n",
        "    'PERFORMANCE_INDEX', 'EMISSIONS_CO2', 'URBAN_CONSUMPTION',\n",
        "    'DISPLACEMENT', 'CAPACITY', 'BLENDED_CONSUMPTION', 'OUTPUT',\n",
        "    'EFFICIENCY_RATIO', 'MILEAGE', 'HIGHWAY_USAGE'\n",
        "]\n",
        "\n",
        "# Initialize the MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Apply Min-Max scaling to the specified columns\n",
        "test[columns_to_normalize] = scaler.fit_transform(test[columns_to_normalize])\n",
        "\n",
        "# Verify the result by checking the first few rows\n",
        "display(test[columns_to_normalize].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Vx4TmptHUCB5",
        "outputId": "bef6d2a2-ef97-4d08-bd4c-8df3fc3ef2fe"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   PERFORMANCE_INDEX  EMISSIONS_CO2  URBAN_CONSUMPTION  DISPLACEMENT  \\\n",
              "0           0.310386       0.326816           0.828125      0.440678   \n",
              "1           0.418612       0.324022           0.273438      0.067797   \n",
              "2           0.254478       0.407821           0.328125      0.440678   \n",
              "3           0.943193       0.346369           0.222656      0.237288   \n",
              "4           0.132503       0.265363           0.218750      0.101695   \n",
              "\n",
              "   CAPACITY  BLENDED_CONSUMPTION    OUTPUT  EFFICIENCY_RATIO  MILEAGE  \\\n",
              "0    0.1250             0.538462  0.323171          0.566667     0.30   \n",
              "1    0.1875             0.129808  0.143293          0.555556     0.35   \n",
              "2    0.3125             0.245192  0.323171          0.477778     0.45   \n",
              "3    0.6875             0.283654  0.185976          0.544444     0.55   \n",
              "4    0.1250             0.182692  0.368902          0.488889     0.40   \n",
              "\n",
              "   HIGHWAY_USAGE  \n",
              "0       0.237179  \n",
              "1       0.173077  \n",
              "2       1.000000  \n",
              "3       0.211538  \n",
              "4       0.224359  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PERFORMANCE_INDEX</th>\n",
              "      <th>EMISSIONS_CO2</th>\n",
              "      <th>URBAN_CONSUMPTION</th>\n",
              "      <th>DISPLACEMENT</th>\n",
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              "      <th>MILEAGE</th>\n",
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              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.310386</td>\n",
              "      <td>0.326816</td>\n",
              "      <td>0.828125</td>\n",
              "      <td>0.440678</td>\n",
              "      <td>0.1250</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.418612</td>\n",
              "      <td>0.324022</td>\n",
              "      <td>0.273438</td>\n",
              "      <td>0.067797</td>\n",
              "      <td>0.1875</td>\n",
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              "      <td>0.173077</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.440678</td>\n",
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              "      <td>0.245192</td>\n",
              "      <td>0.323171</td>\n",
              "      <td>0.477778</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.943193</td>\n",
              "      <td>0.346369</td>\n",
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              "      <td>0.237288</td>\n",
              "      <td>0.6875</td>\n",
              "      <td>0.283654</td>\n",
              "      <td>0.185976</td>\n",
              "      <td>0.544444</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.211538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.132503</td>\n",
              "      <td>0.265363</td>\n",
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              "      <td>0.101695</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.182692</td>\n",
              "      <td>0.368902</td>\n",
              "      <td>0.488889</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.224359</td>\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "            'suggestCharts', [key], {});\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(test[columns_to_normalize]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"PERFORMANCE_INDEX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31443047687616493,\n        \"min\": 0.13250317888888885,\n        \"max\": 0.9431926830409356,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.41861241228070173,\n          0.13250317888888885,\n          0.25447838409356727\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EMISSIONS_CO2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05114859988611529,\n        \"min\": 0.26536312849162014,\n        \"max\": 0.4078212290502793,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3240223463687151,\n          0.26536312849162014,\n          0.4078212290502793\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"URBAN_CONSUMPTION\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2576111903443346,\n        \"min\": 0.21875,\n        \"max\": 0.828125,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.2734375,\n          0.21875,\n          0.328125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DISPLACEMENT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17873120442663143,\n        \"min\": 0.06779661016949151,\n        \"max\": 0.44067796610169496,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.06779661016949151,\n          0.1016949152542373,\n          0.44067796610169496\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAPACITY\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23634588001486298,\n        \"min\": 0.125,\n        \"max\": 0.6875,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.1875,\n          0.6875,\n          0.125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BLENDED_CONSUMPTION\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15809195371176343,\n        \"min\": 0.1298076923076923,\n        \"max\": 0.5384615384615383,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1298076923076923,\n          0.1826923076923077,\n          0.24519230769230765\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OUTPUT\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09816410369990264,\n        \"min\": 0.14329268292682926,\n        \"max\": 0.36890243902439024,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.14329268292682926,\n          0.36890243902439024,\n          0.32317073170731714\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EFFICIENCY_RATIO\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04052129450846763,\n        \"min\": 0.47777777777777797,\n        \"max\": 0.5666666666666665,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5555555555555557,\n          0.48888888888888904,\n          0.47777777777777797\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MILEAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09617692030835673,\n        \"min\": 0.30000000000000004,\n        \"max\": 0.55,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3500000000000001,\n          0.4,\n          0.45000000000000007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HIGHWAY_USAGE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3534255210763091,\n        \"min\": 0.17307692307692307,\n        \"max\": 1.0000000000000002,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.17307692307692307,\n          0.2243589743589744,\n          1.0000000000000002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Columns to check for outliers\n",
        "columns_to_check = ['PERFORMANCE_INDEX', 'EMISSIONS_CO2', 'URBAN_CONSUMPTION',\n",
        "                   'DISPLACEMENT', 'CAPACITY', 'PISTON_COUNT',\n",
        "                   'BLENDED_CONSUMPTION', 'OUTPUT', 'EFFICIENCY_RATIO',\n",
        "                   'YEARS_IN_SERVICE', 'MILEAGE', 'HIGHWAY_USAGE']\n",
        "\n",
        "# Create a box plot for each column to visualize outliers\n",
        "plt.figure(figsize=(12, 8))\n",
        "for i, col in enumerate(columns_to_check, 1):\n",
        "    plt.subplot(3, 4, i)  # Create subplots in a grid of 3 rows and 4 columns\n",
        "    sns.boxplot(x=test[col])  # Create a boxplot for the column\n",
        "    plt.title(f'Box Plot of {col}')  # Title for each plot\n",
        "\n",
        "plt.tight_layout()  # Adjust layout for better spacing\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "5TP96k5yWcRq",
        "outputId": "29f1ce4b-15d9-4cba-a6a1-3809010be5f3"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to detect and count outliers using IQR method\n",
        "def count_outliers(df, columns):\n",
        "    outlier_counts = {}\n",
        "    for col in columns:\n",
        "        # Calculate Q1, Q3 and IQR\n",
        "        Q1 = df[col].quantile(0.25)\n",
        "        Q3 = df[col].quantile(0.75)\n",
        "        IQR = Q3 - Q1\n",
        "\n",
        "        # Define outlier range\n",
        "        lower_bound = Q1 - 1.5 * IQR\n",
        "        upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "        # Count outliers\n",
        "        outlier_count = df[(df[col] < lower_bound) | (df[col] > upper_bound)].shape[0]\n",
        "\n",
        "        # Store outlier count for the column\n",
        "        outlier_counts[col] = outlier_count\n",
        "\n",
        "    return outlier_counts\n",
        "\n",
        "# Count outliers in the specified columns\n",
        "outlier_counts = count_outliers(test, columns_to_check)\n",
        "\n",
        "# Display the number of outliers in each column\n",
        "for col, count in outlier_counts.items():\n",
        "    print(f\"Number of outliers in '{col}': {count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vU7IEdlPWmZa",
        "outputId": "8cb21190-4164-4007-affa-7c33504e51c3"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers in 'PERFORMANCE_INDEX': 75\n",
            "Number of outliers in 'EMISSIONS_CO2': 25\n",
            "Number of outliers in 'URBAN_CONSUMPTION': 72\n",
            "Number of outliers in 'DISPLACEMENT': 10\n",
            "Number of outliers in 'CAPACITY': 376\n",
            "Number of outliers in 'PISTON_COUNT': 59\n",
            "Number of outliers in 'BLENDED_CONSUMPTION': 96\n",
            "Number of outliers in 'OUTPUT': 0\n",
            "Number of outliers in 'EFFICIENCY_RATIO': 97\n",
            "Number of outliers in 'YEARS_IN_SERVICE': 0\n",
            "Number of outliers in 'MILEAGE': 35\n",
            "Number of outliers in 'HIGHWAY_USAGE': 79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the cleaned data to a CSV file\n",
        "test.to_csv('cleaned_test_data.csv', index=False)"
      ],
      "metadata": {
        "id": "nR_tWtzuXrZW"
      },
      "execution_count": 87,
      "outputs": []
    }
  ]
}