CASUALTY_SEVERITY_PREDICTIVE_MODEL.ipynb

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        "<a href=\"https://colab.research.google.com/github/ahmedyasser7/DEPI_GP/blob/main/CASUALTY_SEVERITY_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": [
        "# **CASUALTY SEVERITY PREDICTIVE MODEL**"
      ],
      "metadata": {
        "id": "DNqrdd1bQCXo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Uploading The Dataset"
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    {
      "cell_type": "code",
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              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
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              "// See the License for the specific language governing permissions and\n",
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving casualties cleaned data.csv to casualties cleaned data.csv\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries"
      ],
      "metadata": {
        "id": "UYQwGbevPz3y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the necessary Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.pipeline import Pipeline\n",
        "from imblearn.over_sampling import ADASYN\n",
        "from collections import Counter\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.decomposition import PCA\n",
        "import faiss"
      ],
      "metadata": {
        "id": "v40f4vhnKax9"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install faiss-Gpu\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0YgbjsA9cqS",
        "outputId": "930abc71-29ac-481f-a8cb-cddcc1c3f176"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: faiss-Gpu in /usr/local/lib/python3.10/dist-packages (1.7.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load the Dataset"
      ],
      "metadata": {
        "id": "Db0VdwSFQF68"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the 'casualties cleaned data.csv' dataset\n",
        "df = pd.read_csv('casualties cleaned data.csv')\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "TwyiYXqrKa1S",
        "outputId": "53f0a236-0ce3-418f-c3c2-bf0a2f4a0f81"
      },
      "execution_count": 26,
      "outputs": [
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              "        Accident_Index  Casualty_Reference  Sex_of_Casualty  Age_of_Casualty  \\\n",
              "0        200901BS70001                   1                1               77   \n",
              "1        200901BS70002                   1                1               40   \n",
              "2        200901BS70002                   2                1               38   \n",
              "3        200901BS70002                   3                2               76   \n",
              "4        200901BS70002                   4                1               42   \n",
              "...                ...                 ...              ...              ...   \n",
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              "1185981  2014984139614                   2                1               61   \n",
              "1185982  2014984139614                   1                1               79   \n",
              "\n",
              "         Casualty_Severity  Car_Passenger  Bus_or_Coach_Passenger  \\\n",
              "0                        2              0                       0   \n",
              "1                        3              0                       0   \n",
              "2                        3              0                       0   \n",
              "3                        2              0                       4   \n",
              "4                        2              0                       4   \n",
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              "1185982                  3              0                       0   \n",
              "\n",
              "         Casualty_Type  \n",
              "0                    2  \n",
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              "3                   11  \n",
              "4                   11  \n",
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              "1185979              9  \n",
              "1185980             19  \n",
              "1185981             11  \n",
              "1185982              9  \n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "  \n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploring & Preprocessing the Data"
      ],
      "metadata": {
        "id": "bNJ5wEuqcVs2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BneOiv3-GETo",
        "outputId": "3f9b5297-ef4f-410e-bee5-b145248a10fe"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Accident_Index  Casualty_Reference  Sex_of_Casualty  Age_of_Casualty  \\\n",
              "0  200901BS70001                   1                1               77   \n",
              "1  200901BS70002                   1                1               40   \n",
              "2  200901BS70002                   2                1               38   \n",
              "3  200901BS70002                   3                2               76   \n",
              "4  200901BS70002                   4                1               42   \n",
              "\n",
              "   Casualty_Severity  Car_Passenger  Bus_or_Coach_Passenger  Casualty_Type  \n",
              "0                  2              0                       0              2  \n",
              "1                  3              0                       0             19  \n",
              "2                  3              0                       0             11  \n",
              "3                  2              0                       4             11  \n",
              "4                  2              0                       4             11  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Accident_Index</th>\n",
              "      <th>Casualty_Reference</th>\n",
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              "      <th>Age_of_Casualty</th>\n",
              "      <th>Casualty_Severity</th>\n",
              "      <th>Car_Passenger</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>200901BS70001</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>77</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>200901BS70002</td>\n",
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              "      <td>40</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>19</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>200901BS70002</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>38</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>200901BS70002</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>76</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>200901BS70002</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4828e679-2284-4fb5-ab7e-90e14c4d897c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "  \n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get information about the dataset\n",
        "df.info()"
      ],
      "metadata": {
        "id": "aRBUaryUK3_w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0540be93-2a53-4542-f2b6-a0805ad6b346"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1185983 entries, 0 to 1185982\n",
            "Data columns (total 8 columns):\n",
            " #   Column                  Non-Null Count    Dtype \n",
            "---  ------                  --------------    ----- \n",
            " 0   Accident_Index          1185983 non-null  object\n",
            " 1   Casualty_Reference      1185983 non-null  int64 \n",
            " 2   Sex_of_Casualty         1185983 non-null  int64 \n",
            " 3   Age_of_Casualty         1185983 non-null  int64 \n",
            " 4   Casualty_Severity       1185983 non-null  int64 \n",
            " 5   Car_Passenger           1185983 non-null  int64 \n",
            " 6   Bus_or_Coach_Passenger  1185983 non-null  int64 \n",
            " 7   Casualty_Type           1185983 non-null  int64 \n",
            "dtypes: int64(7), object(1)\n",
            "memory usage: 72.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "B01_9bxdK4DK",
        "outputId": "f1a24c1e-3955-47f7-c4d9-5f8debbd2022"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Casualty_Reference  Sex_of_Casualty  Age_of_Casualty  \\\n",
              "count        1.185983e+06     1.185983e+06     1.185983e+06   \n",
              "mean         1.431425e+00     1.415368e+00     3.576307e+01   \n",
              "std          1.608286e+00     4.927855e-01     1.850742e+01   \n",
              "min          1.000000e+00     1.000000e+00     0.000000e+00   \n",
              "25%          1.000000e+00     1.000000e+00     2.100000e+01   \n",
              "50%          1.000000e+00     1.000000e+00     3.200000e+01   \n",
              "75%          2.000000e+00     2.000000e+00     4.800000e+01   \n",
              "max          8.520000e+02     2.000000e+00     1.030000e+02   \n",
              "\n",
              "       Casualty_Severity  Car_Passenger  Bus_or_Coach_Passenger  Casualty_Type  \n",
              "count       1.185983e+06   1.185983e+06            1.185983e+06   1.185983e+06  \n",
              "mean        2.866305e+00   2.729634e-01            8.525839e-02   7.387409e+00  \n",
              "std         3.670351e-01   5.857770e-01            5.507572e-01   7.306746e+00  \n",
              "min         1.000000e+00   0.000000e+00            0.000000e+00   0.000000e+00  \n",
              "25%         3.000000e+00   0.000000e+00            0.000000e+00   3.000000e+00  \n",
              "50%         3.000000e+00   0.000000e+00            0.000000e+00   9.000000e+00  \n",
              "75%         3.000000e+00   0.000000e+00            0.000000e+00   9.000000e+00  \n",
              "max         3.000000e+00   2.000000e+00            4.000000e+00   9.800000e+01  "
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              "      <th></th>\n",
              "      <th>Casualty_Reference</th>\n",
              "      <th>Sex_of_Casualty</th>\n",
              "      <th>Age_of_Casualty</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.185983e+06</td>\n",
              "      <td>1.185983e+06</td>\n",
              "      <td>1.185983e+06</td>\n",
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              "      <td>1.185983e+06</td>\n",
              "      <td>1.185983e+06</td>\n",
              "      <td>1.185983e+06</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>3.576307e+01</td>\n",
              "      <td>2.866305e+00</td>\n",
              "      <td>2.729634e-01</td>\n",
              "      <td>8.525839e-02</td>\n",
              "      <td>7.387409e+00</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.608286e+00</td>\n",
              "      <td>4.927855e-01</td>\n",
              "      <td>1.850742e+01</td>\n",
              "      <td>3.670351e-01</td>\n",
              "      <td>5.857770e-01</td>\n",
              "      <td>5.507572e-01</td>\n",
              "      <td>7.306746e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.100000e+01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>4.800000e+01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>9.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.520000e+02</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.030000e+02</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>9.800000e+01</td>\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7934ec7a-2075-48bf-abd4-3f455e4d2bca');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  \n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRC2eDcOKbGD",
        "outputId": "2fc3023d-0715-40bb-cc83-0fb100cc9b66"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1185983, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "SbY8t2lnKbBN",
        "outputId": "3857d6ca-b3cd-4f67-d0d5-953ebac4c3ed"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Accident_Index            0\n",
              "Casualty_Reference        0\n",
              "Sex_of_Casualty           0\n",
              "Age_of_Casualty           0\n",
              "Casualty_Severity         0\n",
              "Car_Passenger             0\n",
              "Bus_or_Coach_Passenger    0\n",
              "Casualty_Type             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>Casualty_Reference</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex_of_Casualty</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age_of_Casualty</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Casualty_Severity</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Car_Passenger</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bus_or_Coach_Passenger</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Casualty_Type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicate values\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4gi0-MCNKbDl",
        "outputId": "e85b0e09-f5e3-4b83-9031-6bc0ef10db87"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Selection & Training"
      ],
      "metadata": {
        "id": "WkNB6qycwjdQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define Features and Target Variable"
      ],
      "metadata": {
        "id": "gJXz_9g3QciM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define feature columns and target variable\n",
        "feature_columns = ['Sex_of_Casualty', 'Age_of_Casualty',\n",
        "                   'Car_Passenger', 'Bus_or_Coach_Passenger', 'Casualty_Type']\n",
        "X = df[feature_columns]\n",
        "y = df['Casualty_Severity']\n"
      ],
      "metadata": {
        "id": "rDrNBympKbIO"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check Class Distribution"
      ],
      "metadata": {
        "id": "8WN2RVuvQhd5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check initial class distribution\n",
        "print(f\"Initial class distribution: {Counter(y)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hA3dYX6TKbKW",
        "outputId": "c2cce575-bdf8-4eee-ca12-6ac6c530d6e9"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Initial class distribution: Counter({3: 1038627, 2: 136152, 1: 11204})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split the Dataset"
      ],
      "metadata": {
        "id": "nfcsW2-QQnQa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into training and testing sets (80% train, 20% test)\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.2, random_state=42, stratify=y\n",
        ")\n",
        "\n",
        "# Check class distribution after the split\n",
        "print(f\"Class distribution in training set: {Counter(y_train)}\")\n",
        "print(f\"Class distribution in test set: {Counter(y_test)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CJthlYn-KbMu",
        "outputId": "74a69769-ab7c-4c43-c204-6f0f05e3e39b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class distribution in training set: Counter({3: 830901, 2: 108922, 1: 8963})\n",
            "Class distribution in test set: Counter({3: 207726, 2: 27230, 1: 2241})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Handle Class Imbalance"
      ],
      "metadata": {
        "id": "r21Xc6ckQqlZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle class imbalance using ADASYN on the training set\n",
        "adasyn = ADASYN(sampling_strategy='minority', random_state=42)\n",
        "X_balanced, y_balanced = adasyn.fit_resample(X_train, y_train)\n",
        "\n",
        "# Display class distribution after ADASYN on training data\n",
        "print(f\"Class distribution after ADASYN on training set: {Counter(y_balanced)}\")\n",
        "\n",
        "# Handle class imbalance using ADASYN on the test set (for demonstration purposes)\n",
        "X_test_balanced, y_test_balanced = adasyn.fit_resample(X_test, y_test)\n",
        "\n",
        "# Display class distribution after ADASYN on test data\n",
        "print(f\"Class distribution after ADASYN on test set: {Counter(y_test_balanced)}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s405wCYIKbQF",
        "outputId": "66bb4451-7c2d-4fc8-c3b3-0fa9a7cd5bc0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class distribution after ADASYN on training set: Counter({3: 830901, 1: 828271, 2: 108922})\n",
            "Class distribution after ADASYN on test set: Counter({3: 207726, 1: 207498, 2: 27230})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Scale Features and Apply PCA"
      ],
      "metadata": {
        "id": "Pp0ZP3FiMZ6L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set n_components to a valid value (<= number of features in your dataset)\n",
        "n_components = 5  # Adjust as necessary\n",
        "\n",
        "# Scale features and apply PCA\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X_balanced)\n",
        "pca = PCA(n_components=n_components)\n",
        "X_pca = pca.fit_transform(X_scaled)"
      ],
      "metadata": {
        "id": "ZOdve7S3GYus"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Create a Faiss Index for Efficient KNN"
      ],
      "metadata": {
        "id": "Z7aNzYM5MjQL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Faiss index\n",
        "d = X_pca.shape[1]  # Number of features after PCA\n",
        "\n",
        "# Faiss IndexFlatL2 expects float32 type data\n",
        "X_pca_float32 = X_pca.astype('float32')  # Cast to float32\n",
        "\n",
        "# Create an index with L2 distance metric\n",
        "index = faiss.IndexFlatL2(d)\n",
        "\n",
        "# Add the training data (in float32 format) to the index\n",
        "index.add(X_pca_float32)\n",
        "\n"
      ],
      "metadata": {
        "id": "u1L7hMtrGv6i"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Define Function for Faiss Prediction"
      ],
      "metadata": {
        "id": "hgxN3oWDMuzR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to predict using Faiss\n",
        "def faiss_predict(X_test):\n",
        "    # Scale and apply PCA to the test set\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "    X_test_pca = pca.transform(X_test_scaled)\n",
        "\n",
        "    # Ensure that X_test_pca is in the float32 format\n",
        "    X_test_pca = np.array(X_test_pca, dtype=np.float32)\n",
        "\n",
        "    # Perform nearest neighbor search\n",
        "    k = 3  # Number of neighbors\n",
        "    distances, indices = index.search(X_test_pca, k)\n",
        "\n",
        "    # Initialize predictions array\n",
        "    y_pred = np.zeros(X_test.shape[0], dtype=int)\n",
        "    for i in range(X_test.shape[0]):\n",
        "        # Get the labels of the nearest neighbors\n",
        "        neighbor_labels = y_balanced[indices[i]]\n",
        "        # Use majority vote for classification\n",
        "        y_pred[i] = np.bincount(neighbor_labels).argmax()\n",
        "\n",
        "    return y_pred\n",
        "\n"
      ],
      "metadata": {
        "id": "PIR9F3EyG8CG"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Make Predictions Using Faiss"
      ],
      "metadata": {
        "id": "Ms1-1uWMM3rf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set using Faiss\n",
        "y_pred_numeric = faiss_predict(X_test)"
      ],
      "metadata": {
        "id": "RSF5gDnHM5Op"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Map Predictions to Labels"
      ],
      "metadata": {
        "id": "6PESoBL6NAny"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the mapping from numeric predictions to string labels\n",
        "label_mapping = {1: 'Fatal', 2: 'Serious', 3: 'Slight'}\n",
        "\n",
        "# Map the numeric predictions to string labels\n",
        "#y_pred_labels = [label_mapping[pred] for pred in y_pred_numeric]\n"
      ],
      "metadata": {
        "id": "W9UjJC-fNCOf"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Evaluate the Model"
      ],
      "metadata": {
        "id": "9tmMN7D_NSlD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate accuracy using the numeric predictions\n",
        "accuracy = accuracy_score(y_test, y_pred_numeric)\n",
        "\n",
        "# Generate confusion matrix and classification report using numeric predictions\n",
        "conf_matrix = confusion_matrix(y_test, y_pred_numeric)\n",
        "class_report = classification_report(y_test, y_pred_numeric)\n"
      ],
      "metadata": {
        "id": "LP51X3gANWB6"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Store Evaluation Results"
      ],
      "metadata": {
        "id": "fl-pknU5NYDF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Store evaluation results\n",
        "evaluation_results = {\n",
        "    'KNN (with Faiss)': {\n",
        "        'accuracy': accuracy,\n",
        "        'confusion_matrix': conf_matrix,\n",
        "        'classification_report': class_report\n",
        "    }\n",
        "}\n",
        "\n",
        "# Display evaluation results for the KNN model\n",
        "for model_name, results in evaluation_results.items():\n",
        "    print(f\"\\nModel: {model_name}\")\n",
        "    print(f\"Accuracy: {results['accuracy']:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABWOMp8INbg-",
        "outputId": "dd7a230a-68ba-4a09-f84c-e200a35858bb"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model: KNN (with Faiss)\n",
            "Accuracy: 0.8392\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Display Classification Report"
      ],
      "metadata": {
        "id": "l3oFHNquNeGi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the classification report\n",
        "print(f\"KNN Classification Report:\\n{class_report}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YPkEkZqyNjGI",
        "outputId": "4e7075c1-fc5d-4686-d730-d60c0feb3e81"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.03      0.03      0.03      2241\n",
            "           2       0.20      0.08      0.12     27230\n",
            "           3       0.88      0.95      0.91    207726\n",
            "\n",
            "    accuracy                           0.84    237197\n",
            "   macro avg       0.37      0.36      0.35    237197\n",
            "weighted avg       0.80      0.84      0.81    237197\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Plot Confusion Matrix"
      ],
      "metadata": {
        "id": "t_m_ygrtNk3g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Draw the confusion matrix using seaborn\n",
        "plt.figure(figsize=(4, 3))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=['Slight', 'Serious', 'Fatal'],\n",
        "            yticklabels=['Slight', 'Serious', 'Fatal'])\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.title('Confusion Matrix for KNN Model with Faiss')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "n40ma4o1NqnN",
        "outputId": "b90e8edd-9880-4ac1-81c0-a13c9ea1a94b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ],
            "image/png": 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YkONxN+8TMHIblLKT8fx9z549bNy4kRYtWmR7YxcVFcXLly/f+N1MS0vj/v37wL/X93Wv73vz5k0AvvrqK4oUKaK27Nu3j8ePH+f6fBo0aMC5c+d49eoVx44dw9LSkurVq1O1alX5Mdnx48fVbjDfx4f+hpQpUwY3Nze1JePf7rp166hSpQo6OjoULlyYIkWKsGvXrnf++/Lz8+Py5cuUKFGC2rVr4+PjoxbwbG1t8fb2ZtWqVZiZmeHu7s6SJUu+iPYXyGWAsbKy4vLly7k6QE4HYmlqama7XsrBG53fdIzU1FS1z7q6uhw9epT9+/fTpUsXQkJCaNeuHV9//XWWtB/iQ84lg7a2Nq1bt2bdunVs3779jbUXSB9X4u3tTcOGDdmwYQN//fUXgYGBVKxYMcc1NSDX7QYXLlyQf3AuXbqUq30/1Pu0cSxevJj27duzcOHCHA+GmzRpEpGRkTmunTVs2BAXF5cPqsVYWlri4uLCnDlzOHr06Fv/9krL+L6sX78+y918YGAgO3bsyHWe9evXJzk5maCgII4dOyYHkgYNGnDs2DGuX79OVFTUBwcYJf7dZWfDhg14eXlhZ2fH6tWr2bt3L4GBgXz11Vfv/Pf1/fffc+fOHRYtWoSVlRWzZ8+mYsWK7NmzR04zZ84cQkJCGDt2LK9evWLQoEFUrFiRf/7554PKnR/kqhdZixYtuH37tlytfRtra2vS0tLkO6IMjx49IiYmRu4RpoRChQplO/jq9bteSK+yN2rUiLlz53L16lWmT5/OwYMH31j1zyhnaGholm3Xr1/HzMwMfX39DzuBN+jYsSMXLlzgxYsXtG/f/o3ptm7diqurK6tXr6Z9+/Y0btwYNze3LNdEyVHX8fHxdOvWjQoVKtC7d2/8/Pw4c+bMe+dnbW3NzZs3s/yDvX79urz9Q2loaPDzzz/TtGlTJk+ezMKFC9+5j7OzMy4uLsyaNSvXtZgPeWTYsWNHjh07hpGRUZaxGRmKFCmCnp7eG7+bGhoalChRAvj3+r7u9X0zHjGZm5tnuZt3c3N7rxkdateujZaWFseOHVMLMA0bNuTUqVMcOHBA/vw2eTlrwNts3bqVUqVKsW3bNrp06YK7uztubm45HpNkaWnJDz/8QEBAAGFhYRQuXJjp06erpalcuTLjx4/n6NGjHDt2jAcPHnxQLTi/yFWAGTlyJPr6+vTs2ZNHjx5l2X779m0WLFgAIP+jmD9/vlqauXPnAtC8efP3KW+27OzseP78OSEhIfK6iIiILD3Vnj59mmXfjB4gr3edzmBpaYmDgwPr1q1T+8G+fPky+/bte+M/fiW4uroydepUFi9ejIWFxRvTaWpqZrlL27JlCw8ePFBblxEIlRgJPWrUKMLDw1m3bh1z587FxsYGT0/PN17Hd2nWrBmRkZH89ttv8rqUlBQWLVqEgYEBzs7OH1xmSG/X2bp1K/Xq1WPIkCGsX7/+nftkBIycDiDOHJTed2Bk27ZtmTRpEkuXLn3jY0tNTU0aN27Mjh071LrrPnr0iE2bNlG/fn350XazZs04efIkp0+fltNFRUWxceNGtTzd3d0xMjJixowZJCcnZzlmxli03NDR0aFWrVr88ssvhIeHq9VgXr16xcKFC7Gzs8t26ENmSn5/cyOjZpT539ipU6feeaOdmpqa5VGXubk5VlZW8r+T2NhYUlJS1NJUrlwZDQ2N9/63lJ/kqpuynZ0dmzZtol27dpQvX15tJP+JEyfkbqUAVatWxdPTkxUrVhATE4OzszOnT59m3bp1eHh44OrqqthJtG/fnlGjRvHtt98yaNAguVtl2bJl1RrhpkyZwtGjR2nevDnW1tY8fvyYpUuXUrx4cerXr//G/GfPnk3Tpk1xcnKiR48ecjdlY2PjPJ13SENDg/Hjx78zXYsWLZgyZQrdunWjbt26XLp0iY0bN6o1/EL638/ExAR/f38MDQ3R19fH0dEx1+0ZBw8eZOnSpUyaNEnuNr1mzRpcXFyYMGECfn5+ucoP0htYly9fjpeXF+fOncPGxoatW7fy999/M3/+/Bx3LskJPT09du3ahbOzM927d8fY2JhvvvnmjemdnZ1xdnbO0tHgbSZNmvRB3/GcfremTZsmj+364YcfKFCgAMuXLycxMVHt7zBy5EjWr19PkyZNGDx4sNxN2draWu3GzMjIiGXLltGlSxeqV69O+/btKVKkCOHh4ezatYt69eqxePHiXJ9PgwYNmDlzJsbGxlSuXBlI/7G1t7cnNDRU/t14mxo1agAwbtw42rdvT8GCBWnZsmWePUHI0KJFC7Zt28a3335L8+bNCQsLw9/fnwoVKhAXF/fG/V68eEHx4sVp27YtVatWxcDAgP3793PmzBl5KqKDBw8yYMAAvvvuO8qWLUtKSoo8hVGbNm3y9Lw+ivfpenbjxg2pV69eko2NjaSlpSUZGhpK9erVkxYtWiQlJCTI6ZKTk6XJkydLtra2UsGCBaUSJUpIY8aMUUsjSW/uyvl699g3dVOWJEnat2+fVKlSJUlLS0uyt7eXNmzYkKUb5YEDB6RWrVpJVlZWkpaWlmRlZSV16NBBunHjRpZjvN6Vd//+/VK9evUkXV1dycjISGrZsqV09epVtTSZu6xmltG9NbvulZll7qb8Jm/qpjxs2DDJ0tJS0tXVlerVqycFBQVl2714x44dUoUKFaQCBQqonWfmbrKvy5xPbGysZG1tLVWvXl1KTk5WSzd06FBJQ0NDCgoKeus5vOnv/ejRI6lbt26SmZmZpKWlJVWuXDnL3+Ft34HcHi8yMlIqXbq0pKOjIx06dEiSJPVuyplldBHmLd2UX5fR9Tu33ZTfJLtuypIkSefPn5fc3d0lAwMDSU9PT3J1dZVOnDiRZf+QkBDJ2dlZ0tHRkYoVKyZNnTpVWr16dbbfzUOHDknu7u6SsbGxpKOjI9nZ2UleXl7S2bNns5x7TuzatUsCpKZNm6qt79mzpwRIq1evzrIPr3X5lSRJmjp1qlSsWDFJQ0NDrdxv+rtZW1tLnp6eby3bu75TaWlp0owZMyRra2tJW1tbqlatmrRz585sh0dkLnNiYqI0YsQIqWrVqpKhoaGkr68vVa1aVVq6dKmc/s6dO1L37t0lOzs7SUdHRzI1NZVcXV2l/fv3v7XMnwuVJH1gC5ggCIIgZOOzm65fEARB+DyIACMIgiDkCRFgBEEQhDwhAowgCIKQJ0SAEQRBEPKECDCCIAhCnhABRhAEQcgT/8mXRyekvDuNIHxsaWJImiL0Cn7YnGW61QbkOO2rC7mf1eC/5D8ZYARBEN5IJR7sKEUEGEEQhMw0sp/2X8g9EWAEQRAy+0SvBfgSiQAjCIKQmXhEphgRYARBEDITNRjFiAAjCIKQmajBKEYEGEEQhMxEDUYxIsAIgiBkJmowihEBRhAEITNRg1GMCDCCIAiZiRqMYkSAEQRByEwMtFSMCDCCIAiZiRqMYkSAEQRByExDtMEoRQQYQRCEzEQNRjEiwAiCIGQmepEpRgQYQRCEzEQNRjEiwAiCIGQmajCKEQFGEAQhM1GDUYwIMIIgCJmJGoxiRIARBEHITAy0VEy+rwsePXqUlJSULOtTUlI4evToJyiRIAhfNJVGzpdcOnr0KC1btsTKygqVSkVAQID6oVWqbJfZs2fLaWxsbLJsnzlzplo+ISEhNGjQAB0dHUqUKIGfn1+WsmzZsoVy5cqho6ND5cqV2b17t9p2SZKYOHEilpaW6Orq4ubmxs2bN3N1vvk+wLi6uvL06dMs658/f46rq+snKJEgCF80lSrnSy7Fx8dTtWpVlixZku32iIgIteWnn35CpVLRpk0btXRTpkxRSzdw4EB5W2xsLI0bN8ba2ppz584xe/ZsfHx8WLFihZzmxIkTdOjQgR49enDhwgU8PDzw8PDg8uXLcho/Pz8WLlyIv78/p06dQl9fH3d3dxISEnJ8vipJkqQcp/4ENDQ0ePToEUWKFFFbf+PGDWrWrElsbGyu80zIWiEShE8uLX//U/xs6BX8sDYU3RaLc5z21c4B730clUrF9u3b8fDweGMaDw8PXrx4wYEDB+R1NjY2DBkyhCFDhmS7z7Jlyxg3bhyRkZFoaWkBMHr0aAICArh+/ToA7dq1Iz4+np07d8r71alTBwcHB/z9/ZEkCSsrK4YNG8bw4cOB9Jv6okWLsnbtWtq3b5+jc8y3bTCtW7cG0v8IXl5eaGtry9tSU1MJCQmhbt26n6p4giB8qXLx6CsxMZHExES1ddra2mq/V+/r0aNH7Nq1i3Xr1mXZNnPmTKZOnUrJkiXp2LEjQ4cOpUCB9J/zoKAgGjZsKAcXAHd3d2bNmsWzZ88oVKgQQUFBeHt7q+Xp7u4uP7ILCwsjMjISNzc3ebuxsTGOjo4EBQXlOMDk20dkxsbGGBsbI0kShoaG8mdjY2MsLCzo3bs3GzZs+NTFFAThS5OLR2S+vr5qv03Gxsb4+voqUox169ZhaGgo32xnGDRoEL/++iuHDh2iT58+zJgxg5EjR8rbIyMjKVq0qNo+GZ8jIyPfmibz9sz7ZZcmJ/JtDWbNmjVAenVw+PDh6Ovrf+ISCYLwn5CLGsyYMWOy1ASUqL0A/PTTT3Tq1AkdHR219ZmPV6VKFbS0tOjTpw++vr6KHVsp+bYGk2HSpEkiuAiC8PHkogajra2NkZGR2qLEj/yxY8cIDQ2lZ8+e70zr6OhISkoKd+/eBcDCwoJHjx6ppcn4bGFh8dY0mbdn3i+7NDmR7wPMo0eP6NKlC1ZWVhQoUABNTU21RRAEQVF52E05p1avXk2NGjWoWrXqO9MGBwejoaGBubk5AE5OThw9epTk5GQ5TWBgIPb29hQqVEhOk7njQEYaJycnAGxtbbGwsFBLExsby6lTp+Q0OZFvH5Fl8PLyIjw8nAkTJmBpaYlKjLIVBCEPqTTyLnDExcVx69Yt+XNYWBjBwcGYmppSsmRJIP2HfMuWLcyZMyfL/kFBQZw6dQpXV1cMDQ0JCgpi6NChdO7cWQ4eHTt2ZPLkyfTo0YNRo0Zx+fJlFixYwLx58+R8Bg8ejLOzM3PmzKF58+b8+uuvnD17Vu7KrFKpGDJkCNOmTaNMmTLY2toyYcIErKys3trr7XX5vpuyoaEhx44dw8HBQbE8RTdlIT8S3ZSV8aHdlPXbrslx2vit3XKV9+HDh7Mdv+fp6cnatWsBWLFiBUOGDCEiIgJjY2O1dOfPn+eHH37g+vXrJCYmYmtrS5cuXfD29lZ7NBcSEkL//v05c+YMZmZmDBw4kFGjRqnltWXLFsaPH8/du3cpU6YMfn5+NGvWTN4uSRKTJk1ixYoVxMTEUL9+fZYuXUrZsmVzfL75PsBUqFCBjRs3Uq1aNcXyFAFGyI9EgFHGBweY73IRYLbkLsD81+T7Npj58+czevRouQFLEAQhL71pupbsFuHt8mUbTKFChdT+ePHx8djZ2aGnp0fBggXV0mY3jcznounXX/Hw4YMs69u178jYCZMAuBh8gUUL5nHpUgiaGhrYlyvPshWrs3Rd/C9ZvXI5BwL3ERZ2B20dHRwcqjHEezg2tqXkNFs3/8ae3Tu5dvUK8fHxHAs6g5GRkbz9zOlT9OzWNdv8N/66hUqVq+T5eXwK586e4ec1q7l69QpPoqKYu2Axro3+HUwX/eQJC+b9SNCJv4l78YLqNWoycux4rK1tsuQlSRID+vXmxPFjWfK5cukSC+fP4erVK6hUKipVqsxg7xHYlyv3MU7zg4jAoZx8GWDmz5//qYvwUWz8bStpqany51u3btKnZze+dm8CpAeXH/r0pHvPPoweN4ECmpqEhl5HIw8bIT8HZ8+cpl2HTlSsXJnUlFQWLZhL31492PbHLvT09ABISHhF3XoNqFuvAQvnZ20sdXCoxoHDx9XWLVm0gFOngqhYqfJHOY9P4dWrV5S1L0erb9swbMhAtW2SJDF0cH8KFCjI/IVL0TfQZ8PPa+nbszvbduxE9//XNsPG9euy/TF++TKe/n174uz6FWPGTyQ1NZVlSxbRv09P9uw/lOUmMb8RAUY5+TLAeHp6fuoifBSmpqZqn39atYISJUpSs1ZtAGbP8qVDpy706NVbTpP5Lv2/atmK1Wqfp0yfiWsDJ65dvUKNmrUA6NzVC0ivqWSnoJYWZpnmt0tOTubQoQN06Nj5i/6Bqd+gIfUbNMx2W/i9u1y6eJGtAX9iV7oMAGMn+ODmUp89u3fRuu13ctrQ69dYv24NG3/bytcuDdTyCbtzh+fPn9Ov/yAsLC0B6NOvP9+3bkVExENKlrTOo7NTxpf89//Y8v2tcGxsbLbLixcvSEpK+tTFU0xyUhK7dv6BR+s2qFQqoqOjuRRyEdPChenaqT2uDevS3bMz58+d/dRFzXfiXrwAwOi1Hje5ceTQQZ7HxODxbZt3J/5CZfx70tL6tzeShoYGWgW1CL5wTl736tUrxowczuhxEzEzK5IlHxtbW0xMTAjYtpXk5CQSEhII2PY7tqXssLIqlvcn8qFUuViEt8r3AcbExIRChQplWUxMTNDV1cXa2ppJkyaRlpaW7f6JiYlZgtPrk9PlBwcP7ufFixd84/EtAA/+uQ+A/5LFtG77HUuXr6J8+Qr07uHFvXt3P2FJ85e0tDT8Zs3AoVp1ypTJeffJ123ftpW69epTNBejlL80NralsLC0YtGCucQ+f05ychJrVq/k0aNInkRFyenm+PlS1aEarl81yjYffX0DVq75md07/6RODQfq1a7Oib+Psdh/hTwhY34mGvmVk+8DzNq1a7GysmLs2LEEBAQQEBDA2LFjKVasGMuWLaN3794sXLgwywt3MmQ3Gd3sWcpMRqek7b//Tr36DTE3T59cLiNgtv2+HR7ftqF8+QqMGD0WG1tbArb9/imLmq/MmDaZ2zdv4vfjvHcnfoNHkZGc+Ps437Zuq2DJPj8FCxZkzvyF3Lt7F+d6jjjVrMbZ06eo16ChPPjw8KGDnD51ihGjx7wxn4SEBCZPHE/VatX4eeNvrFm/CbvSZRj0Q99cvUvkUxEBRjn5/nZi3bp1zJkzh++//15e17JlSypXrszy5cs5cOAAJUuWZPr06YwdOzbL/tlNRidp5q8J4R4+fMCpkyeYu2CRvC6jfaCUnZ1aWttSdkRGPPyo5cuvZkybwtEjh/lp3YYPqnkEbP8dYxMTnF2/UrB0n6cKFSvx2+8BvHjxguTkZExNTenS4XsqVKwEwJlTJ/nnfjgNnWqr7Td86CCqVa/BqrXr2bNrJw8fPGDdxl/lDim+fj/SsK4jhw8eoEmz5h/9vHLjv96JRkn5PsCcOHECf3//LOurVatGUFAQAPXr1yc8PDzb/bN7N0N+G2i5Y/s2TE0L06Chi7yuWLHiFDE3525YmFrae3fvvrGR9r9CkiR8p0/l4IFAVq9dT/HiJT4orx0B22j5jUe+7930MRkaGgJw795drl65zA8DBgHQrWcvvm2jXtP77ttvGDZyNM4u6QE6IeEVGhoaanf4KpUGKlRIUvaPsvMVUTFRTL4PMCVKlGD16tVZHoGtXr2aEiXSf1iio6PleXg+N2lpaezYvo2WrTzUnk+rVCq8uvVg2ZJF2NuXw75cef7YsZ27YXeYM2/hJyzxpzdj6mT27N7J/EVL0dfTl9sHDAwN5fFBT6KiePLkCff/f+Nx6+YN9PT0sbS0xNjERM7r9KmTPPjnH1q3+W88Hnv5Ml6+JgAPHvxD6PVrGBkbY2lpReBfeylUqBAWllbcvHmD2TOn4/JVI5zq1QfAzKxItg37lpZWFCteHIA6TvWYP2c2vtOm0L5jZyQpjTWrVqJZQJOatR0/zol+APHoSzn5PsD8+OOPfPfdd+zZs4datdK7oJ49e5br16+zdetWAM6cOUO7du0+ZTHf28mgE0REPMSjddbeS527epGYmMRsP1+eP3+OvX05/Ff+RIn/T4r3X7X5t18A6OHVRW39lGm+tPo2/eVMWzb/iv/Sf199261rpyxpALb/vhUHh2rYllJ/FPmlunr5Mr26/zsMYI5f+o1by1YeTJk+k6iox8zxm0l0dDRmRYrQ4ptW9O7bL1fHsC1VigWLl7F82RI8O7dHQ6VBufLlWeK/kiJFzBU9n7wgAoxy8v1cZJA+4+iKFSsIDQ0FwN7enj59+mBjY/Ne+eW3R2SCAGIuMqV86Fxk5t035zjt45++f3ei/7DPIsAoTQQYIT8SAUYZHxxgeuQiwKwWAeZt8uUjspCQECpVqoSGhgYhISFvTVulypc5Z5QgCJ+GeESmnHwZYBwcHIiMjMTc3BwHBwdUKhXZVbRUKhWpmebyEgRB+FAiwCgnXwaYsLAwivx/HEjYa910BUEQ8pIIMMrJlwHG2to62/8WBEHIayoNEWCUki8DzB9//JHjtN98800elkQQhP8aUYNRTr4MMB4eHjlKJ9pgBEFQmggwysmXk+6kpaXlaBHBRRAEpeXlZJdHjx6lZcuWWFlZoVKpCAgIUNvu5eWV5RhNmjRRS/P06VM6deqEkZERJiYm9OjRg7i4OLU0ISEhNGjQAB0dHUqUKIGfn1+WsmzZsoVy5cqho6ND5cqV2b17t9p2SZKYOHEilpaW6Orq4ubmxs2bN3N1vvkywAAEBQWxc+dOtXU///wztra2mJub07t373w57b4gCJ+5PHwfTHx8PFWrVmXJkiVvTNOkSRMiIiLk5ZdfflHb3qlTJ65cuUJgYCA7d+7k6NGj9O7970sJY2Njady4MdbW1pw7d47Zs2fj4+PDihUr5DQnTpygQ4cO9OjRgwsXLuDh4YGHhweXL1+W0/j5+bFw4UL8/f05deoU+vr6uLu752pG7Hw70LJJkya4uroyatQoAC5dukT16tXx8vKifPnyzJ49mz59+uDj45PrvMVASyE/EgMtlfGhAy1LDsx5G3D4ovdvA1apVGzfvl2tScDLy4uYmJgsNZsM165do0KFCpw5c4aaNWsCsHfvXpo1a8Y///yDlZUVy5YtY9y4cURGRqKlpQXA6NGjCQgI4Pr16wC0a9eO+Ph4tZv4OnXq4ODggL+/P5IkYWVlxbBhwxg+fDgAz58/p2jRoqxdu5b27dvn6BzzbQ3m4sWLNGr07wuNfv31VxwdHVm5ciXe3t4sXLiQzZtzPuJWEAQhJ3LziCwvXmh4+PBhzM3Nsbe3p1+/fkRHR8vbgoKCMDExkYMLgJubGxoaGpw6dUpO07BhQzm4ALi7uxMaGsqzZ8/kNG5ubmrHdXd3l2eoDwsLIzIyUi2NsbExjo6OcpqcyLcB5tmzZxQtWlT+fOTIEZo2bSp/rlWrFvfv3/8URRME4QuWmwCT3QsNfX3f/4WGTZo04eeff+bAgQPMmjVL/t3LaG/OGICeWYECBTA1NSUyMlJOk/m3E5A/vytN5u2Z98suTU7ky15kkH4iYWFhlChRgqSkJM6fP8/kyZPl7S9evBDv7xAEQXG5abzP7oWGr79/KjcyP3qqXLkyVapUwc7OjsOHD6s90flc5NsaTLNmzRg9ejTHjh1jzJgx6Onp0aBBA3l7SEgIdnb/jSnWBUH4eFQaqhwv2traGBkZqS0fEmBeV6pUKczMzLh16xYAFhYWPH78WC1NSkoKT58+xeL/b3W1sLDg0aNHamkyPr8rTebtmffLLk1O5NsAM3XqVAoUKICzszMrV65k5cqVas8Uf/rpJxo3bvwJSygIwpcoL7sp59Y///xDdHQ0lpaWADg5ORETE8O5c+fkNAcPHiQtLQ1HR0c5zdGjR0lOTpbTBAYGYm9vL7+Y0cnJiQMHDqgdKzAwECcnJwBsbW2xsLBQSxMbG8upU6fkNDmRb3uRZXj+/DkGBgZoamqqrX/69CkGBgZqQSenRC8yIT8SvciU8aG9yEoP35PjtLd+bPruRJnExcXJtZFq1aoxd+5cXF1dMTU1xdTUlMmTJ9OmTRssLCy4ffs2I0eO5MWLF1y6dEmuGTVt2pRHjx7h7+9PcnIy3bp1o2bNmmzatAng/y8ntKdx48aMGjWKy5cv0717d+bNmyd3Zz5x4gTOzs7MnDmT5s2b8+uvvzJjxgzOnz9PpUqVAJg1axYzZ85k3bp12NraMmHCBEJCQrh69ar85th3yfcBJi+IACPkRyLAKONDA0yZEXtznPbm7CbvTpTJ4cOHcXV1zbLe09OTZcuW4eHhwYULF4iJicHKyorGjRszdepUtcb2p0+fMmDAAP788080NDRo06YNCxcuxMDAQE4TEhJC//79OXPmDGZmZgwcOFAe8pFhy5YtjB8/nrt371KmTBn8/Pxo1qyZvF2SJCZNmsSKFSuIiYmhfv36LF26lLJly+b4fEWAEYR8QgQYZXxogCk7MucB5oZf7gLMf02+7UUmCILwKYi5yJQjAowgCEImIr4oRwQYQRCETDTE+2AUIwKMIAhCJqIGoxwRYARBEDIRNRjliAAjCIKQiWjkV44IMIIgCJmIAKMcEWAEQRAyEfFFOSLACIIgZCJqMMoRAUYQBCETEV+UIwKMIAhCJqIGoxwRYARBEDIR8UU5IsAIgiBkImowyhEBRhAEIRMRX5QjAowgCEImYiS/ckSAET6IeIeJch4/T/zURfgi2Jjl7G2LbyIekSlHBBhBEIRMRHxRjggwgiAImYgajHI0PnUBBEEQ8hOVKudLbh09epSWLVtiZWWFSqUiICBA3pacnMyoUaOoXLky+vr6WFlZ0bVrVx4+fKiWh42NDSqVSm2ZOXOmWpqQkBAaNGiAjo4OJUqUwM/PL0tZtmzZQrly5dDR0aFy5crs3r1bbbskSUycOBFLS0t0dXVxc3Pj5s2buTpfEWAEQRAyef3H+21LbsXHx1O1alWWLFmSZdvLly85f/48EyZM4Pz582zbto3Q0FC++eabLGmnTJlCRESEvAwcOFDeFhsbS+PGjbG2tubcuXPMnj0bHx8fVqxYIac5ceIEHTp0oEePHly4cAEPDw88PDy4fPmynMbPz4+FCxfi7+/PqVOn0NfXx93dnYSEhByfr0qSPryVNiQkJMdpq1Sp8qGH+2AJKZ+6BF8O0civHNHIr4wPbeRvOPfvHKc96l3vvY+jUqnYvn07Hh4eb0xz5swZateuzb179yhZsiSQXoMZMmQIQ4YMyXafZcuWMW7cOCIjI9HS0gJg9OjRBAQEcP36dQDatWtHfHw8O3fulPerU6cODg4O+Pv7I0kSVlZWDBs2jOHDhwPw/PlzihYtytq1a2nfvn2OzlGRNhgHBwdUKhVvilUZ21QqFampqUocUhAEIU/kpmKSmJhIYqL6jYG2tjba2tqKlOX58+eoVCpMTEzU1s+cOZOpU6dSsmRJOnbsyNChQylQIP3nPCgoiIYNG8rBBcDd3Z1Zs2bx7NkzChUqRFBQEN7e3mp5uru7y4/swsLCiIyMxM3NTd5ubGyMo6MjQUFBHzfAhIWFKZGNIAjCJ5ebR1++vr5MnjxZbd2kSZPw8fH54HIkJCQwatQoOnTogJGRkbx+0KBBVK9eHVNTU06cOMGYMWOIiIhg7ty5AERGRmJra6uWV9GiReVthQoVIjIyUl6XOU1kZKScLvN+2aXJCUUCjLW1tRLZCIIgfHK5GWg5ZsyYLDUBJWovycnJfP/990iSxLJly9S2ZT5elSpV0NLSok+fPvj6+ipWc1JKnjTyr1+/nnr16mFlZcW9e/cAmD9/Pjt27MiLwwmCICgmN73ItLW1MTIyUls+9Ec+I7jcu3ePwMBAtdpLdhwdHUlJSeHu3bsAWFhY8OjRI7U0GZ8tLCzemibz9sz7ZZcmJxQPMMuWLcPb25tmzZoRExMjt7mYmJgwf/58pQ8nCIKgKA2VKseL0jKCy82bN9m/fz+FCxd+5z7BwcFoaGhgbm4OgJOTE0ePHiU5OVlOExgYiL29PYUKFZLTHDhwQC2fwMBAnJycALC1tcXCwkItTWxsLKdOnZLT5ITiAWbRokWsXLmScePGoampKa+vWbMmly5dUvpwgiAIisrLcTBxcXEEBwcTHBwMpLdfBwcHEx4eTnJyMm3btuXs2bNs3LiR1NRUIiMjiYyMJCkpCUhvwJ8/fz4XL17kzp07bNy4kaFDh9K5c2c5eHTs2BEtLS169OjBlStX+O2331iwYIHao7XBgwezd+9e5syZw/Xr1/Hx8eHs2bMMGDDg/9dAxZAhQ5g2bRp//PEHly5domvXrlhZWb2119vrFB/JHxYWRrVq1bKs19bWJj4+XunDCYIgKCovR/KfPXsWV1dX+XPGj76npyc+Pj788ccfQHrP3MwOHTqEi4sL2tra/Prrr/j4+JCYmIitrS1Dhw5VCx7Gxsbs27eP/v37U6NGDczMzJg4cSK9e/eW09StW5dNmzYxfvx4xo4dS5kyZQgICKBSpUpympEjRxIfH0/v3r2JiYmhfv367N27Fx2dnHcDV2QcTGYVKlTA19eXVq1aYWhoyMWLFylVqhSLFi1izZo1nD9/XsnDvRcxDkY5YhyMcsQ4GGV86DiYpstO5Tjtnn6OH3SsL53iNRhvb2/69+9PQkICkiRx+vRpfvnlF3x9fVm1apXShxMEQVCUmItMOYoHmJ49e6Krq8v48eN5+fIlHTt2xMrKigULFuR4cE5m9+/fR6VSUbx4cQBOnz7Npk2bqFChglqVTxAEQQkivignT7opd+rUiZs3bxIXF0dkZCT//PMPPXr0eK+8OnbsyKFDh4D0wT9ff/01p0+fZty4cUyZMkXJYguCIKDKxf+Et8uzyS4fP37MuXPnCA0NJSoq6r3zuXz5MrVr1wZg8+bNVKpUiRMnTrBx40bWrl2rUGkFQRDSaWqocrwIb6d4gHnx4gVdunTBysoKZ2dnnJ2dsbKyonPnzjx//jzX+SUnJ8sDl/bv3y/PLFquXDkiIiIULbsgCEJedlP+r1E8wPTs2ZNTp06xa9cuYmJiiImJYefOnZw9e5Y+ffrkOr+KFSvi7+/PsWPHCAwMpEmTJgA8fPgwR4OQBEEQcuNTDrT80ijeyL9z507++usv6tevL69zd3dn5cqVcnDIjVmzZvHtt98ye/ZsPD09qVq1KgB//PGH/OhMEARBKSJuKEfxAFO4cGGMjY2zrDc2NpZHmuaGi4sLT548ITY2Vm3/3r17o6en90FlFQRBeJ3opqwcxR+RjR8/Hm9vb7UpnSMjIxkxYgQTJkx4rzw1NTWzBCcbGxt57h1BEASliDYY5ShSg6lWrZpa1L958yYlS5aU38AWHh6OtrY2UVFRuW6HsbW1fesdxZ07d96v0IIgCNkQbSvKUSTA5Gbys9x6/bWgycnJXLhwgb179zJixIg8O64gCP9NIrwoR5EAM2nSJCWyydbgwYOzXb9kyRLOnj2bZ8cVBOG/SbTBKCfPBlrmtaZNm/L7779/6mIIgvCFEQMtlaN4L7LU1FTmzZvH5s2bCQ8Pl99jkOHp06eKHGfr1q2YmpoqkpcgCEIGUYFRjuIBZvLkyaxatYphw4Yxfvx4xo0bx927dwkICGDixIm5zu/1DgSSJBEZGUlUVBRLly5VsuiCIAjiEZmCFA8wGzduZOXKlTRv3hwfHx86dOiAnZ0dVapU4eTJkwwaNChX+b3egUBDQ4MiRYrg4uJCuXLlFCy5IAgCiCdfylE8wERGRlK5cmUADAwM5PnHWrRo8V7jYPKyA4EgCMLrRA1GOYoHmOLFixMREUHJkiWxs7Nj3759VK9enTNnzsiTVuZWamoqAQEBXLt2DUifn+ybb75BU1NTyaILgiCIbsoKUrwX2bfffsuBAwcAGDhwIBMmTKBMmTJ07dqV7t275zq/W7duUb58ebp27cq2bdvYtm0bnTt3pmLFity+fVvp4guC8B+Xl5NdHj16lJYtW2JlZYVKpSIgIEBtuyRJTJw4EUtLS3R1dXFzc+PmzZtqaZ4+fUqnTp0wMjLCxMSEHj16EBcXp5YmJCSEBg0aoKOjQ4kSJfDz88tSli1btlCuXDl0dHSoXLkyu3fvznVZ3kXxGszMmTPl/27Xrh3W1tacOHGCMmXK0LJly1znN2jQIOzs7Dh58qTcayw6OprOnTszaNAgdu3apVjZ89KyJYvwX7pYbZ2NrS07du4F4H54OHN+nEXw+XMkJSVRr34DRo+dQGEzMzl906+/4uHDB2p5DBoyjB69vuw3e65euZyD+wO5G3YHbR0dqjpUY/DQYdjYlgLg+fMYli1ZxMkTfxMZEUGhQqa4fNWIHwYOxtDQUM7n1Mkgli5awK2bN9DV1aNlKw/6DxpCgQL//jOQJIn1a3/i962biXj4EJNChfi+XUd69un70c/7Q/3682r+PnKA+/fC0NLWpkJlB3r0G0IJa5ssaSVJYvzw/pw9+TeTfOdRt+FXAMQ+j2Hm5DGE3brJi9gYjAuZ4lTfhW59B6GvbwDA5YvnWb1sAffvhZGYkIC5hSXNW7Wldfsucv4v4+NZt3IJJ44eJObZU+zKlqPfkJHYl6/0Ua5FbuTlE7L4+HiqVq1K9+7dad26dZbtfn5+LFy4kHXr1mFra8uECRNwd3fn6tWr6OjoAOkvdIyIiCAwMJDk5GS6detG79692bRpEwCxsbE0btwYNzc3/P39uXTpEt27d8fExER+C/CJEyfo0KEDvr6+tGjRgk2bNuHh4cH58+epVKlSjsvyLipJkiQlLty7PH78mFWrVjF27Nhc7aevr8/Jkyfldp0MFy9epF69elkid04kpOR6lw+2bMkiAvf9xYpVa+R1mgU0KVTIlJcvX/Jd628oa1+OH/oPBGDJogU8fvyYDb9sRkMjvaLZ9Ouv8GjdhjZtv5fz0NPX/6STfqZ9hK9P/z49cW/ajIqVKpOSksriBfO4desm23bsRFdPj1s3b7BsySK+8fiWUqVKExHxkOlTJlGmrD0/zlsIQOj163Tp8B09evelafMWPH70iBlTfKjf0BnvEaPkY82aMY2TQX8z2HsEZcqU5fnzGGKfP6dO3Xp5fp6Pnycqmt9Y7364NGpC2fIVSU1NZe3yRdy9c4uVG7eho6v+ndn263rOnznJmZPH1QLMi9hYDh/Yi325ihgXKsTDf+6zeM4MStuXZ4xP+s3krRvXuH/vLrZ2ZdDR1eVKyAUW+E2l76ARNGvVFoDpE0Zw984tBo0Yj6lZEQ7+tYttv21g5cZtmBUpquh525jl7MfvTXpvuZLjtCu+q/jex1GpVGzfvl3uyCRJElZWVgwbNozhw4cD8Pz5c4oWLcratWtp3749165do0KFCpw5c4aaNWsCsHfvXpo1a8Y///yDlZUVy5YtY9y4cURGRqKlpQXA6NGjCQgI4Pr160D6zX98fDw7d+6Uy1OnTh0cHBzw9/fPUVly4qMNtIyIiHivRn5tbW1evHiRZX1cXJx88T4XBTQ1MStSRF4KFUqvkQVfOM/DBw+YOn0mZcraU6asPVNnzOLqlcucPnVSLQ99fX21PP4LM0ovWb6KbzxaY1e6DPblyjF5ui+REQ+5ejX9h6B0mbLMmb8IZ5evKFGyJLUd6zBg0FCOHj5ESkr63cS+vbspU9aePv36U7KkNTVr1WbwsOFs/nUT8fHpNyl3bt9m6+ZfmbdwCS6uX1GseHEqVKz0UYJLXpgxdxmNm7fCplRp7MrYM2zcFB4/iuBm6DW1dLdvXOf3X3/Ge+zkLHkYGhnR8tvvKVu+IkUtrKhW05GWrb/n8sXzcprSZcvj+nVTbEqVxsKyGI3cW1Czdl05TWJiAsePHKBn/6FUdqhBseIl6dKjH1bFS7Bz+5a8vQjvITeTXSYmJhIbG6u2JCa+341CWFgYkZGRuLm5yeuMjY1xdHQkKCgIgKCgIExMTOTgAuDm5oaGhganTp2S0zRs2FDt99Hd3Z3Q0FCePXsmp8l8nIw0GcfJSVlyIt+P5G/RogW9e/fm1KlTSJKEJEmcPHmSvn37ym+3/FzcC7+Hm0t9mrk3YszIYUQ8fAhAUlISKpVK7Quhra2NhoYGF86fU8vjp1UraVjXke/beLD2p1XyD+h/SVxc+g1Hdq+FyPDixQv0DQzkx19JyUlZOploa+uQmJjItSvpgerokUMUK16co0cO09y9Ec0af8XkieN5/jwmb07kI8sIpIZGRvK6hIRXzJw8hv7DxmJa2OxNu8qiox7z95GDVHGo8cY0t25c4+rli1R2SP8RTE1JJS01FS2t16+/NldCLrzPqeSp3Izk9/X1xdjYWG3x9fV9r+NmzEBftKh6ja5o0aLytsjIyCyzyBcoUABTU1O1NNnlkfkYb0qTefu7ypITirfBKG3hwoV4enri5OREwYIFAUhJSeGbb75hwYIF79w/MTExyx2FpKn93j3a3lflKlWYOt0XGxtboqKiWL5sCd26duL3HX9SpaoDurq6zJ8zm4FDvJEkiQXz5pCamkpUVJScR4dOXShfoQLGxsYEB19g4fy5REVFMWLUmI96Lp9SWloaP86cgUO16pQuUzbbNM+ePWPl8mVqjxLr1q3PpvU/s2f3Thq7NyX6yRNW+KcP1I16kn6N/7l/n4iHD9m/7y+mzphFWmoqP/rNZMTQwaz4aV3en1weSktLw3+BHxWrOGBTqoy8fvnC2VSoVJW6DVzfur/vpFEEHTtMYmICdeo5M3S0T5Y0nTy+5nnMM1JTU+ncvS9Nv0lvY9DT16d8papsWruCkta2mJgW5vD+PVy7HIJVsRJKnqYictNNecyYMXh7e6ut+9i/LflZvq/BmJiYsGPHDkJDQ9m6dStbt24lNDSU7du3v/UONkN2dxizZ73fHcaHqN/AmcbuTSlrX4569RuweNkKXryI5a+9ezA1NWX23AUcOXIIp1rVqF+nJi9exFK+QkU0Mo366urVjVq1HSlrX47v23Vg2IhR/LppQ5bpeL5kvtOmcOvWTWbOnpvt9ri4OAb90IdSdnb0+WGAvN6pXn2GDBvBjCk+OFavQqsWTajfoCEAGqr0fwaSlEZSUhJTZ8ykeo2a1KztyKQp0zhz+hR3wz7v10IsnjODe3duM2byv72Jgo4dJvjcGfoOHvnO/fsMGsHiNb/iM3MBDx/cZ/miH7OkmbN0DYtW/8LAEePZvnkjhwL3yNtGTpiOJEl09PiaFq61CNiyCRe3Jqg08t9PkEYuFm1tbYyMjNSW9w0wFhYWADx69Eht/aNHj+RtFhYWPH78WG17SkoKT58+VUuTXR6Zj/GmNJm3v6ssOaFYDeb1KP66zHfi76NMmTKUKVPm3Qlfk90dhqT56e8wjIyMsLa24X54OAB169Vn1979PHv2FE3NAhgZGfFVw3oUb9rsjXlUrlKVlJQUHj74R+5R9SWbOX0Kx44cZvW6DRTN5kseHx9H/z490dPXZ+6CxXKNN0MXz2507upFVNRjjIyMefjgAYvmz6V48fS7aDOzIhQoUABrG1t5H9tSdgBERkR8ttd48ZwZnDpxlDlLfqKI+b+PPILPnSbiwX1aN6mvln7quGFUqlqd2YtXy+tMC5thWtiMkta2GBoZMeyHbnT06k1hsyJyGgur4gDY2pUh5mk0G1Yvw/XrpgBYFS/Bj0t+IuHVS+Lj4ylsVoTpE0Zg+f998pNPNdDS1tYWCwsLDhw4gIODA5DeI+zUqVP069cPACcnJ2JiYjh37hw1aqQ/pjx48CBpaWk4OjrKacaNG0dycrL8byAwMBB7e3v5xY1OTk4cOHBA7XUogYGBODk55bgsOaFYgLlw4d3PUhs2bJijvLy9vZk6dSr6+vrvDFxz52Z/J5tBWzvr47BP0YvsdS/j47l//z7Nvymitj6j4f/UySCePo3GxfWrN+YRev0aGhoamJoWztOyfmqSJDFrxlQOHtjPyjU/U6x41h+luLg4fujTA62CWsxftPSNd5EqlQrz///I7t2zCwsLS8pVqACAQ7XqpKSkcD88nBL/f1nevbt3AbC0ssqDM8tbkiSxZK4vJ44eZPbi1XIAyNCuS3eafvOt2ro+XdrSZ9Bw6tRzfmu+AMnJb645p6WlkZycnGW9jq4eOrp6vIiN5dzpIHr+MCQXZ/Rx5OVUMXFxcdy6dUv+HBYWRnBwMKamppQsWZIhQ4Ywbdo0ypQpI3cNtrKyknualS9fniZNmtCrVy/8/f1JTk5mwIABtG/fHqv/f0c7duzI5MmT6dGjB6NGjeLy5cssWLCAefPmyccdPHgwzs7OzJkzh+bNm/Prr79y9uxZVqxYAaT/O3lXWXJCsQBz6NAhpbLiwoUL8pfz/Pnzb7yj+JymdJgzexbOLq5YWlkR9fgxy5YsQlNTg6bNWgAQsP13SpWyo1AhUy5evICf7ww6d/WS75ovBl/gUshFatWug76+PhcvXmD2LF+at/gGoxw8Kvyc+U6bwp7dO5m3cAn6+vo8+X+biYGBITo6OunBpXcPEl69YvqC2cTHx8kN2oUKmcozPqz7aTV169dHQ0ODA/sDWbNqJX5z5snbHZ3qUr5CBXwmjmXEqLGkpaUxc/pU6jjVVavVfC4Wz5nBocA9+Mycj66ePk+jnwCgb2CAtraOXCt5nXlRSzkYnT5xjGfPorEvXxEdXT3uhd1m1ZJ5VKzigIVlMQD++P1XzItaUMI6/RpdCj7H77/8TKvvOsp5nj31N5IEJUpa8+Cf+6xaMo8SJW1o3LxVXl+GXMvLAHP27FlcXf9t78q4gfb09GTt2rWMHDmS+Ph4evfuTUxMDPXr12fv3r1q4042btzIgAEDaNSoERoaGrRp04aFCxfK242Njdm3bx/9+/enRo0amJmZMXHiRHkMDEDdunXZtGkT48ePZ+zYsZQpU4aAgAB5DAyQo7K8y0cbB5OffIoazMjhQzl/9gwxMTEUMjWlWvUaDBw0VL5Tnj/3R/4I2M7z58+xKlaM775vTxdPLzmIXrt6helTJ3M37A5JSUkUK1acFt+0ootnt0/aXftjjIOpVin7SU0nT5vBNx6tOXv6FL26e2abZtdf+7Eqlv5j2bu7J9euXSU5KYmy9uXo3a+/3A6T4fHjR+ljYU78ja6uHvUaNMB7xCiMjU0UPafsKD0Oxr1e1WzXDxs75Y0/7O71qqqNgwk+d5q1KxYTfvcOyUlJFClalHrOjWjXuTsGhum90XZs2cSuHVuJjHiApmYBrIoVp8k3bWjeqq08huvIgb9Y47+QJ1GPMDQypp5zI7r1GYi+gWG25fgQHzoOZtifoTlOO6el/Qcd60uXrwNMcnIyurq6BAcHq0XWD5UfHpF9KT5GgPmvUDrA/Fd9aIAZsTPnAWZ2CxFg3iZfd1MuWLAgJUuWJDU19VMXRRCE/4jP6Ml7vpf/+gi+Zty4cYwdO1axN2EKgiC8TQGVKseL8Hb5ugYDsHjxYm7duoWVlRXW1tbo6+urbT9//vwb9hQEQcg9ETeUkycB5tixYyxfvpzbt2+zdetWihUrxvr167G1taV+/frvziCT3HSJEwRB+FDvMw2/kD3FA8zvv/9Oly5d6NSpExcuXJCnaXn+/DkzZszI8s6BdxFvtBQE4WMS8UU5irfBTJs2DX9/f1auXKk2krpevXrv/TgrJiaGVatWMWbMGLkt5vz58zx48OAdewqCIOSOhirni/B2itdgQkNDsx2xb2xsTExMTK7zCwkJwc3NDWNjY+7evUuvXr0wNTVl27ZthIeH8/PPPytQakEQhHTiEZlyFK/BWFhYqE2FkOH48eOUKpX7uZy8vb3x8vLi5s2baiNImzVrxtGjRz+orIIgCK/LzftghLdTPMD06tWLwYMHc+rUKVQqFQ8fPmTjxo0MHz48V5OkZThz5gx9+vTJsr5YsWK5ei+BIAhCTohHZMpR/BHZ6NGjSUtLo1GjRrx8+ZKGDRuira3N8OHDGThwYK7z09bWJjY2Nsv6GzduUKRIkWz2EARBeH8qRORQSp5NFZOUlMStW7eIi4ujQoUKGBgYvFc+PXv2JDo6ms2bN2NqakpISAiampp4eHjQsGFD5s+fn+s8xVQxyhFTxShHTBWjjA+dKsbv0O0cpx3pavdBx/rS5dlIfi0tLSpUqEDt2rXfO7gAzJkzh7i4OMzNzXn16hXOzs7Y2dlhYGDA9OnTFSyxIAhC+iztOV2Et1P8EZmrq+tbL/zBgwdzlZ+xsTGBgYEcP36ckJAQ4uLiqFGjBo0aNfrQogqCIGQh2laUo3iAyXj7WYbk5GSCg4O5fPkynp7ZT6menaCgIKKjo2nRIv19KfXr1+f27dv4+fnx8uVLPDw8WLRokXj/tSAIihIVE+UoHmAyvzUtMx8fH+Li4nKcz5QpU3BxcZEDzKVLl+jVqxeenp6UL1+e2bNnY2VlhY+PjxLFFgRBAMQ4GCV9tNmUO3fuzE8//ZTj9MHBwWqPwX799Vdq167NypUr8fb2ZuHChWzevDkviioIwn+Y6KasnI82m3JQUFCuXrX57NkzihYtKn8+cuQITZs2lT/XqlWL+/fvK1pGQRAEUYFRjuIBpnXr1mqfJUkiIiKCs2fPMmHChBznU7RoUcLCwihRogRJSUmcP3+eyZMny9tfvHihNteZIAiCEjTEOBjFKP6IzNjYWG0xNTXFxcWF3bt352pm5GbNmjF69GiOHTvGmDFj0NPTo0GDBvL2kJAQ7OxEH3RBEJSVV1PF2NjYZNvVuX///gC4uLhk2da3b1+1PMLDw2nevDl6enqYm5szYsQIUlLUB/YdPnyY6tWro62tTenSpVm7dm2WsixZsgQbGxt0dHRwdHTk9OnTuTuZHFK0BpOamkq3bt2oXLkyhQoV+qC8pk6dSuvWrXF2dsbAwIB169ahpaUlb//pp59o3LjxhxZZEARBTYE8alw5c+aM2uvfL1++zNdff813330nr+vVqxdTpkyRP+vp6cn/nZqaSvPmzbGwsODEiRNERETQtWtXChYsyIwZMwAICwujefPm9O3bl40bN3LgwAF69uyJpaUl7u7uAPz22294e3vj7++Po6Mj8+fPx93dndDQUMzNzRU9Z8VH8uvo6HDt2jVsbW0Vye/58+cYGBigqamptv7p06cYGBioBZ2cEiP5lSNG8itHjORXxoeO5F956l6O0/ZytH7v4wwZMoSdO3dy8+ZNVCoVLi4uODg4vHF2kj179tCiRQsePnwot0/7+/szatQooqKi0NLSYtSoUezatYvLly/L+7Vv356YmBj27t0LgKOjI7Vq1WLx4sUApKWlUaJECQYOHMjo0aPf+3yyo/gjskqVKnHnzh3F8jM2Ns4SXABMTU3fK7gIgiC8jYZKleMlMTGR2NhYtSXjJYtvk5SUxIYNG+jevbvawPSNGzdiZmZGpUqVGDNmDC9fvpS3BQUFUblyZbXOT+7u7sTGxnLlyhU5jZubm9qx3N3dCQoKko977tw5tTQaGhq4ubnJaZSUJy8cGz58ODt37iQiIiLLxRcEQcjPctMG4+vrm6Xd2dfX953HCAgIICYmBi8vL3ldx44d2bBhA4cOHWLMmDGsX7+ezp07y9sjIyPVggsgf86YWf5NaWJjY3n16hVPnjwhNTU12zR5MTu9Ym0wU6ZMYdiwYTRr1gyAb775Ri0yS5KESqVSewYpCIKQ3+TmrnvMmDF4e3urrcvJ7CKrV6+madOmWFlZyet69+4t/3flypWxtLSkUaNG3L59+7Pt0KRYgJk8eTJ9+/bl0KFDSmUpCILw0eVmEkttbe1cT1d179499u/fz7Zt296aztHREYBbt25hZ2eHhYVFlt5ejx49AtJf9Jjx/xnrMqcxMjJCV1cXTU1NNDU1s02TkYeSFAswGX0FnJ2dlcpSEATho8vrUTBr1qzB3Nyc5s2bvzVdcHAwAJaWlgA4OTkxffp0Hj9+LPf2CgwMxMjIiAoVKshpdu/erZZPYGAgTk5OQPos9zVq1ODAgQN4eHgA6Y38Bw4cYMCAAUqdokzRbspi+mpBED53eTkXWVpaGmvWrMHT05MCBf79+b19+zabNm2iWbNmFC5cmJCQEIYOHUrDhg2pUqUKAI0bN6ZChQp06dIFPz8/IiMjGT9+PP3795drUX379mXx4sWMHDmS7t27c/DgQTZv3syuXbvkY3l7e+Pp6UnNmjWpXbs28+fPJz4+nm7duil+vooGmLJly74zyDx9+lTJQwqCICgqL2+T9+/fT3h4ON27d1dbr6Wlxf79++Uf+xIlStCmTRvGjx8vp9HU1GTnzp3069cPJycn9PX18fT0VBs3Y2try65duxg6dCgLFiygePHirFq1Sh4DA9CuXTuioqKYOHEikZGRODg4sHfv3iwN/0pQbByMhoYG8+fPx9jY+K3pcjNlf14R42CUI8bBKEeMg1HGh46D2XT+nxyn7Vi9+Acd60unaA2mffv2io8EFQRB+Jg0xaN+xSgWYET7iyAIXwLxW6YcxXuRCYIgfM5EeFGOYgEmLS1NqawEQRA+GVGDUc5He+FYfiIappWTmCxuLJRS/uvhn7oIX4RXFxZ/0P4f7TW//wH/yQAjCILwJqIGoxwRYARBEDIR4UU5IsAIgiBkIiowyhEBRhAEIRMNUYdRjAgwgiAImeTlXGT/NSLACIIgZCLii3JEgBEEQchEPCJTjggwgiAImYgajHJEgBEEQchEBBjliAAjCIKQiUo8IlOMCDCCIAiZaIj4ohgRYARBEDIRNRjliAAjCIKQiWiDUY6YOFQQBCETTZUqx0tu+Pj4oFKp1JZy5crJ2xMSEujfvz+FCxfGwMCANm3a8OjRI7U8wsPDad68OXp6epibmzNixAhSUtTfAX/48GGqV6+OtrY2pUuXZu3atVnKsmTJEmxsbNDR0cHR0ZHTp0/n6lxySgQYQRCETFS5+F9uVaxYkYiICHk5fvy4vG3o0KH8+eefbNmyhSNHjvDw4UNat24tb09NTaV58+YkJSVx4sQJ1q1bx9q1a5k4caKcJiwsjObNm+Pq6kpwcDBDhgyhZ8+e/PXXX3Ka3377DW9vbyZNmsT58+epWrUq7u7uPH78+D2v2JuppP/gqyhfJv/nTjnPiPfBKMeq3uBPXYQvwoe+D+b4zWc5Tlu/TKEcp/Xx8SEgIIDg4OAs254/f06RIkXYtGkTbdu2BeD69euUL1+eoKAg6tSpw549e2jRogUPHz6kaNGiAPj7+zNq1CiioqLQ0tJi1KhR7Nq1i8uXL8t5t2/fnpiYGPbu3QuAo6MjtWrVYvHi9OuUlpZGiRIlGDhwIKNHj87x+eSEqMEIgiBkosrFkpiYSGxsrNqSmJj4xrxv3ryJlZUVpUqVolOnToSHhwNw7tw5kpOTcXNzk9OWK1eOkiVLEhQUBEBQUBCVK1eWgwuAu7s7sbGxXLlyRU6TOY+MNBl5JCUlce7cObU0GhoauLm5yWmUJAKMIAhCJhoqVY4XX19fjI2N1RZfX99s83V0dGTt2rXs3buXZcuWERYWRoMGDXjx4gWRkZFoaWlhYmKitk/RokWJjIwEIDIyUi24ZGzP2Pa2NLGxsbx69YonT56QmpqabZqMPJQkepEJgiBkkpuWlTFjxuDt7a22TltbO9u0TZs2lf+7SpUqODo6Ym1tzebNm9HV1X2fouZ7ogYjCIKQWS6ekWlra2NkZKS2vCnAvM7ExISyZcty69YtLCwsSEpKIiYmRi3No0ePsLCwAMDCwiJLr7KMz+9KY2RkhK6uLmZmZmhqamabJiMPJYkAIwiCkEle9iLLLC4ujtu3b2NpaUmNGjUoWLAgBw4ckLeHhoYSHh6Ok5MTAE5OTly6dEmtt1dgYCBGRkZUqFBBTpM5j4w0GXloaWlRo0YNtTRpaWkcOHBATqMk8YhMEAQhk7waaDl8+HBatmyJtbU1Dx8+ZNKkSWhqatKhQweMjY3p0aMH3t7emJqaYmRkxMCBA3FycqJOnToANG7cmAoVKtClSxf8/PyIjIxk/Pjx9O/fX6419e3bl8WLFzNy5Ei6d+/OwYMH2bx5M7t27ZLL4e3tjaenJzVr1qR27drMnz+f+Ph4unXrpvg5iwAjCIKQSV4FmH/++YcOHToQHR1NkSJFqF+/PidPnqRIkSIAzJs3Dw0NDdq0aUNiYiLu7u4sXbpU3l9TU5OdO3fSr18/nJyc0NfXx9PTkylTpshpbG1t2bVrF0OHDmXBggUUL16cVatW4e7uLqdp164dUVFRTJw4kcjISBwcHNi7d2+Whn8liHEwwgcR42CUI8bBKONDx8GcDYvNcdqatkYfdKwvnajBCIIgZCLmIlNOvg0wsbE5v4swMhJ3EYIgKEPEF+Xk2wBjYmKC6h23EpIkoVKpSE1N/UilEgThiycijGLybYA5dOjQpy6CIAj/QeJ9MMrJtwHG2dn5UxdBEIT/INEGo5x8G2Cy8/LlS8LDw0lKSlJbX6VKlU9UIkEQvjQivijnswgwUVFRdOvWjT179mS7XbTBCIKgGBFhFPNZTBUzZMgQYmJiOHXqFLq6uuzdu5d169ZRpkwZ/vjjj09dPEEQviAfa6qY/4LPogZz8OBBduzYQc2aNdHQ0MDa2pqvv/4aIyMjfH19ad68+acuYharVy7n4P5A7obdQVtHh6oO1Rg8dBg2tqXkNImJicydPYu/9uwiKSkZp3r1GDt+EoXNzOQ0p04GsXTRAm7dvIGurh4tW3nQf9AQChT490934u9j+C9ZzO1bN9HS1qZ6jZoMGzEKq2LFP+o555XU1FRW+S9h7+4/eRr9BLMi5jRv6UG3Xn3lnobR0U9YsmAup4P+5kXcC6pVr4n3yLGUtLaR8+nX05ML586o5f1tm+8ZNd5H/hwZ8RC/GVM4d/Y0erp6NGvZin4Dh6pd7/yqXnU7hnZ1o3qFklgWMeb7oSv483CIvN3c1JBpg1vh5lQeYwNdjp+/hbffFm6HR6nl41jFFp/+LahV2YbU1DRCbjyg5Q9LSEhMltM0qV+Rsb2bUqmMFQlJKRw/d5PvvVcC0LmlIyundMm2jCW/Gk3UszgA2jetyVAvN0qXMOd53Cv2/X2VsfMDePo8XulLkysaIm4oJv//qwHi4+MxNzcHoFChQkRFRVG2bFkqV67M+fPnP3Hpsnf+7BnadehIxUqVSUlJZfGCefTr3ZNtO3aiq6cHwI+zfDl+9Ah+cxdgYGDAzBlTGTZkIGs3/AJA6PXrDOzXmx69+zLVdxaPHz1ixhQfUlNT8R4xCoAH//zD0IH96dzVi+kzZxMX94If/XwZNmQQv2zZ9qlOX1Hr165i29ZfmTjFF1u70ly/cplpPuPQNzCgXccuSJLEqKEDKVCgAH7zF6Ovb8AvG9YyqG8Pftn2J7q6enJerVp/R+9+A+TPOjr/TpOemprKsEH9MC1sxsq1G3kSFcWUCWMoUKAA/QYO/ajn/D70dbW5dOMBP+8I4re5vbNs3zyvN8kpqXw3ZDmx8QkM6vwVu/0HUq31NF4mpLdrOlaxZcfiH/hxzT68Z20hJTWNKmWLkZb27+wXHo0cWDKhA5MW/8nh0zcoUECDinaW8vat+84TeOKq2rFXTO6CjnZBObg4VS3FqqldGTnnd3YduUwxc2MWjmvP0gkdaD98VV5cnpwTAUYxn0WAsbe3JzQ0FBsbG6pWrcry5cuxsbHB398fS0vLd2fwCSxZrv6PZPJ0Xxo1rMvVq1eoUbMWL168IGDb78zwm01tx/TJ7CZP9aX1N80IuRhMlaoO7Nu7mzJl7enTrz8AJUtaM3jYcEYNG0qfH/qjr2/A1auXSUtLo/+gIWhopD/x7OrVnaED+5OcnEzBggU/7onngUsXg2no/BX1GqT3LLSyKsa+vbu5euUSAPfD73H50kU2bd1BKbsyAIwcO4nmbg3Zt2c3rVq3lfPS0dGhsFmRbI9zKuhvwu7cZqH/agoXNqOsfXl6/zCQJQvn0rNvfwoW1MrjM/0w+/6+yr6/r2a7rXRJcxyr2FK9zTSu3Ul/sdSgGb9xd/8Mvm9ag7Xb099m6DesNUt/PcyPawLlfW/e+3f2Xk1NDX4c0Yax8wNYF/DvGxCv3/n3ZVUJiclqtR2zQga41C5L38kb5XWOVWy59zCapb8cAeDew2hW//43w7zU38b4KYhHX8r5LNpgBg8eTEREBACTJk1iz549lCxZkoULFzJjxoxPXLqciYt7AYCxsTEA165eISUlmTp16sppbEuVwsLSipCLwQAkJSdlebeEtrYOiYmJXPv/K1IrVKiESqVix/ZtpKam8uLFC3b9+QeOdZy+iOACULmqA2dOnyT83l0AboZe52LweZzqNQCQexVqaf17rTQ0NCiopcXFYPUa7l+7d+LuWpeObb9h6cK5JLx6JW+7HHIRu9JlKFz430eUderWJz4ujju3b+XV6X0U2lrp95IJSSnyOkmSSEpKoa6DHQBFChlQu4otUU/jOLTWm7v7Z7Bv1WDqOvz7WLdauRIUK1qItDSJoF9GcWffdAIW96OC3Ztv9Dq1qM3LhCS27w+W150KCaO4RSHc66dPM29uasi3bg7sPZ59gPyYVKqcL8LbfRY1mM6dO8v/XaNGDe7du8f169cpWbIkZpnaK7KTmJiY5R3ZqRpaOX4pkBLS0tL4ceYMHKpVp3SZsgBEP4miYMGCGL42zU3hwoWJfvIEgLp167Np/c/s2b2Txu5NiX7yhBX+6bOrRj1Jf25erHhxlq5YzahhQ5k+ZRKpqalUqerA4mUrPtr55bWu3XoRHxdPu2+bo6GpSVpqKn37D6ZJs5YA2NjYYmFhybJF8xg13gddXV1+2fAzjx9FEv3k3/YF96bNsbC0wqyIObduhrJkwVzu3bvLrDkLgfR2HNPC6t8nU9PC6dv+/zf5XIXejSQ84ilTB37DgGm/EP8qiUGdXSluUQgLs/SbHtvi6ec+rk8zxszbTkjoP3RqUZvdywdS47sZ3A6PktOM79uMUXO2ce9hNIO7NOKvlYOp4jGFZ7Evsxzb08OJ3/acVavVBF28Q7ex61g/szs6WgUpWFCTnUcuMWTmbx/harydiBvK+SxqMFOmTOHly3+/uHp6elSvXh19fX21qaqzk907s3+clf07s/OK77Qp3Lp1k5mz5+ZqP6d69RkybAQzpvjgWL0KrVo0oX6DhgBoqNL/dE+eRDHVZwItW3mw4dctrFq7noIFCzLcezBfykTZB/bt5a89O5kyYzbrNm1l4hRfNq5fw64/AgAoULAgM+csJPzeXRo7O+HiVIPzZ0/jVK+B2nRDHm2+p07d+pQuU5YmzVoyaaovRw7u55/74Z/ozD6elJQ02g9bSWlrcyKOzuZp0Fwa1izL3uNXSJPSZ8TW+H/r9urfj7P+j5NcDP2HkXO2cePuYzxbpb+MSuP/13PWqr8IOBDMhWv36T1pAxISrb+uluW4jlVsKV/KUu1xGkC5Uhb8OLItviv2ULfTLFr+sARrS1MWjWufl5chZ3LxRkvh7T6LGszkyZPp27cvenp6autfvnzJ5MmTmThx4hv3ze6d2akaH+9Z+szpUzh25DCr122gaKZXkhY2K0JycjIvYmPVajHR0dFqvci6eHajc1cvoqIeY2RkzMMHD1g0fy7Fi5cA4LdfNmFgYMiQYSPkfabPnE0TNxcuhVykSlWHvD/JPLZo/o907daTr5s0A6B0mbJERDzk5zUraf6NBwDlKlRk/W/biXvxguTkZAqZmtK9SzvKV6j0xnwrVk4foPvP/XCKlyhJ4cJmXL0copbm6dNoALW/yefqwrX71Gk/EyMDHbQKFuDJsziO/jycc1fTA2xEVPoEs9cytacAhIZFUsKiUHqaJ88BuH4nQt6elJzC3X+iKWFhmuWYXt86EXz9Pheu3VdbP6JbY4KCbzPv5/Q3K16++ZCXrxI5sMabyUt2Evkk55PdKk20wSjns6jBZExq+bqLFy9iapr1S53Zh7wz+0NIksTM6VM4eGA/y39aS7Hi6l2Gy1eoSIECBTl16t87u7thd4iMeJglKKhUKszNi6Kjo8PePbuwsLCk3P9fkZqQ8Epu3M+goZn+OS3ty3hXS0LCK1Qq9XPU1NDI9vwMDA0pZGpK+L27XL96hYYuX70x3xuh1wHkRv9KVapy+9ZNOagAnD55An0DA2xLlVbiVPKF2LgEnjyLw65kEapXKMnO/3dlvvcwmoePYyhrY66WvrS1OeERT4H0IJWQmEwZm39fTlWggAYlrUzlNBn0dbVo83X1LLUXAD1dLbWeaQCp///8rklu85pog1FOvq7BFCpUCJVKhUqlomzZsmpfvNTUVOLi4ujbt+8nLOGb+U6bwp7dO5m3cAn6+vo8+X9bgIGBITo6OhgaGuLRug1z/GZhbGyMvr4Bs2ZMo0pVB7UAs+6n1dStXx8NDQ0O7A9kzaqV+M2Zh6amJgANGrqw8ed1LF+2hCbNmvMyPp7FC+ZhaWVFufIVPsWpK65+Q1fWrl6OhaUltnaluXH9Gr9sWEcLj9ZymgOBezEpZIqFhSW3b95g7mxfGro0wtGpHpBeS9m3Zxd16zfEyMSEWzdCWTBnFtWq16RMWXsAHJ3qYVvKjsnjRzNg8DCio5+wfMlC2n7fAS2t/N2DDNJ/0O1K/NtDzqZYYaqULcaz2Jfcj3xGa7dqRD2L437kUyqVseLHEW3583AIB05el/eZt24/4/s259KNB1wM/YfOLR2xtylKxxGrAXgRn8CqrceZ0LcZ/0Q+IzziKUM903t+bQtU71DR1r0GBTQ1+GWX+tgjgF1HLrF0Qkd6fVefwBPXsDQzZvaINpy5dJeIqOd5cXlyTMQN5eTrN1quW7cOSZLo3r078+fPl3tgAWhpaWFjY4OTk1Ou8/0Yb7SsVqlctusnT5vBN///YcwYaLl39y6SkpOoW7c+YyZMxCxTN9re3T25du0qyUlJlLUvR+9+/eV2mAx7d+9i3ZrV3Lt7Fx1dHapUdWDw0OHYlipFXvsYb7SMj49nxdKFHDm4n2fPnmJWxJyvmzSjR+9+ctfh3zatZ+PPa9IHYpoVoWmLVnTv3Vfe/igyAp9xo7h9+yYJr15hXtQC56/c6N6zL/oGBvKxIh4+wG/GFM6fO4Ouji7NWrbih0HeH2Wg5Ye+0bJBjTLsW5U1j/V/nKT3pA380MGZoV3dMC9sSOSTWDbuPIXvir0kp6hPtTS829f0+b4hhYz1uHTjAePmB3Ai+I68vUABDaYObEWH5rXQ1S7Imcv3GDF7a5ZHa4fWenP3QTTdxq3Ltrz92jvTs219bKwK8zzuFYdPhzJ+wQ4efmCA+dA3Wt589Ordif6vTFHddyf6D8vXASbDkSNHqFu3rmLdbsUrk5UjXpmsHPHKZGV8aIC59TjnAaa0uQgwb/NZtME4OzvLwSUhIYHY2Fi1RRAEQSl51YnM19eXWrVqYWhoiLm5OR4eHoSGhqqlcXFxkZsFMpbXmwHCw8Np3rw5enp6mJubM2LECFJSUtTSHD58mOrVq6OtrU3p0qVZu3ZtlvIsWbIEGxsbdHR0cHR05PTp07k8o3f7LALMy5cvGTBgAObm5ujr61OoUCG1RRAEQTF5FGGOHDlC//79OXnyJIGBgSQnJ9O4cWPi49XnXuvVqxcRERHy4ufnJ29LTU2lefPmJCUlceLECdatW8fatWvVetKGhYXRvHlzXF1dCQ4OZsiQIfTs2ZO//vpLTvPbb7/h7e3NpEmTOH/+PFWrVsXd3Z3Hjx+jpM/iEVn//v05dOgQU6dOpUuXLixZsoQHDx6wfPlyZs6cSadOnXKVn3hEphzxiEw54hGZMj70EdmdqIQcpy1mpMoykFtbWztHPVWjoqIwNzfnyJEjNGyY3q7q4uKCg4MD8+fPz3afPXv20KJFCx4+fEjRouk9+fz9/Rk1ahRRUVFoaWkxatQodu3axeXLl+X92rdvT0xMDHv37gXA0dGRWrVqsXhx+rVKS0ujRIkSDBw4kNGjR+f4/N/ls6jB/PnnnyxdupQ2bdpQoEABGjRowPjx45kxYwYbN258dwaCIAg5lJtuytkN5Pb1zdlA7ufP0zszvD7UYuPGjZiZmVGpUiXGjBmjNsg8KCiIypUry8EFwN3dndjYWK78f/qooKAg3NzU53Rzd3cnKCi9u3hSUhLnzp1TS6OhoYGbm5ucRin5uptyhqdPn1Lq/z2ijIyMePo0vb99/fr16dev36csmiAIX5jcPPnKbiB3TmovaWlpDBkyhHr16lGp0r+DgTt27Ii1tTVWVlaEhIQwatQoQkND2bYtfWb0yMhIteACyJ8jIyPfmiY2NpZXr17x7NkzUlNTs01z/fp1lPRZBJhSpUoRFhZGyZIlKVeuHJs3b6Z27dr8+eefmJiYfOriCYLwJclFhMnp47DX9e/fn8uXL3P8+HG19b17//uahcqVK2NpaUmjRo24ffs2dnZ2uT7Op5avH5HduXOHtLQ0unXrxsWLFwEYPXo0S5YsQUdHh6FDhzJixIh35CIIgpBzef1GywEDBrBz504OHTpE8eJvfymgo6MjALdupc/mbWFhwaNHj9TSZHy2+P9UVG9KY2RkhK6uLmZmZmhqamabxiLTdFZKyNcBpkyZMjx58oShQ4cyaNAg2rVrR+XKlbl+/TqbNm3iwoULDB4sGkYFQVCOhirnS25IksSAAQPYvn07Bw8exNbW9p37BAcHA8jvvXJycuLSpUtqvb0CAwMxMjKiwv+nj3JycuLAgQNq+QQGBsqD0rW0tKhRo4ZamrS0NA4cOPBeA9ffJl/3ItPQ0CAyMlJ+m6WhoSEXL16U22Pel+hFphzRi0w5oheZMj60F9k/zxLfnej/ihfK+eOxH374gU2bNrFjxw7s7e3l9cbGxujq6nL79m02bdpEs2bNKFy4MCEhIQwdOpTixYtz5Ej6i9lSU1NxcHDAysoKPz8/IiMj6dKlCz179pTfjRUWFkalSpXo378/3bt35+DBgwwaNIhdu3bh7u4OpHdT9vT0ZPny5dSuXZv58+ezefNmrl+/nqVt5kN8Fm0wgiAIH0/ezEa2bNkyIL0rcmZr1qzBy8sLLS0t9u/fz/z584mPj6dEiRK0adOG8ePHy2k1NTXZuXMn/fr1w8nJCX19fTw9PdVeW2Jra8uuXbsYOnQoCxYsoHjx4qxatUoOLgDt2rUjKiqKiRMnEhkZiYODA3v37lU0uEA+r8FoamoSGRlJkSLpc3MZGhoSEhKSo6rl24gajHJEDUY5ogajjA+twTyIScpx2mIm+X8S1E8pX9dgJEnCy8tL7qWRkJBA37590dfXV0uX0YVPEAThQ4nZlJWTrwOMp6en2ufMr04WBEHIC+I9L8rJ1wFmzZo1n7oIgiD8x4g3WionXwcYQRCEj07EF8WIACMIgpCJiC/KEQFGEAQhEw3RCKMYEWAEQRAyE/FFMSLACIIgZCLii3JEgBEEQchEPCFTjggwgiAImYhuysoRAUYQBCETUYNRTr6erl8QBEH4fIkajCAIQiaiBqMcEWAEQRAyEW0wyhEBRhAEIRNRg1GOCDCCIAiZiACjHBFgBEEQMhGPyJQjAowgCEImogajHBFgBEEQMhHxRTkiwAiCIGQmIoxiRIARBEHIRLTBKEcEGEEQhExEG4xyVJIkSZ+6EEJWiYmJ+Pr6MmbMGLS1tT91cT5b4joqR1xLIbdEgMmnYmNjMTY25vnz5xgZGX3q4ny2xHVUjriWQm6JyS4FQRCEPCECjCAIgpAnRIARBEEQ8oQIMPmUtrY2kyZNEo2pH0hcR+WIaynklmjkFwRBEPKEqMEIgiAIeUIEGEEQBCFPiAAjCIIg5AkRYD4BlUpFQEAAAHfv3kWlUhEcHJzj/deuXYuJiUmelO1L4uLiwpAhQz51Mf7zDh8+jEqlIiYm5lMXRfjIRIDJA1FRUfTr14+SJUuira2NhYUF7u7u/P3331nSlihRgoiICCpVqqRoGby8vPDw8FA0z7ySm+uVG9u2bWPq1KkKlfLL4eXlhUqlyrLcunXrrfuJGxsht8Rkl3mgTZs2JCUlsW7dOkqVKsWjR484cOAA0dHRWdJqampiYWHxCUqZf+TmeuVEUlISWlpamJqaKlzSL0eTJk1Ys2aN2roiRYp8otIIXyxJUNSzZ88kQDp8+PAb0wDS9u3bJUmSpLCwMAmQLly4IG/fsWOHVLp0aUlbW1tycXGR1q5dKwHSs2fPJEmSpDVr1kjGxsbS3r17pXLlykn6+vqSu7u79PDhQ0mSJGnSpEkSoLYcOnQoj874w+Tkej179kzq0aOHZGZmJhkaGkqurq5ScHCwvH3SpElS1apVpZUrV0o2NjaSSqWSJEmSnJ2dpcGDB8vpnj59KnXp0kUyMTGRdHV1pSZNmkg3btzIkk9m8+bNk6ytreXPhw4dkmrVqiXp6elJxsbGUt26daW7d+9+2EX4yDw9PaVWrVplWT9nzhypUqVKkp6enlS8eHGpX79+0osXLyRJSj/v179TkyZNkiRJkn7++WepRo0akoGBgVS0aFGpQ4cO0qNHj+R8M/bN+P4K/x3iEZnCDAwMMDAwICAggMTExFzvHxYWRtu2bfHw8ODixYv06dOHcePGZUn38uVLfvzxR9avX8/Ro0cJDw9n+PDhAAwfPpzvv/+eJk2aEBERQUREBHXr1v3gc8sLOble3333HY8fP2bPnj2cO3eO6tWr06hRI54+fSqnuXXrFr///jvbtm17Y3uWl5cXZ8+e5Y8//iAoKAhJkmjWrBnJyck5KmtKSgoeHh44OzsTEhJCUFAQvXv3RvWFzO+uoaHBwoULuXLlCuvWrePgwYOMHDkSgLp16zJ//nyMjIzk71TG9y05OZmpU6dy8eJFAgICuHv3Ll5eXp/wTIR841NHuC/R1q1bpUKFCkk6OjpS3bp1pTFjxkgXL16Ut/OWGsyoUaOkSpUqqeU3bty4LDUYQLp165acZsmSJVLRokXlz2+6S82P3na9jh07JhkZGUkJCQlq+9jZ2UnLly+XJCm95lGwYEHp8ePHamky12Bu3LghAdLff/8tb3/y5Imkq6srbd68Wc7nbTWY6Ojod9a2Pgeenp6SpqampK+vLy9t27bNkm7Lli1S4cKF5c8ZNed3OXPmjARkqf2IGsx/j6jB5IE2bdrw8OFD/vjjD5o0acLhw4epXr06a9eufee+oaGh1KpVS21d7dq1s6TT09PDzs5O/mxpacnjx48/uOyfwtuu18WLF4mLi6Nw4cJybcfAwICwsDBu374t52Ftbf3WNoRr165RoEABHB0d5XWFCxfG3t6ea9eu5aicpqameHl54e7uTsuWLVmwYAERERHvf+KfkKurK8HBwfKycOFC9u/fT6NGjShWrBiGhoZ06dKF6OhoXr58+da8zp07R8uWLSlZsiSGhoY4OzsDEB4e/jFORcjHRIDJIzo6Onz99ddMmDCBEydO4OXlxaRJkxTLv2DBgmqfVSoV0mc868+brldcXByWlpZqP4bBwcGEhoYyYsQIeX99ff0PLoOGhkaWa/j647M1a9YQFBRE3bp1+e233yhbtiwnT5784GN/bPr6+pQuXVpeEhMTadGiBVWqVOH333/n3LlzLFmyBEjvNPEm8fHxuLu7Y2RkxMaNGzlz5gzbt29/537Cf4MIMB9JhQoViI+Pf2c6e3t7zp49q7buzJkzuT6elpYWqampud4vv8i4XtWrVycyMpICBQqo/SCWLl0aMzOzHOdXvnx5UlJSOHXqlLwuOjqa0NBQKlSoAKT3ooqMjFQLMtm151SrVo0xY8Zw4sQJKlWqxKZNm97/RPOJc+fOkZaWxpw5c6hTpw5ly5bl4cOHammy+05dv36d6OhoZs6cSYMGDShXrtxnW5MWlCcCjMKio6P56quv2LBhAyEhIYSFhbFlyxb8/Pxo1arVO/fv06cP169fZ9SoUdy4cYPNmzfLj9Zy05hsY2NDSEgIoaGhPHnyJMcN2R/bu66Xm5sbTk5OeHh4sG/fPu7evcuJEycYN25clkD8NmXKlKFVq1b06tWL48ePc/HiRTp37kyxYsXkv4uLiwtRUVH4+flx+/ZtlixZwp49e+Q8wsLCGDNmDEFBQdy7d499+/Zx8+ZNypcvr/h1+dhKly5NcnIyixYt4s6dO6xfvx5/f3+1NDY2NsTFxXHgwAGePHnCy5cvKVmyJFpaWvJ+f/zxhxh7JPzrE7cBfXESEhKk0aNHS9WrV5eMjY0lPT09yd7eXho/frz08uVLSZJy30152bJlEiC9evVKkqTsG1u3b98uZf5zPn78WPr6668lAwODfN1NOSfXKzY2Vho4cKBkZWUlFSxYUCpRooTUqVMnKTw8XJKk7BvnJenN3ZSNjY0lXV1dyd3dXa2bsiRJ0rJly6QSJUpI+vr6UteuXaXp06fLjfyRkZGSh4eHZGlpKWlpaUnW1tbSxIkTpdTU1Dy5NnnlTR1A5s6dK1laWsrX5ueff87SON+3b1+pcOHCat2UN23aJNnY2Eja2tqSk5OT9Mcff6h9p0Uj/3+XmK7/MzB9+nT8/f25f//+py6KIAhCjomR/PnQ0qVLqVWrFoULF+bvv/9m9uzZDBgw4FMXSxAEIVdEgMmHbt68ybRp03j69CklS5Zk2LBhjBkz5lMXSxAEIVfEIzJBEAQhT4heZIIgCEKeEAFGEARByBMiwAiCIAh5QgQYQRAEIU+IACMIgiDkCRFghE/q9Vc7u7i4MGTIkI9ejo/x3nglXmMt3m8vfE5EgBGyyPzOdi0tLUqXLs2UKVNISUnJ82Nv27Ytx3NZfewfWxsbG+bPn/9RjiUIXwIx0FLIVsY72xMTE9m9ezf9+/enYMGC2Q74TEpKQktLS5HjmpqaKpKPIAifnqjBCNnS1tbGwsICa2tr+vXrh5ubG3/88Qfw76Oe6dOnY2Vlhb29PQD379/n+++/x8TEBFNTU1q1asXdu3flPFNTU/H29sbExITChQszcuTILO9fef0RWWJiIqNGjaJEiRJoa2tTunRpVq9ezd27d3F1dQWgUKFCqFQq+TW9aWlp+Pr6Ymtri66uLlWrVmXr1q1qx9m9ezdly5ZFV1cXV1dXtXK+j9TUVHr06CEf097engULFmSbdvLkyRQpUgQjIyP69u2r9t6UnJRdED4XogYj5Iiuri7R0dHy5wMHDmBkZERgYCCQ/mIud3d3nJycOHbsGAUKFGDatGk0adKEkJAQtLS0mDNnDmvXruWnn36ifPnyzJkzh+3bt/PVV1+98bhdu3YlKCiIhQsXUrVqVcLCwnjy5AklSpTg999/p02bNoSGhmJkZISuri4Avr6+bNiwAX9/f8qUKcPRo0fp3LkzRYoUwdnZmfv379O6dWv69+9P7969OXv2LMOGDfug65OWlkbx4sXZsmULhQsX5sSJE/Tu3RtLS0u+//57teumo6PD4cOHuXv3Lt26daNw4cJMnz49R2UXhM/KJ53LWciXMk/nnpaWJgUGBkra2trS8OHD5e1FixaVEhMT5X3Wr18v2dvbS2lpafK6xMRESVdXV/rrr78kSZIkS0tLyc/PT96enJwsFS9eXG3q+MxT7IeGhkqAFBgYmG05s5sGPiEhQdLT05NOnDihlrZHjx5Shw4dJEmSpDFjxkgVKlRQ2z5q1Kh3TilvbW0tzZs3743bX9e/f3+pTZs28mdPT0/J1NRUio+Pl9ctW7ZMMjAwkFJTU3NUdjH1vfA5ETUYIVs7d+7EwMCA5ORk0tLS6NixIz4+PvL2ypUrq7W7XLx4kVu3bmFoaKiWT0JCArdv3+b58+dERETg6OgobytQoAA1a9Z846ueg4OD0dTUzNWd+61bt3j58iVff/212vqkpCSqVasGwLVr19TKAeDk5JTjY7zJkiVL+OmnnwgPD+fVq1ckJSXh4OCglqZq1aro6empHTcuLo779+8TFxf3zrILwudEBBghW66urixbtgwtLS2srKwoUED9q6Kvr6/2OS4ujho1arBx48YseRUpUuS9ypDxyCs34uLiANi1axfFihVT26atrf1e5ciJX3/9leHDhzNnzhycnJwwNDRk9uzZaq9ofpdPVXZByCsiwAjZ0tfXp3Tp0jlOX716dX777TfMzc0xMjLKNo2lpSWnTp2iYcOGAKSkpHDu3DmqV6+ebfrKlSuTlpbGkSNHcHNzy7I9owaV+T3xFSpUQFtbm/Dw8DfWfMqXLy93WMhw8uTJd5/kW/z999/UrVuXH374QV53+/btLOkuXrzIq1ev5OB58uRJDAwMKFGiBKampu8suyB8TkQvMkERnTp1wszMjFatWnHs2DHCwsI4fPgwgwYN4p9//gFg8ODBzJw5k4CAAK5fv84PP/zw1jEsNjY2eHp60r17dwICAuQ8N2/eDIC1tTUqlYqdO3cSFRVFXFwchoaGDB8+nKFDh7Ju3Tpu377N+fPnWbRoEevWrQOgb9++3Lx5kxEjRhAaGsqmTZtYu3Ztjs7zwYMHBAcHqy3Pnj2jTJkynD17lr/++osbN24wYcIEzpw5k2X/pKQkevTowdWrV9m9ezeTJk1iwIABaGho5KjsgvBZ+dSNQEL+86Z3tr9re0REhNS1a1fJzMxM0tbWlkqVKiX16tVLev78uSRJ6Y36gwcPloyMjCQTExPJ29tb6tq16xsb+SVJkl69eiUNHTpUsrS0lLS0tKTSpUtLP/30k7x9ypQpkoWFhaRSqSRPT09JktI7JsyfP1+yt7eXChYsKBUpUkRyd3eXjhw5Iu/3559/SqVLl5a0tbWlBg0aSD/99FOOGvmBLMv69eulhIQEycvLSzI2NpZMTEykfv36SaNHj5aqVq2a5bpNnDhRKly4sGRgYCD16tVLSkhIkNO8q+yikV/4nIgXjgmCIAh5QjwiEwRBEPKECDCCIAhCnhABRhAEQcgTIsAIgiAIeUIEGEEQBCFPiAAjCIIg5AkRYARBEIQ8IQKMIAiCkCdEgBEEQRDyhAgwgiAIQp4QAUYQBEHIE/8D/PRTUnw+YT8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save Model"
      ],
      "metadata": {
        "id": "I0o4DKUgxyHC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "from google.colab import files\n",
        "\n",
        "# Store all models and data in a dictionary\n",
        "model_data = {\n",
        "    'scaler': scaler,\n",
        "    'pca': pca,\n",
        "    'faiss_index': index,\n",
        "    'label_mapping': label_mapping,\n",
        "    'accuracy': accuracy,\n",
        "    'confusion_matrix': conf_matrix,\n",
        "    'classification_report': class_report,\n",
        "   # 'y_balanced': y_balanced  # Add this line\n",
        "}\n",
        "\n",
        "# Save the model data dictionary to a single file using pickle\n",
        "with open('Causality_data.pkl', 'wb') as file:\n",
        "    pickle.dump(model_data, file)\n",
        "\n",
        "# Download the saved model data file\n",
        "files.download('Causality_data.pkl')\n"
      ],
      "metadata": {
        "id": "9ULvkbMkHC_i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ebd8c9b9-c74a-47df-8b07-02631f77662a"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e12c3fa4-ed75-4881-b9bd-8474a056d6fd\", \"Causality_data.pkl\", 35364279)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load Model & Predict\n"
      ],
      "metadata": {
        "id": "DEvPk9ZjD9Vn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import faiss  # Faiss for efficient KNN\n",
        "from collections import Counter\n",
        "\n",
        "# Load the saved model data\n",
        "with open('Causality_data.pkl', 'rb') as file:\n",
        "    model_data = pickle.load(file)\n",
        "\n",
        "# Extract necessary components from the loaded model data\n",
        "scaler = model_data['scaler']\n",
        "pca = model_data['pca']\n",
        "index = model_data['faiss_index']\n",
        "label_mapping = model_data['label_mapping']\n",
        "\n",
        "# Function to predict using Faiss\n",
        "def faiss_predict(X_test):\n",
        "    # Scale and apply PCA to the test set\n",
        "    X_test_scaled = scaler.transform(X_test)\n",
        "    X_test_pca = pca.transform(X_test_scaled)\n",
        "\n",
        "    # Ensure that X_test_pca is in the float32 format\n",
        "    X_test_pca = np.array(X_test_pca, dtype=np.float32)\n",
        "\n",
        "    # Perform nearest neighbor search\n",
        "    k = 3  # Number of neighbors\n",
        "    distances, indices = index.search(X_test_pca, k)\n",
        "\n",
        "    # Initialize predictions array\n",
        "    y_pred = np.zeros(X_test.shape[0], dtype=int)\n",
        "    for i in range(X_test.shape[0]):\n",
        "        # Get the labels of the nearest neighbors from the original labels\n",
        "        neighbor_labels = y[indices[i]]\n",
        "        # Use majority vote for classification\n",
        "        y_pred[i] = np.bincount(neighbor_labels).argmax()\n",
        "\n",
        "    return y_pred\n",
        "\n",
        "# Function to get user input for features\n",
        "def get_user_input():\n",
        "    sex_of_casualty = int(input(\"Enter Sex of Casualty (0 for Female, 1 for Male): \"))\n",
        "    age_of_casualty = int(input(\"Enter Age of Casualty: \"))\n",
        "    car_passenger = int(input(\"Enter if the casualty was a Car Passenger (0 for No, 1 for Yes): \"))\n",
        "    bus_or_coach_passenger = int(input(\"Enter if the casualty was a Bus/Coach Passenger (0 for No, 1 for Yes): \"))\n",
        "    casualty_type = int(input(\"Enter Casualty Type (1 for Driver, 2 for Passenger, 3 for Pedestrian): \"))\n",
        "\n",
        "    return pd.DataFrame({\n",
        "        'Sex_of_Casualty': [sex_of_casualty],\n",
        "        'Age_of_Casualty': [age_of_casualty],\n",
        "        'Car_Passenger': [car_passenger],\n",
        "        'Bus_or_Coach_Passenger': [bus_or_coach_passenger],\n",
        "        'Casualty_Type': [casualty_type]\n",
        "    })\n",
        "\n",
        "# Get user input\n",
        "X_test_user = get_user_input()\n",
        "\n",
        "# Make predictions on the user input using Faiss\n",
        "y_pred_numeric = faiss_predict(X_test_user)\n",
        "\n",
        "# Map predictions back to original labels\n",
        "y_pred_labels = [label_mapping[label] for label in y_pred_numeric]\n",
        "\n",
        "# Print predictions\n",
        "for pred in y_pred_labels:\n",
        "    print(f\"Predicted Casualty Severity: {pred}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oc3Hye3CC7NU",
        "outputId": "66d1d536-d78a-49db-ef05-dd08e46d9fe6"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter Sex of Casualty (0 for Female, 1 for Male): 1\n",
            "Enter Age of Casualty: 55\n",
            "Enter if the casualty was a Car Passenger (0 for No, 1 for Yes): 0\n",
            "Enter if the casualty was a Bus/Coach Passenger (0 for No, 1 for Yes): 0\n",
            "Enter Casualty Type (1 for Driver, 2 for Passenger, 3 for Pedestrian): 2\n",
            "Predicted Casualty Severity: Slight\n"
          ]
        }
      ]
    }
  ]
}