ACCIDENT_SEVERITY_PREDICTIVE_MODEL.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# **ACCIDENT SEVERITY PREDICTIVE MODEL**"
      ],
      "metadata": {
        "id": "iPmobjux4VQA"
      }
    },
    {
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      "source": [
        "# Uploading The Dataset\n",
        "\n"
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        "from google.colab import files\n",
        "uploaded = files.upload()  # This will prompt you to upload the file"
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              "//      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",
              "\n",
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving accident cleaned data.csv to accident cleaned data.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
      "metadata": {
        "id": "OOJ25pDR4WqY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zbBUBBdc-D2g"
      },
      "outputs": [],
      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Loading the Data"
      ],
      "metadata": {
        "id": "6HLdJkIG4erX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the 'accident cleaned data.csv' dataset\n",
        "df =  pd.read_csv('/content/accident cleaned data.csv')\n",
        "\n",
        "# Check the first few rows\n",
        "df.head()"
      ],
      "metadata": {
        "id": "A4kMuqoKWSAq",
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          "base_uri": "https://localhost:8080/",
          "height": 313
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        "outputId": "e84c9229-3ffc-40db-c70e-ecee922b6959"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Accident_Index  Longitude   Latitude  Accident_Severity  Number_of_Vehicles  \\\n",
              "0  200901BS70001  -0.201349  51.512273                  2                   2   \n",
              "1  200901BS70002  -0.199248  51.514399                  2                   2   \n",
              "2  200901BS70003  -0.179599  51.486668                  3                   2   \n",
              "3  200901BS70004  -0.203110  51.507804                  2                   2   \n",
              "4  200901BS70005  -0.173445  51.482076                  2                   2   \n",
              "\n",
              "   Number_of_Casualties        Date  Day_of_Week      Time  \\\n",
              "0                     1  2009-01-01            5  15:11:00   \n",
              "1                    11  2009-01-05            2  10:59:00   \n",
              "2                     1  2009-01-04            1  14:19:00   \n",
              "3                     1  2009-01-05            2  08:10:00   \n",
              "4                     1  2009-01-06            3  17:25:00   \n",
              "\n",
              "   Local_Authority_(District) Local_Authority_(Highway)  Speed_limit  \\\n",
              "0                          12                 E09000020           30   \n",
              "1                          12                 E09000020           30   \n",
              "2                          12                 E09000020           30   \n",
              "3                          12                 E09000020           30   \n",
              "4                          12                 E09000020           30   \n",
              "\n",
              "   Light_Conditions  Weather_Conditions  Road_Surface_Conditions  \\\n",
              "0                 1                   1                        1   \n",
              "1                 1                   1                        2   \n",
              "2                 1                   1                        1   \n",
              "3                 1                   8                        4   \n",
              "4                 4                   1                        1   \n",
              "\n",
              "   Special_Conditions_at_Site  Urban_or_Rural_Area  \n",
              "0                           0                    1  \n",
              "1                           0                    1  \n",
              "2                           0                    1  \n",
              "3                           0                    1  \n",
              "4                           0                    1  "
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              "      <td>2009-01-04</td>\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4816d8f4-b388-4e75-8a9e-5af1aa74b6d0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4816d8f4-b388-4e75-8a9e-5af1aa74b6d0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Exploring & Preprocessing the Data"
      ],
      "metadata": {
        "id": "F7qXlq-Q4gq4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "ArrWstERlix3",
        "outputId": "d41f7326-df24-44e5-fa9f-83336fc13d2b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Accident_Index                0\n",
              "Longitude                     0\n",
              "Latitude                      0\n",
              "Accident_Severity             0\n",
              "Number_of_Vehicles            0\n",
              "Number_of_Casualties          0\n",
              "Date                          0\n",
              "Day_of_Week                   0\n",
              "Time                          0\n",
              "Local_Authority_(District)    0\n",
              "Local_Authority_(Highway)     0\n",
              "Speed_limit                   0\n",
              "Light_Conditions              0\n",
              "Weather_Conditions            0\n",
              "Road_Surface_Conditions       0\n",
              "Special_Conditions_at_Site    0\n",
              "Urban_or_Rural_Area           0\n",
              "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>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>Longitude</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latitude</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Accident_Severity</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number_of_Vehicles</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number_of_Casualties</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day_of_Week</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Local_Authority_(District)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Local_Authority_(Highway)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Speed_limit</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Light_Conditions</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weather_Conditions</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Road_Surface_Conditions</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Special_Conditions_at_Site</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Urban_or_Rural_Area</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicate values\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NMmAVFkfGx09",
        "outputId": "2ccb5d48-6d0b-4aee-8b9b-c73d31c68ed6"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0aPtxb11li0P",
        "outputId": "8ea05b41-2e54-447b-a085-f7a9f2cd68ad"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(898555, 17)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get information about the dataset\n",
        "print(df.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCgsEBWyli2Y",
        "outputId": "a9d6fdd0-f291-409f-9e77-eed607bde7ba"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 898555 entries, 0 to 898554\n",
            "Data columns (total 17 columns):\n",
            " #   Column                      Non-Null Count   Dtype  \n",
            "---  ------                      --------------   -----  \n",
            " 0   Accident_Index              898555 non-null  object \n",
            " 1   Longitude                   898555 non-null  float64\n",
            " 2   Latitude                    898555 non-null  float64\n",
            " 3   Accident_Severity           898555 non-null  int64  \n",
            " 4   Number_of_Vehicles          898555 non-null  int64  \n",
            " 5   Number_of_Casualties        898555 non-null  int64  \n",
            " 6   Date                        898555 non-null  object \n",
            " 7   Day_of_Week                 898555 non-null  int64  \n",
            " 8   Time                        898555 non-null  object \n",
            " 9   Local_Authority_(District)  898555 non-null  int64  \n",
            " 10  Local_Authority_(Highway)   898555 non-null  object \n",
            " 11  Speed_limit                 898555 non-null  int64  \n",
            " 12  Light_Conditions            898555 non-null  int64  \n",
            " 13  Weather_Conditions          898555 non-null  int64  \n",
            " 14  Road_Surface_Conditions     898555 non-null  int64  \n",
            " 15  Special_Conditions_at_Site  898555 non-null  int64  \n",
            " 16  Urban_or_Rural_Area         898555 non-null  int64  \n",
            "dtypes: float64(2), int64(11), object(4)\n",
            "memory usage: 116.5+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "we will:\n",
        "- drop (Accident_Index, Date, Time) as they don't contribute to predicting \"Accident Severity\".\n",
        "- Keep all the other columns.\n",
        "- Encode categorical columns like Local_Authority_(Highway) using One-Hot Encoding.\n"
      ],
      "metadata": {
        "id": "J-R8ZFXO4rFq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Drop Irrelevant Columns"
      ],
      "metadata": {
        "id": "h184YpQB4v5_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop irrelevant columns\n",
        "df = df.drop(['Accident_Index', 'Date', 'Time','Local_Authority_(Highway)'], axis=1)"
      ],
      "metadata": {
        "id": "A5VCcHf_li4B"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check for Numeric Features"
      ],
      "metadata": {
        "id": "oVKCFn6A44gR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if all features are numeric\n",
        "print(df.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0yYSssWvmCs7",
        "outputId": "ac4a3e27-ab50-4e66-ceee-f6b8288e23c8"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Longitude                     float64\n",
            "Latitude                      float64\n",
            "Accident_Severity               int64\n",
            "Number_of_Vehicles              int64\n",
            "Number_of_Casualties            int64\n",
            "Day_of_Week                     int64\n",
            "Local_Authority_(District)      int64\n",
            "Speed_limit                     int64\n",
            "Light_Conditions                int64\n",
            "Weather_Conditions              int64\n",
            "Road_Surface_Conditions         int64\n",
            "Special_Conditions_at_Site      int64\n",
            "Urban_or_Rural_Area             int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert boolean columns to integers (0 and 1)\n",
        "df = df.astype(int)  # Convert all columns to int, ensure they are all numeric"
      ],
      "metadata": {
        "id": "p1KFH4C2mEiz"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the data types after conversion\n",
        "print(df.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvQOLEnMmG1E",
        "outputId": "351a780a-d674-4dbe-b95c-0d3d819c0f81"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Longitude                     int64\n",
            "Latitude                      int64\n",
            "Accident_Severity             int64\n",
            "Number_of_Vehicles            int64\n",
            "Number_of_Casualties          int64\n",
            "Day_of_Week                   int64\n",
            "Local_Authority_(District)    int64\n",
            "Speed_limit                   int64\n",
            "Light_Conditions              int64\n",
            "Weather_Conditions            int64\n",
            "Road_Surface_Conditions       int64\n",
            "Special_Conditions_at_Site    int64\n",
            "Urban_or_Rural_Area           int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Handling Class Imbalance\n"
      ],
      "metadata": {
        "id": "5fqLY3xR5JyG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accident_severity_counts = df['Accident_Severity'].value_counts()\n",
        "accident_severity_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "26mKNPRhmJd7",
        "outputId": "704ec79e-1958-4bc2-e637-93d5554dc6d2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Accident_Severity\n",
              "3    763607\n",
              "2    124471\n",
              "1     10477\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_Severity</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>763607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>124471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10477</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Adjusting the target variable to start from 0\n",
        "df['Accident_Severity'] = df['Accident_Severity'] - 1  # Convert 1, 2, 3 to 0, 1, 2"
      ],
      "metadata": {
        "id": "EsxPjsqTmO85"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Selection & Training"
      ],
      "metadata": {
        "id": "6Q1M6za7pNXW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Train-Test Split"
      ],
      "metadata": {
        "id": "2_okPz1kpSm-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into features and target variable\n",
        "X = df.drop('Accident_Severity', axis=1)\n",
        "y = df['Accident_Severity']\n",
        "\n",
        "# Train-test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "EajR_jW6mGEQ"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feature Scaling"
      ],
      "metadata": {
        "id": "XB1R7BIFpQmT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Initialize the StandardScaler and scale features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "96h8Tpm3n101"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Define the Evaluation Functions"
      ],
      "metadata": {
        "id": "rqT4KDalpeWT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to calculate accuracy\n",
        "def calculate_accuracy(model, X_test, y_test):\n",
        "    y_pred = model.predict(X_test)\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    return accuracy\n",
        "\n",
        "# Function to generate classification report\n",
        "def generate_classification_report(model, X_test, y_test):\n",
        "    y_pred = model.predict(X_test)\n",
        "    report = classification_report(y_test, y_pred, output_dict=True)\n",
        "    return report\n",
        "\n",
        "# Function to generate confusion matrix\n",
        "def generate_confusion_matrix(model, X_test, y_test):\n",
        "    y_pred = model.predict(X_test)\n",
        "    return confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Mapping function to convert numerical labels to string labels\n",
        "def map_predictions(y):\n",
        "    return ['Slight' if x == 1 else 'Serious' if x == 2 else 'Fatal' for x in y]"
      ],
      "metadata": {
        "id": "y9Wij1oImGHn"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Initialize Models and Hyperparameters"
      ],
      "metadata": {
        "id": "RAKXBX0Apm73"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model definition\n",
        "models = {\n",
        "    'Decision Tree': (DecisionTreeClassifier(max_depth=10, min_samples_split=10), {})\n",
        "}"
      ],
      "metadata": {
        "id": "HlDhh92jmO1r"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perform Randomized Search CV for the Model\n"
      ],
      "metadata": {
        "id": "GEdcn6UEpx5-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize dictionaries to store evaluation results\n",
        "evaluation_results = {}\n",
        "\n",
        "# Train Models with Randomized Search\n",
        "for model_name, (model, params) in models.items():\n",
        "    print(f\"Training {model_name} with Randomized Search...\")\n",
        "\n",
        "    # Initialize RandomizedSearchCV\n",
        "    randomized_search = RandomizedSearchCV(estimator=model, param_distributions=params, n_iter=20,\n",
        "                                           cv=3, scoring='accuracy', verbose=2, n_jobs=-1, random_state=42)\n",
        "\n",
        "    # Fit Randomized Search\n",
        "    randomized_search.fit(X_train_scaled, y_train)\n",
        "\n",
        "    # Best parameters and best score\n",
        "    best_params = randomized_search.best_params_\n",
        "    best_score = randomized_search.best_score_\n",
        "    print(f\"Best cross-validated accuracy for {model_name}: {best_score:.4f}\")\n",
        "\n",
        "    # Evaluate the best model\n",
        "    best_model = randomized_search.best_estimator_\n",
        "\n",
        "    # Calculate accuracy using the helper function\n",
        "    accuracy = calculate_accuracy(best_model, X_test_scaled, y_test)\n",
        "    print(f\"{model_name} Accuracy: {accuracy:.4f}\")\n",
        "\n",
        "    # Generate classification report\n",
        "    classification_report_dict = generate_classification_report(best_model, X_test_scaled, y_test)\n",
        "    print(f\"Classification Report for {model_name}:\\n\", pd.DataFrame(classification_report_dict).T)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEsWTNKNn13M",
        "outputId": "2e66d5de-7d7f-447d-ed1b-ece71c8ea6bf"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Decision Tree with Randomized Search...\n",
            "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:320: UserWarning: The total space of parameters 1 is smaller than n_iter=20. Running 1 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best cross-validated accuracy for Decision Tree: 0.8487\n",
            "Decision Tree Accuracy: 0.8497\n",
            "Classification Report for Decision Tree:\n",
            "               precision    recall  f1-score        support\n",
            "0              0.136364  0.001442  0.002854    2080.000000\n",
            "1              0.284866  0.003872  0.007640   24795.000000\n",
            "2              0.850897  0.998521  0.918817  152836.000000\n",
            "accuracy       0.849748  0.849748  0.849748       0.849748\n",
            "macro avg      0.424042  0.334612  0.309770  179711.000000\n",
            "weighted avg   0.764530  0.849748  0.782499  179711.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "    # Calculate predictions\n",
        "    y_pred = best_model.predict(X_test_scaled)\n",
        "\n",
        "    # Map predictions and true labels to string labels\n",
        "    y_test_mapped = map_predictions(y_test + 1)  # Increment to revert to original labels (1, 2, 3)\n",
        "    y_pred_mapped = map_predictions(y_pred + 1)\n",
        "\n",
        "    # Print data types for debugging\n",
        "    print(f\"Data types: y_test_mapped = {type(y_test_mapped)}, y_pred_mapped = {type(y_pred_mapped)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WX2kZEVon16k",
        "outputId": "71a61a05-f2c6-4bed-bf57-6e5d578b4404"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data types: y_test_mapped = <class 'list'>, y_pred_mapped = <class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "    # Calculate confusion matrix\n",
        "    cm = confusion_matrix(y_test_mapped, y_pred_mapped)\n",
        "    print(f\"{model_name} Confusion Matrix:\\n{cm}\")\n",
        "\n",
        "    # Plot the confusion matrix\n",
        "    plt.figure(figsize=(4, 3))\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "                xticklabels=['Slight', 'Serious', 'Fatal'],\n",
        "                yticklabels=['Slight', 'Serious', 'Fatal'])\n",
        "    plt.title(f'Confusion Matrix for {model_name}')\n",
        "    plt.xlabel('Predicted Label')\n",
        "    plt.ylabel('True Label')\n",
        "    plt.show()\n",
        "\n",
        "    # Store evaluation results in the dictionary\n",
        "    evaluation_results[model_name] = {\n",
        "        'accuracy': accuracy,\n",
        "        'best_params': best_params,\n",
        "    }"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "MVD7SVCwmO4M",
        "outputId": "29586845-aae6-4591-eebc-b54108dfe67b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Confusion Matrix:\n",
            "[[152610    214     12]\n",
            " [ 24692     96      7]\n",
            " [  2050     27      3]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Save the Model"
      ],
      "metadata": {
        "id": "BvJXdihU6F5L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Initialize the scaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fit the scaler on the training data\n",
        "X_train_scaled = scaler.fit_transform(X_train)  # assuming X_train is your training features\n",
        "\n",
        "# Now save the scaler and model after fitting\n",
        "scaler_filename = 'scaler.pkl'\n",
        "with open(scaler_filename, 'wb') as file:\n",
        "    pickle.dump(scaler, file)\n",
        "\n",
        "# Save the trained model\n",
        "model_filename = 'best_model.pkl'\n",
        "with open(model_filename, 'wb') as file:\n",
        "    pickle.dump(best_model, file)\n",
        "\n"
      ],
      "metadata": {
        "id": "S_uVcDYaGMNp"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load & Predict"
      ],
      "metadata": {
        "id": "LQnzkEY7KsHW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import numpy as np\n",
        "\n",
        "# Step 1: Load the trained model and the scaler\n",
        "with open('best_model.pkl', 'rb') as file:\n",
        "    best_model = pickle.load(file)\n",
        "\n",
        "with open('scaler.pkl', 'rb') as file:\n",
        "    scaler = pickle.load(file)\n",
        "\n",
        "# Step 2: Define a function to get user input\n",
        "def get_user_input():\n",
        "    print(\"Enter the following inputs:\")\n",
        "    longitude = float(input(\"Enter Longitude: \"))\n",
        "    latitude = float(input(\"Enter Latitude: \"))\n",
        "    number_of_vehicles = int(input(\"Enter Number_of_Vehicles: \"))\n",
        "    number_of_casualties = int(input(\"Enter Number_of_Casualties: \"))\n",
        "    day_of_week = int(input(\"Enter Day_of_Week: \"))\n",
        "    local_authority_district = int(input(\"Enter Local_Authority_(District): \"))\n",
        "    speed_limit = int(input(\"Enter Speed_limit: \"))\n",
        "    light_conditions = int(input(\"Enter Light_Conditions: \"))\n",
        "    weather_conditions = int(input(\"Enter Weather_Conditions: \"))\n",
        "    road_surface_conditions = int(input(\"Enter Road_Surface_Conditions: \"))\n",
        "    special_conditions_at_site = int(input(\"Enter Special_Conditions_at_Site: \"))\n",
        "    urban_or_rural_area = int(input(\"Enter Urban_or_Rural_Area: \"))\n",
        "\n",
        "    return np.array([[longitude, latitude, number_of_vehicles, number_of_casualties,\n",
        "                      day_of_week, local_authority_district, speed_limit,\n",
        "                      light_conditions, weather_conditions, road_surface_conditions,\n",
        "                      special_conditions_at_site, urban_or_rural_area]])\n",
        "\n",
        "# Step 3: Get user input and make a prediction\n",
        "user_input = get_user_input()\n",
        "\n",
        "# Scale the input using the loaded scaler\n",
        "user_input_scaled = scaler.transform(user_input)\n",
        "\n",
        "# Make a prediction\n",
        "prediction = best_model.predict(user_input_scaled)\n",
        "\n",
        "# Map the prediction to severity levels\n",
        "severity_mapping = {0: 'Slight', 1: 'Serious', 2: 'Fatal'}\n",
        "predicted_severity = severity_mapping[prediction[0]]\n",
        "\n",
        "# Display the prediction\n",
        "print(f\"The predicted accident severity is: {predicted_severity}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ch4-6rv1I4WP",
        "outputId": "4701c3bd-2dfd-4309-baa6-8e042eec1d81"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the following inputs:\n",
            "Enter Longitude: -0.199248\n",
            "Enter Latitude: 51.514399\n",
            "Enter Number_of_Vehicles: 4\n",
            "Enter Number_of_Casualties: 3\n",
            "Enter Day_of_Week: 2\n",
            "Enter Local_Authority_(District): 12\n",
            "Enter Speed_limit: 30\n",
            "Enter Light_Conditions: 2\n",
            "Enter Weather_Conditions: 3\n",
            "Enter Road_Surface_Conditions: 2\n",
            "Enter Special_Conditions_at_Site: 1\n",
            "Enter Urban_or_Rural_Area: 1\n",
            "The predicted accident severity is: Fatal\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 StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}