XGBoost_Regression.ipynb

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          "name": "stdout",
          "text": [
            "Saving accident cleaned data.csv to accident cleaned data.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Libraries"
      ],
      "metadata": {
        "id": "UB_Zy_aX__hf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error, confusion_matrix, classification_report\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n"
      ],
      "metadata": {
        "id": "XVe_Tq7G3ziZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load the Dataset\n"
      ],
      "metadata": {
        "id": "7BbDy8giADY7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('accident cleaned data.csv')\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "aB2pJaYc3zlv",
        "outputId": "9dcede1b-3a0d-4ad7-9d69-34132ef1ee78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Accident_Index  Longitude   Latitude  Accident_Severity  \\\n",
              "0       200901BS70001  -0.201349  51.512273                  2   \n",
              "1       200901BS70002  -0.199248  51.514399                  2   \n",
              "2       200901BS70003  -0.179599  51.486668                  3   \n",
              "3       200901BS70004  -0.203110  51.507804                  2   \n",
              "4       200901BS70005  -0.173445  51.482076                  2   \n",
              "...               ...        ...        ...                ...   \n",
              "898550  2014984136414  -3.417278  55.264773                  2   \n",
              "898551  2014984137514  -3.230255  55.054855                  3   \n",
              "898552  2014984137714  -3.230826  54.985668                  3   \n",
              "898553  2014984138414  -3.191397  54.990446                  2   \n",
              "898554  2014984139614  -3.348426  55.106700                  3   \n",
              "\n",
              "        Number_of_Vehicles  Number_of_Casualties        Date  Day_of_Week  \\\n",
              "0                        2                     1  2009-01-01            5   \n",
              "1                        2                    11  2009-01-05            2   \n",
              "2                        2                     1  2009-01-04            1   \n",
              "3                        2                     1  2009-01-05            2   \n",
              "4                        2                     1  2009-01-06            3   \n",
              "...                    ...                   ...         ...          ...   \n",
              "898550                   2                     1  2014-12-07            1   \n",
              "898551                   2                     2  2014-12-11            5   \n",
              "898552                   1                     1  2014-12-09            3   \n",
              "898553                   3                     2  2014-12-17            4   \n",
              "898554                   2                     2  2014-12-24            4   \n",
              "\n",
              "            Time  Local_Authority_(District) Local_Authority_(Highway)  \\\n",
              "0       15:11:00                          12                 E09000020   \n",
              "1       10:59:00                          12                 E09000020   \n",
              "2       14:19:00                          12                 E09000020   \n",
              "3       08:10:00                          12                 E09000020   \n",
              "4       17:25:00                          12                 E09000020   \n",
              "...          ...                         ...                       ...   \n",
              "898550  22:20:00                         917                 S12000006   \n",
              "898551  06:40:00                         917                 S12000006   \n",
              "898552  02:00:00                         917                 S12000006   \n",
              "898553  06:55:00                         917                 S12000006   \n",
              "898554  15:00:00                         917                 S12000006   \n",
              "\n",
              "        Speed_limit  Light_Conditions  Weather_Conditions  \\\n",
              "0                30                 1                   1   \n",
              "1                30                 1                   1   \n",
              "2                30                 1                   1   \n",
              "3                30                 1                   8   \n",
              "4                30                 4                   1   \n",
              "...             ...               ...                 ...   \n",
              "898550           70                 6                   3   \n",
              "898551           70                 6                   1   \n",
              "898552           40                 4                   1   \n",
              "898553           60                 6                   2   \n",
              "898554           60                 1                   1   \n",
              "\n",
              "        Road_Surface_Conditions  Special_Conditions_at_Site  \\\n",
              "0                             1                           0   \n",
              "1                             2                           0   \n",
              "2                             1                           0   \n",
              "3                             4                           0   \n",
              "4                             1                           0   \n",
              "...                         ...                         ...   \n",
              "898550                        3                           0   \n",
              "898551                        3                           0   \n",
              "898552                        4                           0   \n",
              "898553                        2                           0   \n",
              "898554                        2                           0   \n",
              "\n",
              "        Urban_or_Rural_Area  \n",
              "0                         1  \n",
              "1                         1  \n",
              "2                         1  \n",
              "3                         1  \n",
              "4                         1  \n",
              "...                     ...  \n",
              "898550                    2  \n",
              "898551                    2  \n",
              "898552                    2  \n",
              "898553                    2  \n",
              "898554                    2  \n",
              "\n",
              "[898555 rows x 17 columns]"
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              "      <th>Accident_Index</th>\n",
              "      <th>Longitude</th>\n",
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              "      <td>2009-01-04</td>\n",
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              "      <th>898553</th>\n",
              "      <td>2014984138414</td>\n",
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              "      <td>917</td>\n",
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              "      <th>898554</th>\n",
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              "      <td>55.106700</td>\n",
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              "      <td>2</td>\n",
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          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Dreprocessing"
      ],
      "metadata": {
        "id": "me-GTMPuAQ5Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
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      "metadata": {
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          "height": 313
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        "id": "mwSmPd6wAPmP",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Accident_Index  Longitude   Latitude  Accident_Severity  Number_of_Vehicles  \\\n",
              "0  200901BS70001  -0.201349  51.512273                  2                   2   \n",
              "1  200901BS70002  -0.199248  51.514399                  2                   2   \n",
              "2  200901BS70003  -0.179599  51.486668                  3                   2   \n",
              "3  200901BS70004  -0.203110  51.507804                  2                   2   \n",
              "4  200901BS70005  -0.173445  51.482076                  2                   2   \n",
              "\n",
              "   Number_of_Casualties        Date  Day_of_Week      Time  \\\n",
              "0                     1  2009-01-01            5  15:11:00   \n",
              "1                    11  2009-01-05            2  10:59:00   \n",
              "2                     1  2009-01-04            1  14:19:00   \n",
              "3                     1  2009-01-05            2  08:10:00   \n",
              "4                     1  2009-01-06            3  17:25:00   \n",
              "\n",
              "   Local_Authority_(District) Local_Authority_(Highway)  Speed_limit  \\\n",
              "0                          12                 E09000020           30   \n",
              "1                          12                 E09000020           30   \n",
              "2                          12                 E09000020           30   \n",
              "3                          12                 E09000020           30   \n",
              "4                          12                 E09000020           30   \n",
              "\n",
              "   Light_Conditions  Weather_Conditions  Road_Surface_Conditions  \\\n",
              "0                 1                   1                        1   \n",
              "1                 1                   1                        2   \n",
              "2                 1                   1                        1   \n",
              "3                 1                   8                        4   \n",
              "4                 4                   1                        1   \n",
              "\n",
              "   Special_Conditions_at_Site  Urban_or_Rural_Area  \n",
              "0                           0                    1  \n",
              "1                           0                    1  \n",
              "2                           0                    1  \n",
              "3                           0                    1  \n",
              "4                           0                    1  "
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              "      <td>2009-01-04</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>200901BS70005</td>\n",
              "      <td>-0.173445</td>\n",
              "      <td>51.482076</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2009-01-06</td>\n",
              "      <td>3</td>\n",
              "      <td>17:25:00</td>\n",
              "      <td>12</td>\n",
              "      <td>E09000020</td>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97b64e16-e616-45a6-8b7e-410f48066fb3')\"\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-97b64e16-e616-45a6-8b7e-410f48066fb3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-97b64e16-e616-45a6-8b7e-410f48066fb3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0532a0bd-0c14-4d3e-88d4-d00c189c8e8f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0532a0bd-0c14-4d3e-88d4-d00c189c8e8f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    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",
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              "    animation:\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-0532a0bd-0c14-4d3e-88d4-d00c189c8e8f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "id": "KueHiqlsupfl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "ca7a4dc5-5f6d-4e4f-ac49-ee802cfa473d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Accident_Index  Longitude   Latitude  Accident_Severity  \\\n",
              "898550  2014984136414  -3.417278  55.264773                  2   \n",
              "898551  2014984137514  -3.230255  55.054855                  3   \n",
              "898552  2014984137714  -3.230826  54.985668                  3   \n",
              "898553  2014984138414  -3.191397  54.990446                  2   \n",
              "898554  2014984139614  -3.348426  55.106700                  3   \n",
              "\n",
              "        Number_of_Vehicles  Number_of_Casualties        Date  Day_of_Week  \\\n",
              "898550                   2                     1  2014-12-07            1   \n",
              "898551                   2                     2  2014-12-11            5   \n",
              "898552                   1                     1  2014-12-09            3   \n",
              "898553                   3                     2  2014-12-17            4   \n",
              "898554                   2                     2  2014-12-24            4   \n",
              "\n",
              "            Time  Local_Authority_(District) Local_Authority_(Highway)  \\\n",
              "898550  22:20:00                         917                 S12000006   \n",
              "898551  06:40:00                         917                 S12000006   \n",
              "898552  02:00:00                         917                 S12000006   \n",
              "898553  06:55:00                         917                 S12000006   \n",
              "898554  15:00:00                         917                 S12000006   \n",
              "\n",
              "        Speed_limit  Light_Conditions  Weather_Conditions  \\\n",
              "898550           70                 6                   3   \n",
              "898551           70                 6                   1   \n",
              "898552           40                 4                   1   \n",
              "898553           60                 6                   2   \n",
              "898554           60                 1                   1   \n",
              "\n",
              "        Road_Surface_Conditions  Special_Conditions_at_Site  \\\n",
              "898550                        3                           0   \n",
              "898551                        3                           0   \n",
              "898552                        4                           0   \n",
              "898553                        2                           0   \n",
              "898554                        2                           0   \n",
              "\n",
              "        Urban_or_Rural_Area  \n",
              "898550                    2  \n",
              "898551                    2  \n",
              "898552                    2  \n",
              "898553                    2  \n",
              "898554                    2  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accident_Index</th>\n",
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              "      <td>2014-12-11</td>\n",
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              "      <td>917</td>\n",
              "      <td>S12000006</td>\n",
              "      <td>70</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <th>898552</th>\n",
              "      <td>2014984137714</td>\n",
              "      <td>-3.230826</td>\n",
              "      <td>54.985668</td>\n",
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              "      <td>2014-12-09</td>\n",
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              "      <th>898553</th>\n",
              "      <td>2014984138414</td>\n",
              "      <td>-3.191397</td>\n",
              "      <td>54.990446</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2014-12-17</td>\n",
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              "      <td>06:55:00</td>\n",
              "      <td>917</td>\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c3e9e622-6f09-41d9-8e78-eab31f6313d8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-53e024bb-58ad-456c-8a5c-e624331ba3f6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-53e024bb-58ad-456c-8a5c-e624331ba3f6')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\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-53e024bb-58ad-456c-8a5c-e624331ba3f6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "id": "y839SONbupjH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3220d5f-0e7a-4c94-efe5-e4c87723feac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 898555 entries, 0 to 898554\n",
            "Data columns (total 17 columns):\n",
            " #   Column                      Non-Null Count   Dtype  \n",
            "---  ------                      --------------   -----  \n",
            " 0   Accident_Index              898555 non-null  object \n",
            " 1   Longitude                   898555 non-null  float64\n",
            " 2   Latitude                    898555 non-null  float64\n",
            " 3   Accident_Severity           898555 non-null  int64  \n",
            " 4   Number_of_Vehicles          898555 non-null  int64  \n",
            " 5   Number_of_Casualties        898555 non-null  int64  \n",
            " 6   Date                        898555 non-null  object \n",
            " 7   Day_of_Week                 898555 non-null  int64  \n",
            " 8   Time                        898555 non-null  object \n",
            " 9   Local_Authority_(District)  898555 non-null  int64  \n",
            " 10  Local_Authority_(Highway)   898555 non-null  object \n",
            " 11  Speed_limit                 898555 non-null  int64  \n",
            " 12  Light_Conditions            898555 non-null  int64  \n",
            " 13  Weather_Conditions          898555 non-null  int64  \n",
            " 14  Road_Surface_Conditions     898555 non-null  int64  \n",
            " 15  Special_Conditions_at_Site  898555 non-null  int64  \n",
            " 16  Urban_or_Rural_Area         898555 non-null  int64  \n",
            "dtypes: float64(2), int64(11), object(4)\n",
            "memory usage: 116.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe(include='all')"
      ],
      "metadata": {
        "id": "yiUQ4xQlgS0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "bf78f081-5aee-4f07-d46b-3373746ed283"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Accident_Index      Longitude       Latitude  Accident_Severity  \\\n",
              "count          898555  898555.000000  898555.000000      898555.000000   \n",
              "unique         898555            NaN            NaN                NaN   \n",
              "top     200901BS70001            NaN            NaN                NaN   \n",
              "freq                1            NaN            NaN                NaN   \n",
              "mean              NaN      -1.393471      52.557925           2.838157   \n",
              "std               NaN       1.402066       1.451622           0.398710   \n",
              "min               NaN      -7.516225      49.912941           1.000000   \n",
              "25%               NaN      -2.312257      51.483382           3.000000   \n",
              "50%               NaN      -1.335049      52.211135           3.000000   \n",
              "75%               NaN      -0.197891      53.453312           3.000000   \n",
              "max               NaN       1.759398      60.662043           3.000000   \n",
              "\n",
              "        Number_of_Vehicles  Number_of_Casualties        Date    Day_of_Week  \\\n",
              "count        898555.000000         898555.000000      898555  898555.000000   \n",
              "unique                 NaN                   NaN        2191            NaN   \n",
              "top                    NaN                   NaN  2010-10-01            NaN   \n",
              "freq                   NaN                   NaN         698            NaN   \n",
              "mean              1.826297              1.343093         NaN       4.115010   \n",
              "std               0.710371              0.827161         NaN       1.919744   \n",
              "min               1.000000              1.000000         NaN       1.000000   \n",
              "25%               1.000000              1.000000         NaN       2.000000   \n",
              "50%               2.000000              1.000000         NaN       4.000000   \n",
              "75%               2.000000              1.000000         NaN       6.000000   \n",
              "max              67.000000             93.000000         NaN       7.000000   \n",
              "\n",
              "            Time  Local_Authority_(District) Local_Authority_(Highway)  \\\n",
              "count     898555               898555.000000                    898555   \n",
              "unique      1439                         NaN                       207   \n",
              "top     17:00:00                         NaN                 E10000016   \n",
              "freq        8889                         NaN                     25989   \n",
              "mean         NaN                  350.141962                       NaN   \n",
              "std          NaN                  260.007326                       NaN   \n",
              "min          NaN                    1.000000                       NaN   \n",
              "25%          NaN                  112.000000                       NaN   \n",
              "50%          NaN                  325.000000                       NaN   \n",
              "75%          NaN                  530.000000                       NaN   \n",
              "max          NaN                  941.000000                       NaN   \n",
              "\n",
              "          Speed_limit  Light_Conditions  Weather_Conditions  \\\n",
              "count   898555.000000     898555.000000       898555.000000   \n",
              "unique            NaN               NaN                 NaN   \n",
              "top               NaN               NaN                 NaN   \n",
              "freq              NaN               NaN                 NaN   \n",
              "mean        38.631375          1.936235            1.551820   \n",
              "std         13.959299          1.641881            1.598791   \n",
              "min         10.000000          1.000000            1.000000   \n",
              "25%         30.000000          1.000000            1.000000   \n",
              "50%         30.000000          1.000000            1.000000   \n",
              "75%         50.000000          4.000000            1.000000   \n",
              "max         70.000000          7.000000            9.000000   \n",
              "\n",
              "        Road_Surface_Conditions  Special_Conditions_at_Site  \\\n",
              "count             898555.000000               898555.000000   \n",
              "unique                      NaN                         NaN   \n",
              "top                         NaN                         NaN   \n",
              "freq                        NaN                         NaN   \n",
              "mean                   1.372541                    0.104764   \n",
              "std                    0.649301                    0.714385   \n",
              "min                    1.000000                    0.000000   \n",
              "25%                    1.000000                    0.000000   \n",
              "50%                    1.000000                    0.000000   \n",
              "75%                    2.000000                    0.000000   \n",
              "max                    5.000000                    7.000000   \n",
              "\n",
              "        Urban_or_Rural_Area  \n",
              "count         898555.000000  \n",
              "unique                  NaN  \n",
              "top                     NaN  \n",
              "freq                    NaN  \n",
              "mean               1.349956  \n",
              "std                0.476956  \n",
              "min                1.000000  \n",
              "25%                1.000000  \n",
              "50%                1.000000  \n",
              "75%                2.000000  \n",
              "max                2.000000  "
            ],
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              "\n",
              "  <div id=\"df-04cb9aa1-b1ed-4233-aae6-72a044e6c8b3\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accident_Index</th>\n",
              "      <th>Longitude</th>\n",
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              "      <th>Local_Authority_(Highway)</th>\n",
              "      <th>Speed_limit</th>\n",
              "      <th>Light_Conditions</th>\n",
              "      <th>Weather_Conditions</th>\n",
              "      <th>Road_Surface_Conditions</th>\n",
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              "      <th>unique</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>2.838157</td>\n",
              "      <td>1.826297</td>\n",
              "      <td>1.343093</td>\n",
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              "      <td>350.141962</td>\n",
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              "      <td>38.631375</td>\n",
              "      <td>1.936235</td>\n",
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              "      <td>1.372541</td>\n",
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              "      <td>0.710371</td>\n",
              "      <td>0.827161</td>\n",
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              "      <td>1.919744</td>\n",
              "      <td>NaN</td>\n",
              "      <td>260.007326</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.959299</td>\n",
              "      <td>1.641881</td>\n",
              "      <td>1.598791</td>\n",
              "      <td>0.649301</td>\n",
              "      <td>0.714385</td>\n",
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              "    <tr>\n",
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              "      <td>-2.312257</td>\n",
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          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.duplicated().sum()"
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          "metadata": {},
          "execution_count": 14
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    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
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        "colab": {
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          "height": 617
        },
        "outputId": "4f944853-0705-4a80-b7f7-346cae83b4da"
      },
      "execution_count": null,
      "outputs": [
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Urban_or_Rural_Area</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Date'] = pd.to_datetime(data['Date'])  # Convert Date to datetime"
      ],
      "metadata": {
        "id": "oI0jn9WHemYq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feature Selection and Scaling"
      ],
      "metadata": {
        "id": "HZ1PIdrtBHCT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xa93q5JKuvNI"
      },
      "outputs": [],
      "source": [
        "# ## Feature Selection\n",
        "# Selecting relevant features and target variable\n",
        "features = data[['Number_of_Vehicles', 'Speed_limit', 'Light_Conditions',\n",
        "                  'Weather_Conditions', 'Road_Surface_Conditions']].copy()  # Use copy to avoid SettingWithCopyWarning\n",
        "target = data['Number_of_Casualties']\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Feature Scaling\n",
        "# Scaling the numerical features\n",
        "scaler = StandardScaler()\n",
        "numerical_features = ['Number_of_Vehicles', 'Speed_limit',\n",
        "                     'Light_Conditions', 'Weather_Conditions',\n",
        "                     'Road_Surface_Conditions']\n",
        "features.loc[:, numerical_features] = scaler.fit_transform(features[numerical_features])  # Use .loc for assignment\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7Z-XxIf1VWw",
        "outputId": "a5632cfc-3bfc-4eb0-f298-e45511286fcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-3855e29224c7>:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[ 0.24452509  0.24452509  0.24452509 ... -1.16319165  1.65224183\n",
            "  0.24452509]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  features.loc[:, numerical_features] = scaler.fit_transform(features[numerical_features])  # Use .loc for assignment\n",
            "<ipython-input-17-3855e29224c7>:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[-0.61832471 -0.61832471 -0.61832471 ...  0.09804403  1.53078151\n",
            "  1.53078151]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  features.loc[:, numerical_features] = scaler.fit_transform(features[numerical_features])  # Use .loc for assignment\n",
            "<ipython-input-17-3855e29224c7>:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[-0.57022169 -0.57022169 -0.57022169 ...  1.25695241  2.47506849\n",
            " -0.57022169]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  features.loc[:, numerical_features] = scaler.fit_transform(features[numerical_features])  # Use .loc for assignment\n",
            "<ipython-input-17-3855e29224c7>:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[-0.34514872 -0.34514872 -0.34514872 ... -0.34514872  0.28032418\n",
            " -0.34514872]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  features.loc[:, numerical_features] = scaler.fit_transform(features[numerical_features])  # Use .loc for assignment\n",
            "<ipython-input-17-3855e29224c7>:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[-0.57375795  0.9663604  -0.57375795 ...  4.04659711  0.9663604\n",
            "  0.9663604 ]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
            "  features.loc[:, numerical_features] = scaler.fit_transform(features[numerical_features])  # Use .loc for assignment\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Train/Test Split"
      ],
      "metadata": {
        "id": "zjM3HtL3BOpx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Train/Test Split\n",
        "# Splitting the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
        "\n",
        "# ## Log Transformation\n",
        "# Applying log transformation to the target variable to handle skewness\n",
        "y_train = np.log1p(y_train)  # Log transformation\n",
        "y_test = np.log1p(y_test)\n"
      ],
      "metadata": {
        "id": "tuDBHnyJBSSJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Definition and Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "sr7C0gxBBaas"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Define the XGBoost Model\n",
        "# Initializing the XGBoost regressor with specified hyperparameters\n",
        "xgb_model = XGBRegressor(\n",
        "    subsample=0.8,\n",
        "    n_estimators=100,\n",
        "    min_child_weight=3,\n",
        "    max_depth=6,\n",
        "    learning_rate=0.05,\n",
        "    colsample_bytree=1.0\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "id": "ChwI0azcBSUM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Evaluation"
      ],
      "metadata": {
        "id": "OXSDgeppBh8A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Model Training\n",
        "# Fitting the model to the training data\n",
        "xgb_model.fit(X_train, y_train)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "HN2jZRTcBSWP",
        "outputId": "d95939e3-b24d-4b2a-f25d-51f8eec7bb12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=6, max_leaves=None,\n",
              "             min_child_weight=3, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=6, max_leaves=None,\n",
              "             min_child_weight=3, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=1.0, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.05, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=6, max_leaves=None,\n",
              "             min_child_weight=3, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Model Evaluation\n",
        "# Making predictions on the test set\n",
        "y_pred = xgb_model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "g2LHAxkP1qh9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Inverse Transformation\n",
        "# Inverse the log transformation to get the original scale of predictions\n",
        "y_pred_inverse = np.expm1(y_pred)  # Inverse of log1p\n",
        "y_test_inverse = np.expm1(y_test)  # Inverse of log1p\n"
      ],
      "metadata": {
        "id": "WD2GuWM91qw4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Cast Predictions to Integers\n",
        "# Rounding predictions and converting to integers\n",
        "y_pred_inverse_int = np.round(y_pred_inverse).astype(int)\n",
        "y_test_inverse_int = np.round(y_test_inverse).astype(int)\n"
      ],
      "metadata": {
        "id": "iSQG0Jsh1yEl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Calculate RMSE\n",
        "# Calculating the mean squared error and root mean squared error\n",
        "mse = mean_squared_error(y_test_inverse_int, y_pred_inverse_int)\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "# Output the results\n",
        "print(f\"XGBoost Regression - Best Params: {{'subsample': 0.8, 'n_estimators': 100, 'min_child_weight': 3, 'max_depth': 6, 'learning_rate': 0.05, 'colsample_bytree': 1.0}}\")\n",
        "print(f\"RMSE: {rmse:.4f}\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXllCXC214BN",
        "outputId": "bfe96d35-4e0a-4dfa-8511-67785991a921"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost Regression - Best Params: {'subsample': 0.8, 'n_estimators': 100, 'min_child_weight': 3, 'max_depth': 6, 'learning_rate': 0.05, 'colsample_bytree': 1.0}\n",
            "RMSE: 0.8861\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Confusion Matrix and Classification Report\n"
      ],
      "metadata": {
        "id": "Wd9FTxs7BqWS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Bin Predictions for Confusion Matrix\n",
        "# Defining bins for the predictions\n",
        "bins = [0, 1, 5, 10, 20, np.inf]  # Define your bins\n",
        "labels = [0, 1, 2, 3]  # Corresponding labels for the bins\n",
        "y_test_binned = np.digitize(y_test_inverse_int, bins) - 1  # Binned true values\n",
        "y_pred_binned = np.digitize(y_pred_inverse_int, bins) - 1  # Binned predicted values\n",
        "\n"
      ],
      "metadata": {
        "id": "agdkgCzEBSYN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Create Confusion Matrix\n",
        "# Generating the confusion matrix\n",
        "cm = confusion_matrix(y_test_binned, y_pred_binned)\n"
      ],
      "metadata": {
        "id": "UOuwisMS1-sy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Classification Report\n",
        "# Printing the classification report\n",
        "print(classification_report(y_test_binned, y_pred_binned, target_names=[f'{l}-{bins[i+1]}' for i, l in enumerate(labels)]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "23ogSfmD1-ym",
        "outputId": "b7d9da55-d0dc-4600-9340-a8000bdd4ca5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0-1       0.99      1.00      0.99    177916\n",
            "         1-5       0.25      0.00      0.00      1726\n",
            "        2-10       0.00      0.00      0.00        59\n",
            "        3-20       0.00      0.00      0.00        10\n",
            "\n",
            "    accuracy                           0.99    179711\n",
            "   macro avg       0.31      0.25      0.25    179711\n",
            "weighted avg       0.98      0.99      0.99    179711\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Display Confusion Matrix\n",
        "# Visualizing the confusion matrix\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
        "disp.plot(cmap=plt.cm.Blues)\n",
        "plt.title('Confusion Matrix for XGBoost Regression Predictions')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "voby4sQc1-vR",
        "outputId": "46258551-41c7-4679-c7ca-45375fc7421c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Save Model"
      ],
      "metadata": {
        "id": "igU4hsySNNXL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ## Save the Model\n",
        "import pickle\n",
        "\n",
        "# Save the trained model to a file\n",
        "model_filename = 'xgb_model.pkl'  # Specify your filename here\n",
        "with open(model_filename, 'wb') as file:\n",
        "    pickle.dump(xgb_model, file)\n",
        "\n",
        "print(f\"Model saved as {model_filename}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4l8BxItBSaP",
        "outputId": "ba61c548-2486-4692-8795-a88cfb11c5aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved as xgb_model.pkl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9uN4AfXq4WXu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import numpy as np\n",
        "\n",
        "# Load the saved model\n",
        "model_filename = 'xgb_model.pkl'  # Specify your filename here\n",
        "with open(model_filename, 'rb') as file:\n",
        "    loaded_model = pickle.load(file)\n",
        "\n",
        "# Get user inputs\n",
        "print(\"Please enter the following inputs:\")\n",
        "number_of_vehicles = float(input(\"Number of Vehicles: \"))\n",
        "speed_limit = float(input(\"Speed Limit: \"))\n",
        "light_conditions = float(input(\"Light Conditions (numeric value): \"))\n",
        "weather_conditions = float(input(\"Weather Conditions (numeric value): \"))\n",
        "road_surface_conditions = float(input(\"Road Surface Conditions (numeric value): \"))\n",
        "\n",
        "# Create a feature array for the prediction\n",
        "features = np.array([[number_of_vehicles, speed_limit, light_conditions, weather_conditions, road_surface_conditions]])\n",
        "\n",
        "# Predict using the loaded model\n",
        "predicted_log_casualties = loaded_model.predict(features)\n",
        "\n",
        "# Inverse transform the log transformation\n",
        "predicted_casualties = np.expm1(predicted_log_casualties)\n",
        "\n",
        "# Cast prediction to integer\n",
        "predicted_casualties_int = int(np.round(predicted_casualties[0]))\n",
        "\n",
        "# Print the result\n",
        "print(f\"Predicted Number of Casualties: {predicted_casualties_int}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwGQrXx_4Wja",
        "outputId": "1f132c56-8cfd-409d-b974-9901cb21e4c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please enter the following inputs:\n",
            "Number of Vehicles: 4\n",
            "Speed Limit: 50\n",
            "Light Conditions (numeric value): 3\n",
            "Weather Conditions (numeric value): 4\n",
            "Road Surface Conditions (numeric value): 2\n",
            "Predicted Number of Casualties: 2\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPsUnJLcKD+cDHi5IGbAVyc",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}