Mapping_Predictive_Model.ipynb

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    {
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        "<a href=\"https://colab.research.google.com/github/ahmedyasser7/DEPI_GP/blob/main/Mapping_Predictive_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# **DRIVER & CAR INVOLOVMENT IN THE ACCIDENT PREDICTIVE MODEL**  "
      ],
      "metadata": {
        "id": "ryZelyOdb67_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Uploading The Dataset"
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      "metadata": {
        "id": "LPQg2dt1bxVV"
      }
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    {
      "cell_type": "code",
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              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
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              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
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            ]
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Vechicles cleaned data.csv to Vechicles cleaned data.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Importing Libraries"
      ],
      "metadata": {
        "id": "DZiAzZ6gH8eZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the necessary Libraries\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.under_sampling import RandomUnderSampler\n",
        "from collections import Counter\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "-GYcdzwHCNav"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Loading the Data"
      ],
      "metadata": {
        "id": "dZrzLmX5IWUz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the 'Vechicles cleaned data.csv' dataset\n",
        "df = pd.read_csv('Vechicles cleaned data.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "cHVnKGSo-sC5",
        "outputId": "e9c6a3ee-0d07-45f9-aa30-efe90b06b54f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "        Accident_Index  Vehicle_Type  Was_Vehicle_Left_Hand_Drive?  \\\n",
              "0        200901BS70001             2                             1   \n",
              "1        200901BS70001             9                             1   \n",
              "2        200901BS70002            19                             1   \n",
              "3        200901BS70002            11                             1   \n",
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              "...                ...           ...                           ...   \n",
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              "1640703  2014984139614             9                             1   \n",
              "\n",
              "         Journey_Purpose_of_Driver  Sex_of_Driver  Age_of_Driver  \\\n",
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              "1                               15              1             31   \n",
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              "\n",
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              "        <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-1dcd9f52-156e-41cb-83f2-858cf6a4bf74 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7679d36c-a483-4240-951c-f14aa0dd2e16\">\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",
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              "\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('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7679d36c-a483-4240-951c-f14aa0dd2e16 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Exploring & Preprocessing the Data"
      ],
      "metadata": {
        "id": "7LYyfgXxIcmS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the first few rows\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lbBz2t8GNh7M",
        "outputId": "ed2d4a2c-7fde-43ef-c5cd-79e4f46daa5d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Accident_Index  Vehicle_Type  Was_Vehicle_Left_Hand_Drive?  \\\n",
              "0  200901BS70001             2                             1   \n",
              "1  200901BS70001             9                             1   \n",
              "2  200901BS70002            19                             1   \n",
              "3  200901BS70002            11                             1   \n",
              "4  200901BS70003             3                             1   \n",
              "\n",
              "   Journey_Purpose_of_Driver  Sex_of_Driver  Age_of_Driver  Age_of_Vehicle  \n",
              "0                         15              1             77              26  \n",
              "1                         15              1             31               2  \n",
              "2                          1              1             40               3  \n",
              "3                          1              1             38               1  \n",
              "4                         15              1             40               2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29e2fb11-aec0-4238-8b76-94e1082dc5e2\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "      <th>0</th>\n",
              "      <td>200901BS70001</td>\n",
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              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>200901BS70003</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>2</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-29e2fb11-aec0-4238-8b76-94e1082dc5e2 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-29e2fb11-aec0-4238-8b76-94e1082dc5e2');\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",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --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",
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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",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-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",
              "    }\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",
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              "      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-4344178f-2e67-4971-9dc7-42a7c6911e9b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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    {
      "cell_type": "code",
      "source": [
        "# Get information about the dataset\n",
        "df.info()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "h4k1VFPoIoxl",
        "outputId": "beacc002-6aaf-4553-8b90-e080b417cd0b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1640704 entries, 0 to 1640703\n",
            "Data columns (total 7 columns):\n",
            " #   Column                        Non-Null Count    Dtype \n",
            "---  ------                        --------------    ----- \n",
            " 0   Accident_Index                1640704 non-null  object\n",
            " 1   Vehicle_Type                  1640704 non-null  int64 \n",
            " 2   Was_Vehicle_Left_Hand_Drive?  1640704 non-null  int64 \n",
            " 3   Journey_Purpose_of_Driver     1640704 non-null  int64 \n",
            " 4   Sex_of_Driver                 1640704 non-null  int64 \n",
            " 5   Age_of_Driver                 1640704 non-null  int64 \n",
            " 6   Age_of_Vehicle                1640704 non-null  int64 \n",
            "dtypes: int64(6), object(1)\n",
            "memory usage: 87.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
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          "height": 300
        },
        "id": "gzGgcQxKIo08",
        "outputId": "07c0e101-e5cc-4f92-f32e-363e48424a81"
      },
      "execution_count": 7,
      "outputs": [
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          "data": {
            "text/plain": [
              "       Vehicle_Type  Was_Vehicle_Left_Hand_Drive?  Journey_Purpose_of_Driver  \\\n",
              "count  1.640704e+06                  1.640704e+06               1.640704e+06   \n",
              "mean   9.551219e+00                  1.004265e+00               7.035593e+00   \n",
              "std    8.388348e+00                  6.516478e-02               5.052299e+00   \n",
              "min    1.000000e+00                  1.000000e+00               1.000000e+00   \n",
              "25%    9.000000e+00                  1.000000e+00               2.000000e+00   \n",
              "50%    9.000000e+00                  1.000000e+00               6.000000e+00   \n",
              "75%    9.000000e+00                  1.000000e+00               1.500000e+01   \n",
              "max    9.800000e+01                  2.000000e+00               1.500000e+01   \n",
              "\n",
              "       Sex_of_Driver  Age_of_Driver  Age_of_Vehicle  \n",
              "count   1.640704e+06   1.640704e+06    1.640704e+06  \n",
              "mean    1.405757e+00   3.927871e+01    7.458439e+00  \n",
              "std     5.992807e-01   1.515689e+01    3.945962e+00  \n",
              "min     1.000000e+00   1.000000e+00    1.000000e+00  \n",
              "25%     1.000000e+00   2.800000e+01    5.000000e+00  \n",
              "50%     1.000000e+00   3.800000e+01    7.000000e+00  \n",
              "75%     2.000000e+00   4.800000e+01    9.000000e+00  \n",
              "max     3.000000e+00   1.000000e+02    1.110000e+02  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Vehicle_Type</th>\n",
              "      <th>Was_Vehicle_Left_Hand_Drive?</th>\n",
              "      <th>Journey_Purpose_of_Driver</th>\n",
              "      <th>Sex_of_Driver</th>\n",
              "      <th>Age_of_Driver</th>\n",
              "      <th>Age_of_Vehicle</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.640704e+06</td>\n",
              "      <td>1.640704e+06</td>\n",
              "      <td>1.640704e+06</td>\n",
              "      <td>1.640704e+06</td>\n",
              "      <td>1.640704e+06</td>\n",
              "      <td>1.640704e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.551219e+00</td>\n",
              "      <td>1.004265e+00</td>\n",
              "      <td>7.035593e+00</td>\n",
              "      <td>1.405757e+00</td>\n",
              "      <td>3.927871e+01</td>\n",
              "      <td>7.458439e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.388348e+00</td>\n",
              "      <td>6.516478e-02</td>\n",
              "      <td>5.052299e+00</td>\n",
              "      <td>5.992807e-01</td>\n",
              "      <td>1.515689e+01</td>\n",
              "      <td>3.945962e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.800000e+01</td>\n",
              "      <td>5.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.800000e+01</td>\n",
              "      <td>7.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>4.800000e+01</td>\n",
              "      <td>9.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.800000e+01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>1.110000e+02</td>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Vehicle_Type\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 580069.1929820444,\n        \"min\": 1.0,\n        \"max\": 1640704.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1640704.0,\n          9.551218867022936,\n          98.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Was_Vehicle_Left_Hand_Drive?\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 580076.105100072,\n        \"min\": 0.06516477759387772,\n        \"max\": 1640704.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.0042646327430176,\n          2.0,\n          0.06516477759387772\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Journey_Purpose_of_Driver\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 580073.8818555259,\n        \"min\": 1.0,\n        \"max\": 1640704.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1640704.0,\n          7.035592648034014,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex_of_Driver\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 580075.9568297351,\n        \"min\": 0.599280661307008,\n        \"max\": 1640704.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1640704.0,\n          1.4057569189811203,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age_of_Driver\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 580062.8543380799,\n        \"min\": 1.0,\n        \"max\": 1640704.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          39.27871023658137,\n          38.0,\n          1640704.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age_of_Vehicle\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 580069.1698146304,\n        \"min\": 1.0,\n        \"max\": 1640704.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.458439182204712,\n          7.0,\n          1640704.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 7
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      ]
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    {
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      "source": [
        "df.shape"
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      "metadata": {
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        "id": "qxGMF3fHI-7x",
        "outputId": "3594ff62-7162-4256-fc72-c2722fd3de87"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1640704, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
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    {
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      "source": [
        "# Check for missing values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "j-dx-cR8Iv-0",
        "outputId": "42ba1fb3-7990-4f71-a6c1-5c9369355380"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Accident_Index                  0\n",
              "Vehicle_Type                    0\n",
              "Was_Vehicle_Left_Hand_Drive?    0\n",
              "Journey_Purpose_of_Driver       0\n",
              "Sex_of_Driver                   0\n",
              "Age_of_Driver                   0\n",
              "Age_of_Vehicle                  0\n",
              "dtype: int64"
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              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Accident_Index</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Vehicle_Type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Was_Vehicle_Left_Hand_Drive?</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Journey_Purpose_of_Driver</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex_of_Driver</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age_of_Driver</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age_of_Vehicle</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 9
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      "cell_type": "code",
      "source": [
        "# Check for duplicate values\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QoyYe-I7IwCM",
        "outputId": "c60f512a-5290-486f-948f-c33e4da75212"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3113"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
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      "cell_type": "code",
      "source": [
        "# Check for duplicates in each column\n",
        "duplicate_columns = df.apply(lambda col: col.duplicated().sum())\n",
        "\n",
        "# Print columns with duplicates\n",
        "duplicate_columns[duplicate_columns > 0]"
      ],
      "metadata": {
        "id": "gT78TGhj02yA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "b1da798d-a89c-4b5c-a3f3-12c56823335f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Accident_Index                   742233\n",
              "Vehicle_Type                    1640684\n",
              "Was_Vehicle_Left_Hand_Drive?    1640702\n",
              "Journey_Purpose_of_Driver       1640697\n",
              "Sex_of_Driver                   1640701\n",
              "Age_of_Driver                   1640604\n",
              "Age_of_Vehicle                  1640613\n",
              "dtype: int64"
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      "source": [
        "# Handling Duplicate Values\n",
        "df = df.drop_duplicates(keep = 'last')\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hEfpgEaBrLfx",
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            "text/plain": [
              "(1637591, 7)"
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          "metadata": {},
          "execution_count": 12
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      "source": [
        "df['Was_Vehicle_Left_Hand_Drive?'].value_counts()"
      ],
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          "height": 178
        },
        "id": "P0wIDkdlCGqY",
        "outputId": "236fe5e1-0e0a-43e2-917b-a7c6e3cdd1eb"
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      "execution_count": 13,
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "Was_Vehicle_Left_Hand_Drive?\n",
              "1    1630600\n",
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        "df['Age_of_Driver'].value_counts()"
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          "output_type": "execute_result",
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            "text/plain": [
              "Age_of_Driver\n",
              "38     204965\n",
              "30      42958\n",
              "25      38053\n",
              "20      37953\n",
              "40      37730\n",
              "        ...  \n",
              "97         20\n",
              "2          18\n",
              "99         16\n",
              "1           3\n",
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        "outputId": "f555c992-9057-4997-e22d-fef1b26a7651"
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      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age_of_Vehicle\n",
              "7      532274\n",
              "8       94211\n",
              "9       93118\n",
              "6       91829\n",
              "10      89363\n",
              "        ...  \n",
              "99          1\n",
              "67          1\n",
              "100         1\n",
              "110         1\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create the target variable"
      ],
      "metadata": {
        "id": "AI_2EFm7JjxI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the target variable 'Accident_Occurred'\n",
        "df['Accident_involved'] = ((df['Was_Vehicle_Left_Hand_Drive?'] == 2) |\n",
        "                               ((df['Age_of_Driver'] >= 38) & (df['Age_of_Vehicle'] >= 6))).astype(int)\n"
      ],
      "metadata": {
        "id": "vAOZwQQ-C0SY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c53b22d7-602d-4dd7-8e45-e44ff4585d86"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-d4e34f37b5da>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Accident_involved'] = ((df['Was_Vehicle_Left_Hand_Drive?'] == 2) |\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
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      "metadata": {
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          "height": 423
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        "id": "VgdRuuy8KUnq",
        "outputId": "b02676f4-4151-4e0d-c24e-68afc2fafd64"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Accident_Index  Vehicle_Type  Was_Vehicle_Left_Hand_Drive?  \\\n",
              "0        200901BS70001             2                             1   \n",
              "1        200901BS70001             9                             1   \n",
              "2        200901BS70002            19                             1   \n",
              "3        200901BS70002            11                             1   \n",
              "4        200901BS70003             3                             1   \n",
              "...                ...           ...                           ...   \n",
              "1640699  2014984138414             9                             1   \n",
              "1640700  2014984138414            19                             1   \n",
              "1640701  2014984138414             9                             1   \n",
              "1640702  2014984139614            11                             1   \n",
              "1640703  2014984139614             9                             1   \n",
              "\n",
              "         Journey_Purpose_of_Driver  Sex_of_Driver  Age_of_Driver  \\\n",
              "0                               15              1             77   \n",
              "1                               15              1             31   \n",
              "2                                1              1             40   \n",
              "3                                1              1             38   \n",
              "4                               15              1             40   \n",
              "...                            ...            ...            ...   \n",
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              "1640701                          6              1             73   \n",
              "1640702                          1              1             40   \n",
              "1640703                          6              1             79   \n",
              "\n",
              "         Age_of_Vehicle  Accident_involved  \n",
              "0                    26                  1  \n",
              "1                     2                  0  \n",
              "2                     3                  0  \n",
              "3                     1                  0  \n",
              "4                     2                  0  \n",
              "...                 ...                ...  \n",
              "1640699              12                  0  \n",
              "1640700               4                  0  \n",
              "1640701               7                  1  \n",
              "1640702               4                  0  \n",
              "1640703              11                  1  \n",
              "\n",
              "[1637591 rows x 8 columns]"
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1640699</th>\n",
              "      <td>2014984138414</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1640700</th>\n",
              "      <td>2014984138414</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1640701</th>\n",
              "      <td>2014984138414</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>73</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1640702</th>\n",
              "      <td>2014984139614</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1640703</th>\n",
              "      <td>2014984139614</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>79</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1637591 rows × 8 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-3e84c833-a717-4ec6-bf31-f5d3acac4600')\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-3e84c833-a717-4ec6-bf31-f5d3acac4600 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-3e84c833-a717-4ec6-bf31-f5d3acac4600');\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",
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              "\n",
              "\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",
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              "      --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",
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              "      --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",
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              "    30% {\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",
              "    }\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-8370e7aa-ec9c-4962-a604-57cae448d5d3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f83818a0-95b0-4300-b945-7373a9cdaca2\">\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('df')\"\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_f83818a0-95b0-4300-b945-7373a9cdaca2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Accident_involved'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "X53w4mpQKWEy",
        "outputId": "80320bd3-68a9-474e-bbb7-c23bdf5f0256"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Accident_involved\n",
              "0    972133\n",
              "1    665458\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Accident_involved</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>972133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>665458</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Selection & Training"
      ],
      "metadata": {
        "id": "q7YXvBQhnf_N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train-Test Split"
      ],
      "metadata": {
        "id": "JMg-vSgPJwKS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define features (X) and target variable (y)\n",
        "X = df[['Was_Vehicle_Left_Hand_Drive?', 'Age_of_Driver', 'Age_of_Vehicle']]\n",
        "y = df['Accident_involved']\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "SotTB-anJpAD"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Apply Undersampling"
      ],
      "metadata": {
        "id": "iYqfHdegJ61o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply undersampling to balance the classes in the training set\n",
        "undersample = RandomUnderSampler(random_state=42)\n",
        "X_train_under, y_train_under = undersample.fit_resample(X_train, y_train)\n",
        "\n",
        "# Check the distribution of classes after undersampling\n",
        "print(f\"Class distribution before undersampling: {Counter(y_train)}\")\n",
        "print(f\"Class distribution after undersampling: {Counter(y_train_under)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_wN-yYpJpCw",
        "outputId": "5747f74a-1ea2-40f8-ed2a-4eb90c9741a7"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class distribution before undersampling: Counter({0: 777663, 1: 532409})\n",
            "Class distribution after undersampling: Counter({0: 532409, 1: 532409})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Train the Model"
      ],
      "metadata": {
        "id": "OoKMXDhBKA8F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Logistic Regression model\n",
        "log_reg = LogisticRegression(max_iter=2000, class_weight={0: 1, 1: 2})\n",
        "\n",
        "# Train the model using the undersampled data\n",
        "log_reg.fit(X_train_under, y_train_under)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "bCSBwwa2JpFA",
        "outputId": "0406482d-bc09-40cf-9d19-1d2da98cd049"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(class_weight={0: 1, 1: 2}, max_iter=2000)"
            ],
            "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>LogisticRegression(class_weight={0: 1, 1: 2}, max_iter=2000)</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;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(class_weight={0: 1, 1: 2}, max_iter=2000)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate the Model"
      ],
      "metadata": {
        "id": "qQ9ComH1KJed"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data\n",
        "score = log_reg.score(X_test, y_test)\n",
        "print(f\"Accuracy of Logistic Regression: {score:.2f}\")\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = log_reg.predict(X_test)\n",
        "\n",
        "# Convert predictions to labels\n",
        "y_pred_labels = ['Involved' if pred == 1 else 'Not Involved' for pred in y_pred]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnOBQiTEJpHR",
        "outputId": "997f9b8f-629e-40a9-d7bc-eda8eb789c70"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Logistic Regression: 0.83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualize Confusion Matrix"
      ],
      "metadata": {
        "id": "UD0iZLRxKRiK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(3, 2))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False,\n",
        "            xticklabels=['Not Involved', 'Involved'],\n",
        "            yticklabels=['Not Involved', 'Involved'])\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Classification Report\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred, target_names=['Not Involved', 'Involved']))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "x3HTGdGTJpJV",
        "outputId": "2c00c1a6-240d-44d5-8663-1cf284ad13d3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "Not Involved       0.99      0.73      0.84    194470\n",
            "    Involved       0.71      0.99      0.83    133049\n",
            "\n",
            "    accuracy                           0.83    327519\n",
            "   macro avg       0.85      0.86      0.83    327519\n",
            "weighted avg       0.87      0.83      0.83    327519\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save Model"
      ],
      "metadata": {
        "id": "8IBIavWIMoXI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "# Save the model to a file\n",
        "model_filename = 'Mapping_Model.pkl'\n",
        "with open(model_filename, 'wb') as file:\n",
        "    pickle.dump(log_reg, file)"
      ],
      "metadata": {
        "id": "9V-KizBkJpLY"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load & Predict\n"
      ],
      "metadata": {
        "id": "Crgmt3sdPTCv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "# Load the model from the file\n",
        "model_filename = 'Mapping_Model.pkl'\n",
        "with open(model_filename, 'rb') as file:\n",
        "    loaded_model = pickle.load(file)\n",
        "\n",
        "# Ask the user for input values\n",
        "print(\"Please enter the following details:\")\n",
        "was_vehicle_left_hand_drive = int(input(\"Was the vehicle left hand drive? (0 for No, 1 for Yes): \"))\n",
        "age_of_driver = int(input(\"Age of Driver: \"))\n",
        "age_of_vehicle = int(input(\"Age of Vehicle: \"))\n",
        "\n",
        "# Prepare the input for prediction (make sure the input is in the same format as your training data)\n",
        "user_input = [[was_vehicle_left_hand_drive, age_of_driver, age_of_vehicle]]\n",
        "\n",
        "# Make the prediction\n",
        "prediction = loaded_model.predict(user_input)\n",
        "\n",
        "# Convert the prediction to labels\n",
        "prediction_label = 'Involved' if prediction[0] == 1 else 'Not Involved'\n",
        "\n",
        "# Display the result\n",
        "print(f\"\\nPrediction: {prediction_label}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDdCrm_MGyfP",
        "outputId": "547b44c1-e6b1-46b2-f71b-960879414850"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please enter the following details:\n",
            "Was the vehicle left hand drive? (0 for No, 1 for Yes): 0\n",
            "Age of Driver: 28\n",
            "Age of Vehicle: 8\n",
            "\n",
            "Prediction: Not Involved\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}