HSBC_AI_based_fraud_detection.ipynb

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      "metadata": {
        "id": "GHx4Pbsa_228"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
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      "execution_count": null,
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
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        "id": "pz5_eBhb__Dz",
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              "(513643, 10)"
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          "execution_count": 3
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      ],
      "source": [
        "df = pd.read_csv('/content/train_hsbc_df - train_hsbc_df.csv')\n",
        "#shape the data\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
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          "height": 206
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              "   step      customer age gender zipcodeOri      merchant zipMerchant  \\\n",
              "0     0   C583110837'  3'     M'     28007'   M480139044'      28007'   \n",
              "1     0  C1332295774'  3'     M'     28007'   M480139044'      28007'   \n",
              "2     0  C1160421902'  3'     M'     28007'   M857378720'      28007'   \n",
              "3     0   C966214713'  3'     M'     28007'   M857378720'      28007'   \n",
              "4     0  C1450140987'  4'     F'     28007'  M1198415165'      28007'   \n",
              "\n",
              "                category  amount  fraud  \n",
              "0             es_health'   44.26      1  \n",
              "1             es_health'  324.50      1  \n",
              "2      es_hotelservices'  176.32      1  \n",
              "3      es_hotelservices'  337.41      1  \n",
              "4  es_wellnessandbeauty'  220.11      1  "
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 513643 entries, 0 to 513642\n",
            "Data columns (total 10 columns):\n",
            " #   Column       Non-Null Count   Dtype  \n",
            "---  ------       --------------   -----  \n",
            " 0   step         513643 non-null  int64  \n",
            " 1   customer     513643 non-null  object \n",
            " 2   age          513643 non-null  object \n",
            " 3   gender       513643 non-null  object \n",
            " 4   zipcodeOri   513643 non-null  object \n",
            " 5   merchant     513643 non-null  object \n",
            " 6   zipMerchant  513643 non-null  object \n",
            " 7   category     513643 non-null  object \n",
            " 8   amount       513643 non-null  float64\n",
            " 9   fraud        513643 non-null  int64  \n",
            "dtypes: float64(1), int64(2), object(7)\n",
            "memory usage: 39.2+ MB\n"
          ]
        }
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        "df.info()"
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              "step           0\n",
              "customer       0\n",
              "age            0\n",
              "gender         0\n",
              "zipcodeOri     0\n",
              "merchant       0\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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            "text/plain": [
              "                step         amount          fraud\n",
              "count  513643.000000  513643.000000  513643.000000\n",
              "mean       94.992629      37.915481       0.012071\n",
              "std        51.063474     111.557455       0.109201\n",
              "min         0.000000       0.000000       0.000000\n",
              "25%        52.000000      13.740000       0.000000\n",
              "50%        97.000000      26.920000       0.000000\n",
              "75%       139.000000      42.550000       0.000000\n",
              "max       179.000000    7665.560000       1.000000"
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              "\n",
              "  <div id=\"df-99b9856c-1392-462a-b20a-90ba37eb6b76\" class=\"colab-df-container\">\n",
              "    <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>step</th>\n",
              "      <th>amount</th>\n",
              "      <th>fraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>513643.000000</td>\n",
              "      <td>513643.000000</td>\n",
              "      <td>513643.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94.992629</td>\n",
              "      <td>37.915481</td>\n",
              "      <td>0.012071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>51.063474</td>\n",
              "      <td>111.557455</td>\n",
              "      <td>0.109201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>52.000000</td>\n",
              "      <td>13.740000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>97.000000</td>\n",
              "      <td>26.920000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139.000000</td>\n",
              "      <td>42.550000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>179.000000</td>\n",
              "      <td>7665.560000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\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-99b9856c-1392-462a-b20a-90ba37eb6b76');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\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-a1e25d40-e215-4ff6-8124-d7fafe73e0a9 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"step\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181569.26861994262,\n        \"min\": 0.0,\n        \"max\": 513643.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          94.99262912178303,\n          97.0,\n          513643.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181220.95840450292,\n        \"min\": 0.0,\n        \"max\": 513643.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          37.91548110263355,\n          26.92,\n          513643.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181600.16757203406,\n        \"min\": 0.0,\n        \"max\": 513643.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.012070640503228897,\n          1.0,\n          0.10920148056465895\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1hFznIiKAZBE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebc441f3-f221-498b-c1c1-f4cab8b31a16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skewness of each column:\n",
            "step      -0.119017\n",
            "amount    32.196979\n",
            "fraud      8.936349\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "# Identify columns with object (string) dtype\n",
        "object_columns = df.select_dtypes(include='object').columns\n",
        "\n",
        "# Exclude object columns from skewness calculation\n",
        "numeric_df = df.drop(columns=object_columns)\n",
        "skewness = numeric_df.skew()\n",
        "\n",
        "print(\"Skewness of each column:\")\n",
        "print(skewness)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b6xK9MC4AZDi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b35e2cd2-b68b-4d78-e6d9-e306981b0298"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "for column in df.select_dtypes(include=['float64', 'int64']).columns:\n",
        "    plt.figure(figsize=(6,4))\n",
        "    plt.hist(df[column], bins=30, edgecolor='k')\n",
        "    plt.title(f'Distribution of {column}')\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frequency')\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building the Random Forest Model"
      ],
      "metadata": {
        "id": "7RNXmR4jM5qY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LC3v3bh8AZH5",
        "outputId": "728ed548-bf72-4fd1-bd5a-28b95b35c052"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of fraudulent transactions \t: 6200\n",
            "Number of non-fraudulent transactions \t: 507443\n"
          ]
        }
      ],
      "source": [
        "print('Number of fraudulent transactions \\t: {}'.format(df['fraud'].sum()))\n",
        "print('Number of non-fraudulent transactions \\t: {}'.format(len(df[df['fraud']==0])))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VvyZrSUNB2Sc"
      },
      "source": [
        "Splitting the dataset into training and testing dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMHkTzx-CLSq"
      },
      "source": [
        "tringin the dataset on random  forest classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBYRl8gVCmG9",
        "outputId": "08892c97-f357-47c2-ec84-a95258f143f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training and testing datasets saved as 'training_data.csv' and 'testing_data.csv'.\n"
          ]
        }
      ],
      "source": [
        "# Import the necessary libraries\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Load the dataset\n",
        "data = pd.read_csv('/content/train_hsbc_df - train_hsbc_df.csv')\n",
        "\n",
        "# Assuming 'fraud' is the target column indicating fraudulent transactions\n",
        "X = data.drop(columns=['fraud'])  # Features\n",
        "y = data['fraud']  # Target variable\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Combine X and y back together for training and testing sets\n",
        "train_set = pd.concat([X_train, y_train], axis=1)\n",
        "test_set = pd.concat([X_test, y_test], axis=1)\n",
        "\n",
        "# Save the training and testing sets to CSV files\n",
        "train_set.to_csv('training_data.csv', index=False)\n",
        "test_set.to_csv('testing_data.csv', index=False)\n",
        "\n",
        "print(\"Training and testing datasets saved as 'training_data.csv' and 'testing_data.csv'.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FphDe3TLDSZs"
      },
      "source": [
        "Loading the datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XPSDR6NiDJe7"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the training and testing datasets\n",
        "train_set = pd.read_csv('/content/training_data.csv')\n",
        "test_set = pd.read_csv('/content/testing_data.csv')\n",
        "\n",
        "# Separate features and target variable\n",
        "X_train = train_set.drop(columns=['fraud'])\n",
        "y_train = train_set['fraud']\n",
        "\n",
        "X_test = test_set.drop(columns=['fraud'])\n",
        "y_test = test_set['fraud']\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1O8kYpNDVIU"
      },
      "source": [
        "Handle Missing Values and Encode Categorical Variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JhWE6f15DYK0"
      },
      "outputs": [],
      "source": [
        "# Handle missing values (if any)\n",
        "X_train = X_train.fillna(0)\n",
        "X_test = X_test.fillna(0)\n",
        "\n",
        "# Encode categorical variables if there are any\n",
        "X_train = pd.get_dummies(X_train)\n",
        "X_test = pd.get_dummies(X_test)\n",
        "\n",
        "# Ensure the columns in the test set match those in the training set\n",
        "X_train, X_test = X_train.align(X_test, join='left', axis=1, fill_value=0)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "U2WfzhM8KEi9",
        "outputId": "efc1657d-7a11-4d93-9aee-7fc1952f9710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "step           0\n",
              "customer       0\n",
              "age            0\n",
              "gender         0\n",
              "zipcodeOri     0\n",
              "merchant       0\n",
              "zipMerchant    0\n",
              "category       0\n",
              "amount         0\n",
              "fraud          0\n",
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        "outputId": "abd74084-e7d2-4976-f1ab-91dcf5dee247"
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      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 513643 entries, 0 to 513642\n",
            "Data columns (total 10 columns):\n",
            " #   Column       Non-Null Count   Dtype  \n",
            "---  ------       --------------   -----  \n",
            " 0   step         513643 non-null  int64  \n",
            " 1   customer     513643 non-null  object \n",
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            " 3   gender       513643 non-null  object \n",
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            " 7   category     513643 non-null  object \n",
            " 8   amount       513643 non-null  float64\n",
            " 9   fraud        513643 non-null  int64  \n",
            "dtypes: float64(1), int64(2), object(7)\n",
            "memory usage: 39.2+ MB\n"
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    {
      "cell_type": "code",
      "source": [
        "data.describe().round()"
      ],
      "metadata": {
        "colab": {
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          "height": 300
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        "id": "p1BqYX4uKPxt",
        "outputId": "9791b3fc-af4b-48b0-d203-d69a78579e4d"
      },
      "execution_count": null,
      "outputs": [
        {
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              "           step    amount     fraud\n",
              "count  513643.0  513643.0  513643.0\n",
              "mean       95.0      38.0       0.0\n",
              "std        51.0     112.0       0.0\n",
              "min         0.0       0.0       0.0\n",
              "25%        52.0      14.0       0.0\n",
              "50%        97.0      27.0       0.0\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"step\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181569.27145543418,\n        \"min\": 0.0,\n        \"max\": 513643.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          95.0,\n          97.0,\n          513643.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181220.87082613344,\n        \"min\": 0.0,\n        \"max\": 513643.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.0,\n          27.0,\n          513643.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181600.17369721082,\n        \"min\": 0.0,\n        \"max\": 513643.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          513643.0,\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print ('Non Fraud % ',round(data['fraud'].value_counts()[0]/len(df)*100,2))\n",
        "print ()\n",
        "print (round(df.amount[data.fraud == 0].describe(),2))\n",
        "print ()\n",
        "print ()\n",
        "print ('Fraud %    ',round(data['fraud'].value_counts()[1]/len(df)*100,2))\n",
        "print ()\n",
        "print (round(data.amount[data.fraud == 1].describe(),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1mu0s6DKP14",
        "outputId": "ef39f0a2-89c6-4586-a40c-0f0137b8ebf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Non Fraud %  98.79\n",
            "\n",
            "count    507443.00\n",
            "mean         31.86\n",
            "std          31.34\n",
            "min           0.00\n",
            "25%          13.59\n",
            "50%          26.64\n",
            "75%          41.92\n",
            "max        1993.68\n",
            "Name: amount, dtype: float64\n",
            "\n",
            "\n",
            "Fraud %     1.21\n",
            "\n",
            "count    6200.00\n",
            "mean      533.51\n",
            "std       837.93\n",
            "min         0.03\n",
            "25%       159.72\n",
            "50%       319.48\n",
            "75%       554.50\n",
            "max      7665.56\n",
            "Name: amount, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "sns.set_style('darkgrid')\n",
        "sns.barplot(x=df['fraud'].value_counts().index,y=df['fraud'].value_counts(), palette=[\"C1\", \"C8\"])\n",
        "plt.title('Non_Fraud X Fraud')\n",
        "plt.ylabel('Count')\n",
        "plt.xlabel('0:Non Fraud, 1:Fraud')\n",
        "print ('Non Fraud % ',round(data['fraud'].value_counts()[0]/len(data)*100,2))\n",
        "print ('Fraud %    ',round(data['fraud'].value_counts()[1]/len(data)*100,2));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "id": "auxJN50aKP4N",
        "outputId": "1f271432-5c0b-4456-aaf4-585c1a0ea296"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Non Fraud %  98.79\n",
            "Fraud %     1.21\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-b329690d4a56>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=df['fraud'].value_counts().index,y=df['fraud'].value_counts(), palette=[\"C1\", \"C8\"])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names = data.iloc[:, 1:30].columns\n",
        "target = data.iloc[:1, 9:].columns\n",
        "\n",
        "data_features = data[feature_names]\n",
        "data_target = data[target]"
      ],
      "metadata": {
        "id": "3dlGhHKLKP8X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feature_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4hb6tthKKP-z",
        "outputId": "46914952-2049-41ed-978e-220fa17d0951"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['customer', 'age', 'gender', 'zipcodeOri', 'merchant', 'zipMerchant',\n",
              "       'category', 'amount', 'fraud'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKwKI8tWKQA8",
        "outputId": "82103359-4d79-4404-defe-fe577e2a2a14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['fraud'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np #para funções matemátias básicas\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "np.random.seed(123)\n",
        "X_train, X_test, y_train, y_test = train_test_split(data_features, data_target,\n",
        "                                                    train_size = 0.70, test_size = 0.30, random_state = 1)"
      ],
      "metadata": {
        "id": "h_zvDzFmKQFU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BkQFrGpvAZFq",
        "outputId": "8281d16a-c39b-479c-9aeb-3e971529a310"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values in each column:\n",
            "step           0\n",
            "customer       0\n",
            "age            0\n",
            "gender         0\n",
            "zipcodeOri     0\n",
            "merchant       0\n",
            "zipMerchant    0\n",
            "category       0\n",
            "amount         0\n",
            "fraud          0\n",
            "dtype: int64\n",
            "Confusion Matrix:\n",
            "[[151852    381]\n",
            " [   806   1054]]\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      1.00    152233\n",
            "           1       0.73      0.57      0.64      1860\n",
            "\n",
            "    accuracy                           0.99    154093\n",
            "   macro avg       0.86      0.78      0.82    154093\n",
            "weighted avg       0.99      0.99      0.99    154093\n",
            "\n",
            "\n",
            "Accuracy Score:\n",
            "0.9922968596886296\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "# Load the dataset\n",
        "file_path = '/content/train_hsbc_df - train_hsbc_df.csv'\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "# Check for missing values\n",
        "print(\"Missing values in each column:\")\n",
        "print(df.isnull().sum())\n",
        "\n",
        "# If there are missing values, you might want to fill or drop them\n",
        "# df = df.dropna()  # Optionally drop rows with missing values\n",
        "\n",
        "# Assuming 'fraud' is the target variable and others are features\n",
        "X = df.drop(['fraud'], axis=1)\n",
        "y = df['fraud']\n",
        "\n",
        "# Identify and drop non-numeric columns (e.g., customer ID)\n",
        "non_numeric_columns = X.select_dtypes(exclude=['float64', 'int64']).columns\n",
        "X = X.drop(columns=non_numeric_columns)  # Drop non-numeric columns\n",
        "\n",
        "# Split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n",
        "\n",
        "# Standardize the data\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Initialize the Random Forest model\n",
        "model = RandomForestClassifier(n_estimators=100, random_state=42, class_weight='balanced')\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "# Evaluate the model\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "print(\"\\nAccuracy Score:\")\n",
        "print(accuracy_score(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Assuming 'data_features' is your DataFrame with features\n",
        "# Identify columns with string (object) dtype\n",
        "categorical_cols = data_features.select_dtypes(include=['object']).columns\n",
        "\n",
        "# Apply Label Encoding to convert string labels to numerical representations\n",
        "label_encoders = {}\n",
        "for col in categorical_cols:\n",
        "    le = LabelEncoder()\n",
        "    data_features[col] = le.fit_transform(data_features[col])\n",
        "    label_encoders[col] = le  # Store encoders for later use if needed\n",
        "\n",
        "# Split data into training and testing sets\n",
        "np.random.seed(123)\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    data_features, data_target, train_size=0.70, test_size=0.30, random_state=1\n",
        ")\n",
        "\n",
        "# Initialize and train the RandomForestClassifier\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 858
        },
        "id": "fsNZacErRNA2",
        "outputId": "f20cfadd-5fad-4515-81f7-5253892062f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-4d98dce452e1>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_features[col] = le.fit_transform(data_features[col])\n",
            "<ipython-input-24-4d98dce452e1>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_features[col] = le.fit_transform(data_features[col])\n",
            "<ipython-input-24-4d98dce452e1>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_features[col] = le.fit_transform(data_features[col])\n",
            "<ipython-input-24-4d98dce452e1>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_features[col] = le.fit_transform(data_features[col])\n",
            "<ipython-input-24-4d98dce452e1>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_features[col] = le.fit_transform(data_features[col])\n",
            "<ipython-input-24-4d98dce452e1>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_features[col] = le.fit_transform(data_features[col])\n",
            "<ipython-input-24-4d98dce452e1>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_features[col] = le.fit_transform(data_features[col])\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return fit_method(estimator, *args, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handle Multiple Categorical Columns"
      ],
      "metadata": {
        "id": "bQY9lPFuNnz3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = data.select_dtypes(include=['object', 'category']).columns.tolist()\n",
        "print(\"Categorical Columns:\", categorical_columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rvAkw3TENoUJ",
        "outputId": "22c95d90-2d8f-45fc-d758-a6c02523e864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Categorical Columns: ['customer', 'age', 'gender', 'zipcodeOri', 'merchant', 'zipMerchant', 'category']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# One-Hot Encoding for all categorical columns\n",
        "data_encoded = pd.get_dummies(data, columns=categorical_columns, drop_first=True)\n"
      ],
      "metadata": {
        "id": "tkJT0jkMN6cx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data_encoded.head())\n",
        "print(data_encoded.info())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZcRKu0xOImN",
        "outputId": "a1dca1b6-0722-40e6-a442-5e72b20eb1b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   step  amount  fraud  customer_C100045114'  customer_C1000699316'  \\\n",
            "0     0   44.26      1                 False                  False   \n",
            "1     0  324.50      1                 False                  False   \n",
            "2     0  176.32      1                 False                  False   \n",
            "3     0  337.41      1                 False                  False   \n",
            "4     0  220.11      1                 False                  False   \n",
            "\n",
            "   customer_C1001065306'  customer_C1002658784'  customer_C1002759277'  \\\n",
            "0                  False                  False                  False   \n",
            "1                  False                  False                  False   \n",
            "2                  False                  False                  False   \n",
            "3                  False                  False                  False   \n",
            "4                  False                  False                  False   \n",
            "\n",
            "   customer_C1004109477'  customer_C1004300450'  ...  category_es_home'  \\\n",
            "0                  False                  False  ...              False   \n",
            "1                  False                  False  ...              False   \n",
            "2                  False                  False  ...              False   \n",
            "3                  False                  False  ...              False   \n",
            "4                  False                  False  ...              False   \n",
            "\n",
            "   category_es_hotelservices'  category_es_hyper'  category_es_leisure'  \\\n",
            "0                       False               False                 False   \n",
            "1                       False               False                 False   \n",
            "2                        True               False                 False   \n",
            "3                        True               False                 False   \n",
            "4                       False               False                 False   \n",
            "\n",
            "   category_es_otherservices'  category_es_sportsandtoys'  category_es_tech'  \\\n",
            "0                       False                       False              False   \n",
            "1                       False                       False              False   \n",
            "2                       False                       False              False   \n",
            "3                       False                       False              False   \n",
            "4                       False                       False              False   \n",
            "\n",
            "   category_es_transportation'  category_es_travel'  \\\n",
            "0                        False                False   \n",
            "1                        False                False   \n",
            "2                        False                False   \n",
            "3                        False                False   \n",
            "4                        False                False   \n",
            "\n",
            "   category_es_wellnessandbeauty'  \n",
            "0                           False  \n",
            "1                           False  \n",
            "2                           False  \n",
            "3                           False  \n",
            "4                            True  \n",
            "\n",
            "[5 rows x 4187 columns]\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 513643 entries, 0 to 513642\n",
            "Columns: 4187 entries, step to category_es_wellnessandbeauty'\n",
            "dtypes: bool(4184), float64(1), int64(2)\n",
            "memory usage: 2.0 GB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data_encoded.drop('fraud', axis=1)  # replace with your actual target column name\n",
        "y = data_encoded['fraud']\n"
      ],
      "metadata": {
        "id": "7vP5O0rmOPac"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the random forest"
      ],
      "metadata": {
        "id": "ZMn7nGgTbmJd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "wDVbInaXOac1",
        "outputId": "93c60137-6af9-4d59-b56f-477b95b441d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return fit_method(estimator, *args, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=50, max_depth=5, random_state=42)\n",
        "rf.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "5ttues7suTiZ",
        "outputId": "9ebaaabe-4552-4085-92c3-57f162862342"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return fit_method(estimator, *args, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, n_estimators=50, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, n_estimators=50, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5, n_estimators=50, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# rf = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "GtINr9wfMvUU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "accuracy metrics calculation"
      ],
      "metadata": {
        "id": "L4lwomsgbul3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_accuracy(cmat):\n",
        "    tpos = cmat[0][0]  # True Positives\n",
        "    tneg = cmat[1][1]  # True Negatives\n",
        "    fpos = cmat[0][1]  # False Positives\n",
        "    fneg = cmat[1][0]  # False Negatives\n",
        "\n",
        "    accuracy = (tpos + tneg) / (tpos + tneg + fpos + fneg) if (tpos + tneg + fpos + fneg) > 0 else 0\n",
        "    print(\"Accuracy:\", accuracy)\n"
      ],
      "metadata": {
        "id": "fF3S69kTRugv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_precision(cmat):\n",
        "    tpos = cmat[0][0]  # True Positives\n",
        "    fpos = cmat[0][1]  # False Positives\n",
        "\n",
        "    precision = tpos / (tpos + fpos) if (tpos + fpos) > 0 else 0\n",
        "    print(\"Precision:\", precision)\n"
      ],
      "metadata": {
        "id": "i9T2s6K-Rx24"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_recall(cmat):\n",
        "    tpos = cmat[0][0]  # True Positives\n",
        "    fneg = cmat[1][0]  # False Negatives\n",
        "\n",
        "    recall = tpos / (tpos + fneg) if (tpos + fneg) > 0 else 0\n",
        "    print(\"Recall:\", recall)\n"
      ],
      "metadata": {
        "id": "sNuIvDfYb_Ex"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_f1_score(cmat):\n",
        "    tpos = cmat[0][0]  # True Positives\n",
        "    fpos = cmat[0][1]  # False Positives\n",
        "    fneg = cmat[1][0]  # False Negatives\n",
        "\n",
        "    precision = tpos / (tpos + fpos) if (tpos + fpos) > 0 else 0\n",
        "    recall = tpos / (tpos + fneg) if (tpos + fneg) > 0 else 0\n",
        "\n",
        "    f1_score = (2 * precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n",
        "    print(\"F1 Score:\", f1_score)\n"
      ],
      "metadata": {
        "id": "tfnR676WdIVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_specificity(cmat):\n",
        "    tneg = cmat[1][1]  # True Negatives\n",
        "    fpos = cmat[0][1]  # False Positives\n",
        "\n",
        "    specificity = tneg / (tneg + fpos) if (tneg + fpos) > 0 else 0\n",
        "    print(\"Specificity:\", specificity)\n"
      ],
      "metadata": {
        "id": "WPsc1DN9dIbx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Generate confusion matrix\n",
        "cmat = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Calculate metrics\n",
        "def calculate_metrics(cmat):\n",
        "    tpos = cmat[0][0]  # True Positives\n",
        "    fneg = cmat[1][0]  # False Negatives\n",
        "    fpos = cmat[0][1]  # False Positives\n",
        "    tneg = cmat[1][1]  # True Negatives\n",
        "\n",
        "    accuracy = (tpos + tneg) / (tpos + tneg + fpos + fneg) if (tpos + tneg + fpos + fneg) > 0 else 0\n",
        "    precision = tpos / (tpos + fpos) if (tpos + fpos) > 0 else 0\n",
        "    recall = tpos / (tpos + fneg) if (tpos + fneg) > 0 else 0\n",
        "    f1_score_value = (2 * precision * recall) / (precision + recall) if (precision + recall) > 0 else 0\n",
        "\n",
        "    return accuracy, precision, recall, f1_score_value\n",
        "\n",
        "# Get metrics\n",
        "accuracy, precision, recall, f1_score_value = calculate_metrics(cmat)\n"
      ],
      "metadata": {
        "id": "-pfEGk8XdIh4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
        "\n",
        "# Assuming you have `y_test` and `y_pred` from your model predictions\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred, average='binary')  # Use 'binary' or adjust for multi-class\n",
        "recall = recall_score(y_test, y_pred, average='binary')        # Use 'binary' or adjust for multi-class\n",
        "f1 = f1_score(y_test, y_pred, average='binary')                # Use 'binary' or adjust for multi-class\n",
        "roc_auc = roc_auc_score(y_test, y_pred)                  # Make sure y_pred_proba is the probability for the positive class\n"
      ],
      "metadata": {
        "id": "ulHNgAU3vHeD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Store metrics in a dictionary\n",
        "metrics = {\n",
        "    'Accuracy': accuracy,\n",
        "    'Precision': precision,\n",
        "    'Recall': recall,\n",
        "    'F1 Score': f1,\n",
        "    'ROC AUC': roc_auc\n",
        "}\n",
        "\n",
        "# Convert metrics dictionary to lists for plotting\n",
        "metrics_names = list(metrics.keys())\n",
        "metrics_values = list(metrics.values())\n",
        "\n",
        "# Plot each metric separately\n",
        "for metric_name, metric_value in zip(metrics_names, metrics_values):\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.barplot(x=[metric_name], y=[metric_value], palette='viridis')\n",
        "    plt.ylim(0, 1)\n",
        "    plt.title(f'{metric_name}')\n",
        "    plt.ylabel('Score')\n",
        "    plt.xlabel('Metric')\n",
        "    plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "SXi4LEKuvHlW",
        "outputId": "bdbc7807-b40f-4895-89bd-f997f5041585"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-41-66a5b8dda64c>:20: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=[metric_name], y=[metric_value], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-41-66a5b8dda64c>:20: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=[metric_name], y=[metric_value], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-41-66a5b8dda64c>:20: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=[metric_name], y=[metric_value], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-41-66a5b8dda64c>:20: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=[metric_name], y=[metric_value], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-41-66a5b8dda64c>:20: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=[metric_name], y=[metric_value], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gkaNNHUwvHre"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DiNDFXhXvHxF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Plot Accuracy\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=['Accuracy'], y=[accuracy], palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('Accuracy')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metric')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "2aqTbVLceqze",
        "outputId": "4bd6cf47-6e60-41f9-9d25-f120412f5abc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-3a779c9c6a58>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=['Accuracy'], y=[accuracy], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Plot Precision\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=['Precision'], y=[precision], palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('Precision')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metric')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "03YfhvSCdIlh",
        "outputId": "74c99aab-8271-4b3f-c4bf-81ff129b4ef9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-43-88b586af628a>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=['Precision'], y=[precision], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Plot Recall\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=['Recall'], y=[recall], palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('Recall')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metric')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "5auBI0GAewlX",
        "outputId": "8b9ab9c8-22b7-4166-b7db-15c50c6edd98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-4bd5cfee4c66>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=['Recall'], y=[recall], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Plot F1 Score\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=['F1 Score'], y=[f1_score_value], palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('F1 Score')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metric')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "WOCMpuKBeydy",
        "outputId": "352eb5b2-9e61-4e5c-9a48-f7d9ef1661ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-45-888ccc65fc84>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=['F1 Score'], y=[f1_score_value], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Plot Confusion Matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cmat, annot=True, fmt='d', cmap='Blues', xticklabels=['Predicted Negative', 'Predicted Positive'], yticklabels=['Actual Negative', 'Actual Positive'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "nJcjfyjQezpM",
        "outputId": "fbb36c58-92f8-4a4c-b0f9-de4848e788d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Accuracy\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=['Accuracy'], y=[accuracy], palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('Accuracy')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metric')\n",
        "plt.show()\n",
        "\n",
        "# Plot Precision\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=['Precision'], y=[precision], palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('Precision')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metric')\n",
        "plt.show()\n",
        "\n",
        "# Plot Recall\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=['Recall'], y=[recall], palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('Recall')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metric')\n",
        "plt.show()\n",
        "\n",
        "# Plot F1 Score\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=['F1 Score'], y=[f1_score_value], palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('F1 Score')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metric')\n",
        "plt.show()\n",
        "\n",
        "# Plot Confusion Matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cmat, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=['Predicted Negative', 'Predicted Positive'],\n",
        "            yticklabels=['Actual Negative', 'Actual Positive'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ma6gAcHKOa1i",
        "outputId": "d6b1e67c-56ba-403f-c519-74bd5f6ae300"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-364ef5b125c2>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=['Accuracy'], y=[accuracy], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-364ef5b125c2>:12: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=['Precision'], y=[precision], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-364ef5b125c2>:21: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=['Recall'], y=[recall], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-364ef5b125c2>:30: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=['F1 Score'], y=[f1_score_value], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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ktWvX5sMPP8TT05NPPvmEihUrMm/ePOBuVTQyMpJOnTrRsGFDypcvz9ixY7l06dIjq7l/pmRURERExIrD9CkpKSQmJlq8UlJSHjvUtLQ0evXqRUhICM8//3yG8/v27aNAgQJ4e3ubjwUEBGBnZ8fBgwcB2L9/P/7+/jg5OZnbBAYGcvLkSa5du2ZuU6tWLYu+AwMD2b9/PwDnzp0jNjaWgIAA8/n8+fNTpUoV9u3bl+n7UTIqIiIiYkURERFUrVrV4hUREfHY/c2YMQMHBweCg4MfeD4uLo5ChQpZHHNwcMDFxYXY2FhzGzc3N4s2997HxcU9tI2rq6v5/L2+XF1dH9omM7S1k4iIiMhjzO3MrA4dOtC2bVuLY/dXJLMiOjqayMhIli5dismKMT9JSkZFRERErMjJyemxk88/27NnD/Hx8bz44ovmY3fu3GHMmDFERkayadMm3NzcuHz5ssXnbt++zbVr13B3dwfuVkH/XL289/5eNfRBbeLj483n7/UVHx9vsYgqPj6e8uXLZ/qeNEwvIiIi8hRt7fQozZo1Y8WKFSxfvtz8Kly4MCEhIXz55ZcA+Pr6kpCQQHR0tPlzu3btIi0tjcqVKwPg4+PDnj17SE1NNbfZsWMHpUuXxsXFxdxm165dFtffsWMHPj4+AHh4eODu7s7OnTvN5xMTEzlw4AC+vr6ZvicloyIiIiJPkRs3bhATE0NMTAxwd6FQTEwMFy5coGDBgpQrV87i5ejoiJubG2XKlAHA09OT2rVrM2jQIA4ePMjPP//M8OHDadq0KUWKFAEgKCgIR0dHBgwYwC+//MKaNWuIjIy0mE4QHBzMtm3bmDlzJidOnGDKlClER0fTunVrAEwmE8HBwXz++eds3LiRY8eO0bt3bwoXLkzDhg0zfb8aphcRERF5iuZfRkdHWyxOCg8PB6B58+aMHj06U32MGzeO4cOH8/7772NnZ8dLL73EwIEDzefz58/PV199xbBhw2jRogUFCxakc+fOtGzZ0tzGz8+PcePGMXHiRCZMmECpUqWYNm0a5cqVM7f56KOPSE5OZvDgwSQkJFC1alW+/PJLcuXKlen7NaWnp6dnuvU/RB7frkaHICJWEv/TFKNDEBEryetoXEKYp/EEq/Wd/P2nVus7J9AwvYiIiIgYRsP0IiIiIk/RML2tUWVURERERAyjyqiIiIhINm/BJJmnb15EREREDKPKqIiIiIjmjBpGlVERERERMYwqoyIiIiKaM2oYJaMiIiIiSkYNo29eRERERAyjyqiIiIiIFjAZRpVRERERETGMKqMiIiIimjNqGH3zIiIiImIYVUZFRERENGfUMKqMioiIiIhhVBkVERER0ZxRwygZFREREdEwvWH0a4CIiIiIGEaVUREREbF5JlVGDaPKqIiIiIgYRpVRERERsXmqjBrnqaiM7tmzh549e9KyZUsuXrwIwPLly9mzZ4/BkYmIiIiINRmejK5bt46QkBBy587NkSNHSElJASAxMZGIiAiDoxMRERGbYLLiSx7J8GT0888/JywsjBEjRuDg8L9ZA35+fhw5csTAyERERETE2gyfM3ry5En8/f0zHM+fPz8JCQkGRCQiIiK2RnNGjWN4ZdTNzY0zZ85kOP7zzz9TokQJAyISERERW2Mymaz2kkczPBl9++23GTlyJAcOHMBkMnHx4kVWrFjBmDFjeOedd4wOT0RERESsyPBh+vbt25OWlsYHH3xAcnIyrVu3xsnJiXbt2tGmTRujwxMREREboAqmcQxPRk0mE506dSIkJIQzZ86QlJSEp6cn+fLlMzo0EREREbEyw5PR7777jpdeeok8efJQtmxZo8MRERERG6TKqHEMnzMaHh5OQEAAPXr0YOvWrdy5c8fokERERETkCTG8Mrp9+3a2bdvGqlWr+OSTT8idOzeNGzcmKCgIPz8/o8MTERERW6DCqGEMT0YdHBx48cUXefHFF0lOTmb9+vWsWrWK4OBgnn32WTZs2GB0iCIiIiJiJYYno/fLkycPgYGBJCQkcOHCBU6cOGF0SCIiImIDNGfUOE9FMnqvIrpy5Up27txJ0aJFadq0KZMmTTI6NBERERGxIsOT0e7du7NlyxZy587NK6+8QufOnfH19TU6LBEREbEhqowax/Bk1M7OjokTJxIYGIi9vb3R4YiIiIgNUjJqHMOT0fHjxxsdgoiIiIgYxJBkNDIykpYtW5IrVy4iIyMf2TY4OPgJRSUiIiK2SpVR4xiSjM6ePZugoCBy5crF7NmzH9rOZDIpGRURERHJwQxJRjdt2vTAP4uIiIgYQoVRwxj+ONCpU6eSnJyc4fjNmzeZOnWqARGJiIiIyJNieDI6bdo0kpKSMhxPTk5m2rRpBkQkIiIitsZkMlntJY9meDKanp7+wP9QR48excXFxYCIRERERORJMWxrp2rVqpl/Y3j55ZctEtI7d+6QlJREq1atjApPREREbIgqmMYxLBnt378/6enp9O/fn9DQUPLnz28+5+joSPHixfUkJhEREXkilIwax7BktHnz5gB4eHjg6+uLo6OjUaGIiIiIiEEMfwJT9erVzX++desWqampFuednZ2fdEgiIiJia1QYNYzhyWhycjKfffYZa9eu5erVqxnOx8TEPPmgREREROSJMHw1/dixY9m1axdDhw7FycmJESNGEBoaSuHChRkzZozR4YmIiIgNeJq2doqKiqJjx44EBgbi5eXFhg0bzOdSU1P57LPPCAoKwsfHh8DAQHr37s3Fixct+rh69So9evTAz88Pf39/+vfvz40bNyzaHD16lHfffRdvb2/q1q3LjBkzMsSydu1aGjdujLe3N0FBQWzdutXifHp6OpMmTSIwMJDKlSvzwQcfcOrUqSzdr+HJ6ObNmxkyZAgvv/wy9vb2+Pv707lzZ7p3787KlSuNDk9ERETkiUpKSsLLy4shQ4ZkOHfz5k2OHDlCp06dWLp0KVOnTuXkyZN06tTJol3Pnj359ddfmTVrFtOnT2fPnj0MHjzYfD4xMZGQkBCKFSvG0qVL6d27N1OnTmXRokXmNnv37qVHjx68+eabLF++nAYNGtClSxeOHz9ubjNjxgzmzp3L0KFDWbx4MXny5CEkJIRbt25l+n4NT0avXbtGiRIlgLvzQ69duwZA1apV2bNnj5GhiYiIiI14miqjdevWpXv37jRq1CjDufz58zNr1iyaNGlCmTJl8PHxYdCgQRw+fJgLFy4AcOLECbZt28aIESOoUqUK/v7+DBw4kNWrV5srqCtWrCA1NZVRo0bx/PPP07RpU9q0acOsWbPM14qMjKR27dp8+OGHeHp68sknn1CxYkXmzZsH3K2KRkZG0qlTJxo2bEj58uUZO3Ysly5dsqjm/hXDk1EPDw/OnTsHQJkyZVi7di1wt2J6/3ZPIiIiIv9EKSkpJCYmWrxSUlKyrf/ExERMJhMFChQAYN++fRQoUABvb29zm4CAAOzs7Dh48CAA+/fvx9/fHycnJ3ObwMBATp48aS4M7t+/n1q1allcKzAwkP379wNw7tw5YmNjCQgIMJ/Pnz8/VapUYd++fZmO3/Bk9I033uDo0aMAtG/fnvnz5+Pt7U14eDghISEGRyciIiK2wJqV0YiICKpWrWrxioiIyJa4b926xbhx42jatKl5B6K4uDgKFSpk0c7BwQEXFxdiY2PNbdzc3Cza3HsfFxf30Daurq7m8/f6cnV1fWibzDB8Nf0HH3xg/nNAQABr167l8OHDlCxZkvLlyxsXmIiIiNgMa25636FDB9q2bWtx7P6K5ONKTU3l448/Jj09nbCwsL/dn1EMT0b/rHjx4hQvXtzoMERERESyhZOTU7Ykn/dLTU3lk08+4cKFC8yZM8diX3Y3NzcuX75s0f727dtcu3YNd3d3c5s/Vy/vvb9XDX1Qm/j4ePP5e33Fx8dTuHBhizZZKSganoxGRkY+8LjJZCJXrlyULFmSatWqYW9v/4QjExEREZvxD9r0/l4ievr0aSIjIylYsKDFeV9fXxISEoiOjqZSpUoA7Nq1i7S0NCpXrgyAj48PEydOJDU11fwUzB07dlC6dGlcXFzMbXbt2mUxir1jxw58fHyAu+t+3N3d2blzJxUqVADuzl89cOAA77zzTqbvx/BkdPbs2Vy5coXk5GTzzV+7do08efKQN29e4uPjKVGiBJGRkRQtWtTgaEVERESs68aNG5w5c8b8/ty5c8TExODi4oK7uzvdunXjyJEjREREcOfOHfPcTRcXF5ycnPD09KR27doMGjSIsLAwUlNTGT58OE2bNqVIkSIABAUFMW3aNAYMGMBHH33EL7/8QmRkJP369TNfNzg4mDZt2jBz5kzq1q3LmjVriI6OZtiwYcDdwmFwcDCff/45zz33HB4eHkyaNInChQvTsGHDTN+vKT09PT07vrjHtWrVKhYtWsTIkSMpWbIkAKdPn2bw4MG0bNkSPz8/unfvjru7O5MnT85Un3l8u1ozZBExUPxPU4wOQUSsJK+jceXJ4p2WWa3v8583z1L73bt3ExwcnOF48+bN6dq1Kw0aNHjg5yIjI6lRowZwd9P74cOHs2nTJuzs7HjppZcYOHAg+fLlM7c/evQow4YN49ChQxQsWJDWrVvTvn17iz7Xrl3LxIkTOX/+PKVKlaJXr17UrVvXfD49PZ3JkyezePFiEhISqFq1KkOGDKF06dKZvl/Dk9GGDRsyZcoUc3n3niNHjhAaGsrGjRvZu3cv3bp1Y/v27ZnqU8moSM6lZFQk51IyapsMH6aPjY3l9u3bGY7fvn3bPGm2cOHCGR5hJSIiIpJdrLmaXh7N8H1Ga9SowZAhQzhy5Ij52JEjRxg6dCg1a9YE4Pjx43h4eBgVooiIiIhYieGV0ZEjR9K7d29atGiBg8PdcO7cuUOtWrUYOXIkAHnz5qVPnz5GhikiIiI5mCqjxjE8GXV3d2fWrFmcOHGCU6dOAVC6dGnKlCljbnOvQioiIiJiFcpFDWN4MnpPiRIlMJlMlCxZ0lwhFREREZGczfA5o8nJyfTv3x8fHx9effVVfv/9dwCGDx/OF198YXB0IiIiYgus+Wx6eTTDk9Hx48dz9OhRIiMjyZUrl/l4rVq1WLNmjYGRiYiIiIi1GT4evnHjRv7973+bHy11z/PPP2/x9AERERERa1EF0ziGV0YvX76Mq6trhuPJycn6P4aIiIhIDmd4MlqpUiW2bNmS4fiSJUsyVEvln+dffp58M7EDv/13JMn7phJUr7LF+S/CWpO8b6rF67upnS3aFCyQl1kj3+fits/4/YexfD7kXfLlcbJo07BWBbbO6cGl7eM4symcBeM+pGTRQubztas+n+E6yfumUsQ1/wPj7tm2Ecn7pvJZzzey6ZsQsU0/74ni4y4dafRibXwrlWfzxg0PbTsibAi+lcozf+4ci+NfRkzn/fdaUcvfh9q1qj308yuWL+Xt5q9Rw68y9esEED5iWLbdh+R8mjNqHMOH6bt3785HH33Er7/+yp07d4iMjOTEiRPs27ePuXPnGh2e/E358uTi0PHzRH63k0UT2j+wzbofD9NhyDzz+1splk/kmjXqfZ51c+HVTlNxdLAnIqw10wa9ywf9ZwPwXDFXlvy7PZPnbeKDAXNwcc7N2J5vsHD8RwS8O8aiL+9mw7h+I9n8/tLlxAzxVK1YkpA3/sXB4+ce97ZF5P8lJydTzqs8zZq/QY9PQh/abtOG9Rw6eAD3woUznEtNTaHRy42p7OPD8qXfPvDzc+fMYu6cWXTv0YtK3lVITk7mwoXz2XYfImI9hiej/v7+fPfdd3zxxReUK1eOH3/8kYoVK7Jw4UK8vLyMDk/+pv/+eIT//njkkW1SUm5zMf76A895lS7Cy/96gX+9N5a9R+7OIf50zBKWT+lEv38v4/fYa/hVLIG9nR1Dp60iPT0dgImRG1ny7/Y4ONhx+3aaub/Yy9e5lpj8wGsB5MvjxKxRH9B5+AL6ftg4q7crIn8SWLsOgbXrPLLNpYsXGRM+gv9EfElo5w4Zznfq2g24W/l8kIRr1/jPlElMnPo5NWrWMh8vp39DJAtUwTSO4ckoQMmSJRkxYoTRYYhBavs/z+mN4VxNSGJL1HHCpq3i8rUbANSoXJorCUnmRBRg0+5jpKWlU63Sc6zYfJC9R86Slp5GcLOazF2xC+e8uXi3aXU27T5mkYgC7F7UFydHB46c+J2R09ew88BvFucn9mvJ99ui2bz7mJJRkScgLS2Ngf168/4HIXiWff6x+ti1cwdpaWlcuniRFkFNuJF0gyo+vnzasw/PFi2azRFLjqVc1DCGzxkV27Z+RwwfDppLkw5TGDjpO2pXLct3UzthZ3f3b4UirgWIvWxZNb1zJ43LCUkUcSsAwOkL8bzaeRphXYO4tnsiF7eNo3iRZ2jde6b5M3/EXaPriAW80/NL3u31Jef+uMK6GR/jU97D3Oatl6viU74Eg6aseAJ3LiIAs76agb29Pe+0bvPYfZw7d5a0tHRmfhlBz779+GzCJK5du0an9u1ITU3JxmhFxBoMq4yWL1/+L0viJpOJI0cePcQr/2xL1v1s/vPhXy9w6JfzxKwKo47/82z56Xim+ijimp//DHqX+St3s/j7n3HOl4vBnV7l63EhNO04FYBfTl/il9OXzJ/ZdeAkZUq4EfpefUIGReJR5Bk+6/UGr3aammHOqohYx5HD0SyYN5evl3z7t4ZI09PSuH07ld59B1DrX4EAhI8dT6N6gUT9tJuAf9XOrpAlB9MwvXEMS0anTp360HP79+9n7ty5pKWlPbSN5EynzscTe+U6niXc2fLTcS7GJ+BeyHLFu729HYUK5OViXAIAHVrWISExmQGTvjO3aTdgDr+uG0F171L8dOjUA6+1J/o0Ab6eAPhWKEkR1wLs/LqP+byDgz2Bfp50bFkHlxqfkJaWns13K2Lb9u39mcuX42nSqL752J07d5jw2Rjmz53Dmv9uylQ/bu7uAJTxLGs+VqhQIZ55piB//P9T/UTk6WVYMtqwYcMMx3777TfGjx/P5s2bCQoKolu3bgZEJkYqXvgZXF3y8cf/J5q7D56kYIG8+FYowb6YswDUq1YOOzsTUdGnAcib2ylDonjn/3+RuTfc/yCVvTz4I/YaAJt/OkbVN0danP8irDXHTl5k/Oz1SkRFrKBp0GsWC44AOnf4kKZBzWj2evNM9+Pj6wfAqVMnKfLsswBcu3aVq1evULRosewLWHI0VUaN81QsYLp48SJTpkxh+fLlBAYGsnz5csqVK2d0WJIN8uVxwrOEu/l9qeKuVC5XnCsJSVy+doMBHZqwfON+/ohLoEwJN0Z+/DonzsaxfkcMAMdOXmTdj4eZNuhduo1ciKODPf/u+zZL1u3l9/9PJNduO0zoey/Sr31jFn//M/nz5iKs62ucvhDP/qN3t2fq+m49Tl2I58iJ38nt5Ejb5gHUq1aOVzvfrdAnJt3iyAnLCsqN5BQuX7uR4biIZF5S0g3O3vc0vfPnz3HsaAwFXFwoWrQYzzxT0KK9g4MDbm5ulCpdxnzs998vkHDtGr///jtpd+5w7Ojdvx9KlCxJ3rz5eK5UaerVb8Bno0cxcEgYzs7OTJk4gVKly+BfvcaTuVEReWyGJqPXr19n+vTpzJs3jwoVKjB79mz8/f2NDEmymV/F5/jvlx+b34/9/03k567YRbdRi6j0fHHeC6rBM/nz8HvsNTbsPMqw/6wiJfV/8zbb9p/Dv/u+zZqIUNLS0lm+cT89xi4xn98adZwP+s+h+/sN+fT9RiTdTGH3wZO81uU/3LyVCoCTowOju7egWGEXkm6mEv3LeZp0nMIPe355Qt+EiG06Eh3NR+3eN78fP3Y0AEHNXmfYyNGZ6uPzqZNZ+d1y8/tWb96tms6YOcecbA4fNYZxY8Lp1qUjdiYTVf2rM236DBwdHbPpTiSnU2HUOKb0exszPmEzZszgyy+/xM3Nje7duz9w2P5x5fHtmm19icjTJf6nKUaHICJWktfRuIywbM+1Vuv713GvWK3vnMCwyuj48ePJnTs3JUuWZPny5SxfvvyB7R610ElEREQkO2jOqHEMS0Zff/11/YcXERGRp4JSEuMYloyOHp25uUIiIiIiknM9FavpRURERIyk0Vrj6HGgIiIiImIYVUZFRETE5qkwahxVRkVERETEMKqMioiIiM171OOjxboMSUY3btyY6bYNGjSwYiQiIiIiYiRDktEuXbpkqp3JZCImJsbK0YiIiIit05xR4xiSjB49etSIy4qIiIg8kLZ2Mo4WMImIiIiIYZ6KBUxJSUlERUVx4cIFUlNTLc4FBwcbFJWIiIjYChVGjWN4MnrkyBHat29PcnIyycnJuLi4cOXKFfLkyUOhQoWUjIqIiIjkYIYP04eHh/Piiy8SFRVFrly5WLx4MZs3b+aFF16gT58+RocnIiIiNsBkMlntJY9meDIaExND27ZtsbOzw97enpSUFIoWLUqvXr2YMGGC0eGJiIiIiBUZnow6ODhgZ3c3DFdXVy5cuACAs7Mzf/zxh5GhiYiIiI1QZdQ4hs8ZrVixIocOHaJUqVJUq1aNyZMnc+XKFb777juef/55o8MTERERESsyvDLavXt33N3dzX8uUKAAQ4cO5cqVKwwfPtzg6ERERMQWmEzWe8mjGV4Z9fb2Nv/Z1dWVr776ysBoRERExBZpON04hldGRURERMR2GV4ZrV+//iN/G9m4ceMTjEZERERskQqjxjE8GX3//fct3t++fZsjR46wfft2QkJCDIpKRERERJ6Epy4ZvWf+/PlER0c/4WhERETEFmnOqHGe2jmjderUYd26dUaHISIiIiJWZHhl9GG+//57nnnmGaPDEBERERugwqhxDE9GX3/9dYvSeHp6OnFxcVy+fJkhQ4YYGJmIiIiIWJvhyWiDBg0sklGTyUShQoWoXr06np6eBkYmIiIitkJzRo1jeDIaGhpqdAgiIiIiYhDDFzBVqFCB+Pj4DMevXLlChQoVDIhIREREbI0eB2ocwyuj6enpDzyekpKCo6PjE45GREREbJGG6Y1jWDIaGRkJ3P2Pv2TJEvLmzWs+l5aWRlRUFGXKlDEqPBERERFDREVF8dVXXxEdHU1sbCzTpk2jYcOG5vPp6elMnjyZJUuWkJCQgJ+fH0OHDqVUqVLmNlevXmX48OFs3rwZOzs7XnrpJQYMGEC+fPnMbY4ePcqwYcM4dOgQhQoVonXr1nz00UcWsaxdu5ZJkyZx/vx5SpUqRc+ePalbt26WYvkrhg3Tz549m9mzZ5Oens7ChQvN72fPns2CBQu4efMmYWFhRoUnIiIiNuRpGqZPSkrCy8vrobsKzZgxg7lz5zJ06FAWL15Mnjx5CAkJ4datW+Y2PXv25Ndff2XWrFlMnz6dPXv2MHjwYPP5xMREQkJCKFasGEuXLqV3795MnTqVRYsWmdvs3buXHj168Oabb7J8+XIaNGhAly5dOH78eJZi+Sum9IeNkz8hbdq0YerUqbi4uGRbn3l8u2ZbXyLydIn/aYrRIYiIleR1NG6ovOborVbr+4dPa5GSkmJxzMnJCScnp7/8rJeXl0VlND09ndq1a9O2bVvzY9OvX79OQEAAo0ePpmnTppw4cYImTZrwzTff4O3tfTeGH36gffv2bN26lSJFivD1118zceJEtm/fbo5j3LhxbNiwge+//x6ATz75hOTkZCIiIszxvP3225QvX55hw4ZlKpbMMHwB09y5c7M1ERURERHJKpPJZLVXREQEVatWtXjdn+Blxblz54iNjSUgIMB8LH/+/FSpUoV9+/YBsG/fPgoUKGBORAECAgKws7Pj4MGDAOzfvx9/f3+LhDgwMJCTJ09y7do1c5tatWpZXD8wMJD9+/dnOpbMMHwBU2hoKN7e3rRv397i+IwZMzh06BCTJ082KDIRERGRv69Dhw60bdvW4lhmqqIPEhsbC4Crq6vFcVdXV+Li4gCIi4ujUKFCFucdHBxwcXExfz4uLg4PDw+LNm5ubuZzLi4uxMXFmY896DqZiSUzDK+MRkVFWUyEvadOnTrs2bPHgIhERETE1lhzzqiTkxPOzs4Wr8dNRnMiw5PRpKSkB27h5ODgQGJiogERiYiIiDyd3N3dATLs0R4fH2+uYrq5uXH58mWL87dv3+batWvmz7u5uWWoXt57f38/f25z/3UyE0tmGJ6MlitXjjVr1mQ4vmbNGsqWLWtARCIiImJrrDlnNDt5eHjg7u7Ozp07zccSExM5cOAAvr6+APj6+pKQkEB0dLS5za5du0hLS6Ny5coA+Pj4sGfPHlJTU81tduzYQenSpc1reXx8fNi1a5fF9Xfs2IGPj0+mY8kMw+eMdu7cmdDQUM6ePUvNmjUB2LlzJ6tXr2bSpEkGRyciIiK24Gna8/7GjRucOXPG/P7cuXPExMTg4uJCsWLFCA4O5vPPP+e5557Dw8ODSZMmUbhwYfOKe09PT2rXrs2gQYMICwsjNTWV4cOH07RpU4oUKQJAUFAQ06ZNY8CAAXz00Uf88ssvREZG0q9fP/N1g4ODadOmDTNnzqRu3bqsWbOG6Ohohg0bBtxN4P8qlswwfGsngC1btjB9+nSOHj1Krly58PLyomvXrlSvXv2x+tPWTiI5l7Z2Esm5jNzaKXDcNqv1vb1n7Sy13717N8HBwRmON2/enNGjR5s3ml+8eDEJCQlUrVqVIUOGULp0aXPbe5veb9q0ybzp/cCBAx+66X3BggVp3bp1hgXla9euZeLEieZN73v16vXATe8fFctfeSqS0Yc5fvw45cqVy/LnlIyK5FxKRkVyLiOT0drjt1ut7209Aq3Wd05g+DD9nyUmJrJ69WqWLFnC4cOHiYmJMTokEREREbGSpyYZjYqKYsmSJaxfv57ChQvTqFEji8dWiYiIiFhLdi80kswzNBmNjY1l2bJlfPPNNyQmJvLKK6+QkpLCtGnTtJJeRERExAYYlox27NiRqKgo6tWrR//+/alduzb29vYsXLjQqJBERETERqkwahzDktEffviBNm3a8M4771CqVCmjwhARERERAxm26f3XX3/NjRs3aNGiBW+99Rbz5s3L8LQAERERkSfhn7LpfU5kWDLq4+PDiBEj2L59Oy1btmT16tXUqVOHtLQ0fvzxRz0KVERERJ4Yaz6bXh7N8MeB5s2blzfffJMFCxawYsUK2rZty4wZMwgICKBjx45GhyciIiIiVmR4Mnq/MmXK0Lt3b7Zu3cqECROMDkdERERshIbpjfPU7DN6P3t7exo2bJil55qKiIiIyD/PU5mMioiIiDxJKmAa56kaphcRERER26LKqIiIiNg8O5VGDaPKqIiIiIgYRpVRERERsXkqjBpHyaiIiIjYPG3BZBwN04uIiIiIYVQZFREREZtnp8KoYVQZFRERERHDqDIqIiIiNk9zRo2jyqiIiIiIGEaVUREREbF5KowaR5VRERERETGMKqMiIiJi80yoNGoUJaMiIiJi87S1k3E0TC8iIiIihlFlVERERGyetnYyjiqjIiIiImIYVUZFRETE5qkwahxVRkVERETEMKqMioiIiM2zU2nUMKqMioiIiIhhVBkVERERm6fCqHGUjIqIiIjN09ZOxtEwvYiIiIgYRpVRERERsXkqjBpHlVERERERMYwqoyIiImLztLWTcVQZFRERERHDqDIqIiIiNk91UeOoMioiIiIihlFlVERERGye9hk1jpJRERERsXl2ykUNo2F6ERERETGMKqMiIiJi8zRMbxxVRkVERETEMKqMioiIiM1TYdQ4qoyKiIiIiGFUGRURERGbpzmjxslUMrpx48ZMd9igQYPHDkZEREREbEumktEuXbpkqjOTyURMTMzfCkhERETkSXta9hm9c+cOU6ZMYcWKFcTFxVG4cGGaN29O586dzdXb9PR0Jk+ezJIlS0hISMDPz4+hQ4dSqlQpcz9Xr15l+PDhbN68GTs7O1566SUGDBhAvnz5zG2OHj3KsGHDOHToEIUKFaJ169Z89NFHFvGsXbuWSZMmcf78eUqVKkXPnj2pW7dutt5zppLRo0ePZutFRURERJ4mT8sw/YwZM1iwYAFjxoyhbNmyREdH069fP/Lnz09wcLC5zdy5cxk9ejQeHh5MmjSJkJAQ1qxZQ65cuQDo2bMnsbGxzJo1i9TUVPr378/gwYMZP348AImJiYSEhFCrVi3CwsI4fvw4/fv3p0CBArRs2RKAvXv30qNHDz799FNefPFFVq5cSZcuXVi6dCnlypXLtnvWAiYRERGRp8S+ffto0KAB9erVw8PDg8aNGxMYGMjBgweBu1XRyMhIOnXqRMOGDSlfvjxjx47l0qVLbNiwAYATJ06wbds2RowYQZUqVfD392fgwIGsXr2aixcvArBixQpSU1MZNWoUzz//PE2bNqVNmzbMmjXLHEtkZCS1a9fmww8/xNPTk08++YSKFSsyb968bL3nx0pGk5KS2Lp1KwsWLCAyMtLiJSIiIvJPY7LiKyUlhcTERItXSkrKA+Pw9fVl165dnDx5Erg7Ov3zzz9Tp04dAM6dO0dsbCwBAQHmz+TPn58qVaqwb98+4G5CW6BAAby9vc1tAgICsLOzMye1+/fvx9/fHycnJ3ObwMBATp48ybVr18xtatWqZRFfYGAg+/fvz+zXmilZXk1/5MgR2rdvT3JyMsnJybi4uHDlyhXy5MlDoUKFzCVkEREREYGIiAimTp1qcaxr166EhoZmaNu+fXsSExN55ZVXsLe3586dO3Tv3p3XXnsNgNjYWABcXV0tPufq6kpcXBwAcXFxFCpUyOK8g4MDLi4u5s/HxcXh4eFh0cbNzc18zsXFhbi4OPOxB10nu2Q5GQ0PD+fFF18kLCyMqlWrsnjxYhwcHOjVq5cSUREREflHsrPinNEOHTrQtm1bi2P3VyTvt3btWlauXMn48eMpW7YsMTExhIeHmxcy5URZHqaPiYmhbdu22NnZYW9vT0pKCkWLFqVXr15MmDDBGjGKiIiI/GM5OTnh7Oxs8XpYMjp27Fjat29P06ZN8fLy4vXXX+f9998nIiICAHd3dwDi4+MtPhcfH2+uYrq5uXH58mWL87dv3+batWvmz7u5uWWocN57f38/f25z/3WyS5aTUQcHB+zs7n7M1dWVCxcuAODs7Mwff/yRrcGJiIiIPAkmk/VeWXHz5s0MK/vt7e1JT08HwMPDA3d3d3bu3Gk+n5iYyIEDB/D19QXuzjtNSEggOjra3GbXrl2kpaVRuXJlAHx8fNizZw+pqanmNjt27KB06dK4uLiY2+zatcsilh07duDj45O1m/oLWU5GK1asyKFDhwCoVq0akydPZsWKFebVWCIiIiLyeF588UWmT5/Oli1bOHfuHOvXr2fWrFk0bNgQuLsFVXBwMJ9//jkbN27k2LFj9O7dm8KFC5vbeHp6Urt2bQYNGsTBgwf5+eefGT58OE2bNqVIkSIABAUF4ejoyIABA/jll19Ys2YNkZGRFtMJgoOD2bZtGzNnzuTEiRNMmTKF6OhoWrduna33bEq/l2pn0qFDh7hx4wY1a9YkPj6e3r17s2/fPkqVKsWoUaMoX758tgb4OPL4djU6BBGxkvifphgdgohYSV5H4/b6bL/ksNX6/uKtFzLdNjExkUmTJrFhwwbi4+MpXLgwTZs2pUuXLuah/Xub3i9evJiEhASqVq3KkCFDKF26tLmfe5veb9q0ybzp/cCBAx+66X3BggVp3bo17du3t4hn7dq1TJw40bzpfa9evbJ90/ssJ6P/BEpGRXIuJaMiOZeSUduU5dX0IiIiIjnNU/IAJpuU5WS0fv36j3xk1saNG/9WQCIiIiJPmjW3dpJHy3Iy+v7771u8v337NkeOHGH79u2EhIRkW2AiIiIikvP97WT0nvnz51tsISAiIiLyT6HCqHEe69n0D1KnTh3WrVuXXd2JiIiIiA3ItgVM33//Pc8880x2dSciIiLyxDxqPYxYV5aT0ddff93iP1h6ejpxcXFcvnyZIUOGZGtwIiIiIpKzZTkZbdCggUUyajKZKFSoENWrV8fT0zNbg3tc8bu1D6FITqUVryJiDdk2b1GyLMvJaGhoqDXiEBEREREblOVfBCpUqEB8fHyG41euXKFChQrZEpSIiIjIk2Qymaz2kkfLcmX0YU8PTUlJwdHR8W8HJCIiIvKk2SlnNEymk9HIyEjg7m8OS5YsIW/evOZzaWlpREVFUaZMmeyPUERERERyrEwno7NnzwbuVkYXLlyInd3/RvgdHR3x8PAgLCws2wMUERERsTZVRo2T6WR006ZNALRp04apU6fi4uJitaBERERExDZkec7o3LlzrRGHiIiIiGG00Mg4WV5NHxoayhdffJHh+IwZM+jWrVu2BCUiIiIitiHLyWhUVBR169bNcLxOnTrs2bMnW4ISEREReZLsTNZ7yaNlORlNSkp64BZODg4OJCYmZktQIiIiImIbspyMlitXjjVr1mQ4vmbNGsqWLZstQYmIiIg8SSaT9V7yaFlewNS5c2dCQ0M5e/YsNWvWBGDnzp2sWrWKyZMnZ3uAIiIiItZmp6zRMFlORuvXr8+0adOYPn0669atI1euXJQvX545c+ZouycRERERyZIsJ6MA9erVo169egAkJiayatUqxowZw+HDh4mJicnO+ERERESsLsvzFiXbPFYyCndX1X/zzTf897//pXDhwjRq1IjBgwdnZ2wiIiIiksNlKRmNjY1l2bJlfPPNNyQmJvLKK6+QkpLCtGnTtHhJRERE/rE0ZdQ4mU5GO3bsSFRUFPXq1aN///7Url0be3t7Fi5caM34RERERCQHy3Qy+sMPP9CmTRveeecdSpUqZcWQRERERJ4sraY3Tqbn63799dfcuHGDFi1a8NZbbzFv3jwuX75szdhEREREJIfLdDLq4+PDiBEj2L59Oy1btmT16tXUqVOHtLQ0fvzxRz19SURERP6xtOm9cUzp6enpj/vh3377jW+++YYVK1aQkJBAQEAA06dPz874HktSymPfkog85ez0oGeRHCv3Y+/x8/cN/e8v1uv7peet1ndO8Le21SpTpgy9e/dm69atTJgwIbtiEhEREREbkS2/g9jb29OwYUMaNmyYHd2JiIiIPFFawGQcPXBARERERAxj4OwMERERkaeDCqPGUWVURERERAyjyqiIiIjYPG3UYRxVRkVERETEMKqMioiIiM0zodKoUZSMioiIiM3TML1xNEwvIiIiIoZRZVRERERsniqjxlFlVEREREQMo8qoiIiI2DyTdr03jCqjIiIiImIYVUZFRETE5mnOqHFUGRURERERw6gyKiIiIjZPU0aNo2RUREREbJ6dslHDaJheRERERAyjyqiIiIjYPC1gMo4qoyIiIiJiGCWjIiIiYvNMJuu9surixYv07NmTGjVqULlyZYKCgjh06JD5fHp6OpMmTSIwMJDKlSvzwQcfcOrUKYs+rl69So8ePfDz88Pf35/+/ftz48YNizZHjx7l3Xffxdvbm7p16zJjxowMsaxdu5bGjRvj7e1NUFAQW7duzfoN/QUloyIiIiJPiWvXrvHOO+/g6OjIjBkzWL16NX369MHFxcXcZsaMGcydO5ehQ4eyePFi8uTJQ0hICLdu3TK36dmzJ7/++iuzZs1i+vTp7Nmzh8GDB5vPJyYmEhISQrFixVi6dCm9e/dm6tSpLFq0yNxm79699OjRgzfffJPly5fToEEDunTpwvHjx7P1nk3p6enp2drjUyApJcfdkoj8PztN7BLJsXIbuJJl2o+nrNZ3l3+VynTbcePGsXfvXr7++usHnk9PT6d27dq0bduWkJAQAK5fv05AQACjR4+madOmnDhxgiZNmvDNN9/g7e0NwA8//ED79u3ZunUrRYoU4euvv2bixIls374dJycn87U3bNjA999/D8Ann3xCcnIyERER5uu//fbblC9fnmHDhj3OV/FAqoyKiIiIWFFKSgqJiYkWr5SUlAe23bRpE5UqVaJbt27UqlWL119/ncWLF5vPnzt3jtjYWAICAszH8ufPT5UqVdi3bx8A+/bto0CBAuZEFCAgIAA7OzsOHjwIwP79+/H39zcnogCBgYGcPHmSa9eumdvUqlXLIr7AwED279//976QP1EyKiIiIjbPmnNGIyIiqFq1qsXr/mrj/c6ePcuCBQsoVaoUX331Fe+88w4jRoxg2bJlAMTGxgLg6upq8TlXV1fi4uIAiIuLo1ChQhbnHRwccHFxMX8+Li4ONzc3izb33t/fz5/b3H+d7KKtnURERMTmWXMG0IcdOtC2bVuLY/dXJO+Xnp5OpUqV+PTTTwGoWLEiv/zyCwsXLqR58+bWC9JAqoyKiIiIWJGTkxPOzs4Wr4clo+7u7nh6elocK1OmDBcuXDCfB4iPj7doEx8fb65iurm5cfnyZYvzt2/f5tq1a+bPu7m5Zahw3nt/fz9/bnP/dbKLklERERGxeXYmk9VeWeHn58fJkyctjp06dYrixYsD4OHhgbu7Ozt37jSfT0xM5MCBA/j6+gLg6+tLQkIC0dHR5ja7du0iLS2NypUrA+Dj48OePXtITU01t9mxYwelS5c2r9z38fFh165dFrHs2LEDHx+fLN3TX1EyKiIiIvKUeP/99zlw4ADTp0/n9OnTrFy5ksWLF/Puu+8CYDKZCA4O5vPPP2fjxo0cO3aM3r17U7hwYRo2bAiAp6cntWvXZtCgQRw8eJCff/6Z4cOH07RpU4oUKQJAUFAQjo6ODBgwgF9++YU1a9YQGRlpMZ0gODiYbdu2MXPmTE6cOMGUKVOIjo6mdevW2XrP2tpJRP5RtLWTSM5l5NZOM3aftlrfH9V4LkvtN2/ezIQJEzh16hQeHh60bduWt99+23w+PT2dyZMns3jxYhISEqhatSpDhgyhdOnS5jZXr15l+PDhbNq0CTs7O1566SUGDhxIvnz5zG2OHj3KsGHDOHToEAULFqR169a0b9/eIpa1a9cyceJEzp8/T6lSpejVqxd169Z9zG/iwZSMisg/ipJRkZxLyaht0mp6ERERsXlZndsp2UdzRkVERETEMKqMioiIiM1TYdQ4SkZFRETE5mmo2Dj67kVERETEMKqMioiIiM0zaZzeMKqMioiIiIhhVBkVERERm6e6qHFUGRURERERw6gyKiIiIjZPm94bR5VRERERETGMKqMiIiJi81QXNY6SUREREbF5GqU3zlMxTL9nzx569uxJy5YtuXjxIgDLly9nz549BkcmIiIiItZkeDK6bt06QkJCyJ07N0eOHCElJQWAxMREIiIiDI5OREREbIHJZLLaSx7N8GT0888/JywsjBEjRuDg8L9ZA35+fhw5csTAyERERETE2gyfM3ry5En8/f0zHM+fPz8JCQkGRCQiIiK2xvDqnA0z/Lt3c3PjzJkzGY7//PPPlChRwoCIRERERORJMTwZffvttxk5ciQHDhzAZDJx8eJFVqxYwZgxY3jnnXeMDk9ERERsgOaMGsfwYfr27duTlpbGBx98QHJyMq1bt8bJyYl27drRpk0bo8MTERERESsypaenpxsdBEBKSgpnzpwhKSkJT09P8uXL99h9JaU8FbckIlZgZ6cqg0hOldvAEtmS/Res1vdbPsWs1ndOYPgw/XfffUdycjJOTk6ULVuWypUr/61EVERERET+OQxPRsPDwwkICKBHjx5s3bqVO3fuGB2SiIiI2BjNGTWO4XNGt2/fzrZt21i1ahWffPIJuXPnpnHjxgQFBeHn52d0eCIiImIDDK/O2bCnZs4oQHJyMuvXr2fVqlXs2LGDZ599lg0bNmS5H80ZFcm5NGdUJOcycs7o0gO/W63vFlWKWq3vnMDwyuj98uTJQ2BgIAkJCVy4cIETJ04YHZKIiIjYAA2nG+epSEbvVURXrlzJzp07KVq0KE2bNmXSpElGhyYiIiIiVmR4Mtq9e3e2bNlC7ty5eeWVV+jcuTO+vr5GhyUiIiI2RHVR4xiejNrZ2TFx4kQCAwOxt7c3OhwREREReYKeqgVM2UULmERyLi1gEsm5jFzA9N2hP6zWdzPvZ63Wd05gyH/2yMhIWrZsSa5cuYiMjHxk2+Dg4CcUlYiIiIg8aYYko7NnzyYoKIhcuXIxe/bsh7YzmUxKRkVERMTq7DRr1DAapheRfxQN04vkXEYO06+Kvmi1vl+tVMRqfecEhj9wYOrUqSQnJ2c4fvPmTaZOnWpARCIiIiLypBiejE6bNo2kpKQMx5OTk5k2bZoBEYmIiIitMVnxf/Johiej6enpD3zqwdGjR3FxcTEgIhERERF5UgybnVGtWjVMJhMmk4mXX37ZIiG9c+cOSUlJtGrVyqjwRERExIboaaDGMSwZ7d+/P+np6fTv35/Q0FDy589vPufo6Ejx4sX1JCYRERGRHM6wZLR58+YAeHh44Ovri6Ojo1GhiIiIiI3T1k7GMSQZTUxMxNnZGYCKFSty69Ytbt269cC299qJiIiISM5jSDJarVo1tm/fjqurK/7+/g9cwHRvYVNMTIwBEYqIiIgt0ZxR4xiSjM6ZM8e8Uv6vHgcqIiIiYm1KRo2jJzCJyD+KnsAkknMZ+QSm/8bEWq3vlyq4W63vnMDwfUZ/+OEH9uzZY34/f/58mjVrRo8ePbh27ZqBkYmIiIit0Kb3xjE8Gf3ss8+4ceMGAMeOHSM8PJy6dety7tw5Ro8ebXB0IiIiImJNBhbE7zp37hyenp4A/Pe//6V+/fp8+umnHD58mPbt2xscnYiIiNgCzQAyjuGVUUdHR27evAnAjh07+Ne//gWAi4sLiYmJRoYmIiIiIlZmeGXUz8+P8PBw/Pz8OHToEBMnTgTg1KlTPPvss8YGJyIiIjZBczuNY3hldPDgwTg4OLBu3TqGDBlCkSJFgLsLm2rXrm1wdCIiIiJiTdraSUT+UbS1k0jOZeTWTpuPxVut7xe9XK3Wd05geGUU4M6dO6xbt47//Oc//Oc//2H9+vXcuXPH6LBERETERjytWzt98cUXeHl5MXLkSPOxW7duERYWRo0aNfD19SU0NJS4uDiLz124cIH27dtTpUoVatWqxZgxY7h9+7ZFm927d9O8eXMqVapEo0aNWLp0aYbrz58/n/r16+Pt7c1bb73FwYMH/9b9PIjhyejp06dp0qQJffr0Yf369axfv55evXrRtGlTzpw5Y3R4IiIiIoY4ePAgCxcuxMvLy+L4qFGj2Lx5MxMnTmTu3LlcunSJrl27ms/fuXOHDh06kJqaysKFCxk9ejTLli1j8uTJ5jZnz56lQ4cO1KhRg++++47333+fgQMHsm3bNnObNWvWEB4eTpcuXVi2bBnly5cnJCSE+PjsrSIbnoyOGDGCEiVKsGXLFpYtW8ayZcvYvHkzHh4ejBgxwujwRERExAbYmaz3ehw3btygV69ejBgxwvwIdYDr16/z7bff0rdvX2rVqkWlSpUYNWoU+/btY//+/QBs376dX3/9lc8++4wKFSpQt25dPv74Y+bPn09KSgoACxcuxMPDg759++Lp6Unr1q15+eWXmT17tvlas2bN4u233+aNN96gbNmyhIWFkTt3br799tvH/ZofyPBkNCoqil69evHMM8+YjxUsWJCePXsSFRVlXGAiIiIi2SAlJYXExESL172k8GGGDRtG3bp1CQgIsDgeHR1NamqqxXFPT0+KFStmTkb3799PuXLlcHNzM7cJDAwkMTGRX3/91dymVq1aFn0HBgaa+0hJSeHw4cMW17GzsyMgIIB9+/Zl+Tt4FMO3dnJycjI/gel+N27cwNHR0YCIRERExNZYc2uniIgIpk6danGsa9euhIaGPrD96tWrOXLkCN98802Gc3FxcTg6OlKgQAGL466ursTGxprb3J+IAub3f9UmMTGRmzdvcu3aNe7cuYOrq+XiK1dXV3777be/uuUsMTwZrVevHoMHD2bkyJFUrlwZgAMHDjB06FDq169vcHQiIiIif0+HDh1o27atxTEnJ6cHtv39998ZOXIkM2fOJFeuXE8iPMMZPkw/cOBASpQoQcuWLfH29sbb25t33nmHkiVLMmDAAKPDkyfg5z1RfNy1I43q18bXuzybN26wOJ+UdIPRI4fxcoO61PSvQotmTVmyeKFFm1u3bhE+Yhj1AmsQUN2PHt1Dif/TysJ7rl69wssN6uLrXZ7rCQlWuy8RyZyf90QR2rkjDesFUuUFLzb96e+A+Lg4BvXvS8N6gdSoWoVO7UM4ffqUMcFKjmUyWe/l5OSEs7Ozxethyejhw4eJj4+nRYsWVKxYkYoVK/LTTz8xd+5cKlasiJubG6mpqST86d+v+Ph43N3dgbsVzj+vrr/3/q/aODs7kzt3bgoWLIi9vX2GxUrx8fEZKqp/l+GV0QIFCvD5559z+vRpTpw4Adyd+/Dcc88ZHJk8KcnJyZQrV55mzd+gxycZhyzGjx1N1E+7GTl6LMWKFWfnjh8JHzkMd/fC1HvxbvV83Nhwtv+wlbHjJ+Hs7MzoUcPp0T2U2XMXZOgvbPBAni/nxaVLF61+byLy15KTk/Dy8uL1Fm/w6cddLc6lp6fzSbcuODg4MHHKf3B2diZyzmw6hLRl6YrV5M2b16CoRayjZs2arFy50uJYv379KFOmDB999BFFixbF0dGRnTt38vLLLwPw22+/ceHCBXx8fADw8fFh+vTpxMfHm4fZd+zYgbOzM2XLljW3+eGHHyyus2PHDnMfTk5OvPDCC+zcuZOGDRsCkJaWxs6dO2ndunW23rNhyWhaWhpffvklmzZtIjU1lVq1atG1a1dy585tVEhikMDadQisXeeh5w8c2M+rr72Of7UaALzxVku+XbKIw4cOUu/F+ly/fp3lS79l1JjPqF6jJgBhw8Np0awJBw/sp3IVH3Nfixct4Pr1BNp37MKP23940OVE5AkLrF2XwNp1H3ju9OlTHDywn2+/W0XZss8DMHDwUOrX/Rffr1lNizffepKhSg72tDxOw9nZmXLlylkcy5s3L88884z5+BtvvMHo0aNxcXHB2dmZESNG4Ovra04kAwMDKVu2LL1796ZXr17ExsYyceJE3nvvPXNFtlWrVsyfP5+xY8fyxhtvsGvXLtauXUtERIT5um3btqVPnz5UqlSJypUrM2fOHJKTk2nRokW23rNhw/Sff/45//73v8mXLx9FihQhMjKSsLAwo8KRp1iVKj5s3bKJSxcvkp6eTtRPuzh9+hQ1A/4FQMyRw9y+nUrNmv9b8Ve6TBmeLVqMgwf2m4+dOPErM6b/h+GjxugpPiL/EKn/v+I4l9P/5s7Z2dnh5OTEvr0/GxWW5EB2JpPVXtmtf//+1KtXj27dutG6dWvc3NyYMmWK+by9vT3Tp0/Hzs6Oli1b0qtXL15//XW6detmblOiRAkiIiLYsWMHzZo1Y9asWYwYMcLiUez39oGfPHkyzZo1IyYmhi+//DLnDNN/9913DBkyhFatWgF3S8Pt27dn5MiR2NkZPpVVniJ9+g9ieNggXm5YFwcHB0wmE4OGDqeqfzUA4uNicXR0JP8DVhbemzeakpJCv949+OTTXhQtWozz584+8fsQkawrVboMRYsWY/LE8QwaMow8efIwN3I2F//4w7wqWCSnmzt3rsX7XLlyMWTIEIYMGfLQzxQvXpwZM2Y8st8aNWqwfPnyR7Zp3bp1tg/L/5lhyeiFCxeoW/d/wzIBAQGYTCYuXbrEs88+a1RY8hRa+PVcDh08wMQp/6Fo0eLs/TmK0f8/Z7RmrYC/7gCYPHE8pct40jToNStHKyLZydHRkQmTpjB00ABqB1TH3t6eGjVrEVi7Dunp6UaHJzmIxsuMY1gyeufOnQxbFjg4OJCammpQRPI0unnzJlMmTWTCpCnUrlMPgHJeXhw7dpS5c2ZSs1YArm7upKamcj0hwaI6Gh8fj+v/DyVE/bSbX385jv/6dQDmf8RerFOLkI860KlLN0Tk6VTxhUosXvod169fJzU1lUKFCvFeq7d44YVKRocmItnAsGQ0PT2dvn37WmxtkJKSwtChQ8mTJ4/52J83iRXbcvv2bW7fTsVkspy6YW9nR1paGgAVKr6Ag4Mju3fvpGGjuysLT538jT9+v2BevDTu35O5dfOm+fOHow8xdPAAvpo9jxIlSj6ZmxGRvyV//vzA3UVNRw5H0yX0Y4MjkhxFpVHDGJaMNm/ePMOx117TEKotSkq6wdkzZ8zvz58/x7GjMRRwcaFo0WJU9a/GxAmfkTt3LooWLc7Pe35i1crv+LRXX+DuP1Cvt3iD8Z+NwcXFhXz5nBkTPoLKVXzMyeifE86rV68AUKaMZ4a5piLyZCXduMGZ+/8OOHeOozExuLi4ULRYMf67bi0FCxaiaNFi/PLLMcaGj+LF+g0J+FeggVGLSHYxpefASTdJKTnulnK0PVG7+ajd+xmOB732OsNGjiYuLpYpEyewc+ePJFy7RtGixWjx5tu0Dv4A0/+vUrx16xYTPhvD92tXk5KaQkBAIP0GDsbNzf2R1/zhx5+UjP7DaCeEnCfqp9182DY4w/HXmjVn+KjRzJ8XyZxZXxEfd3dT71dfa0aHjp1xfMim4fLPldvA3c93n7hmtb5reLpYre+cQMmoiPyjKBkVybmUjNomw5/AJCIiImI0K2wHKpmkZFRERERsnnJR42h3eRERERExjCqjIiIiIiqNGsaQZHTjxo2ZbtugQQMrRiIiIiIiRjIkGe3SpUum2plMJmJiYqwcjYiIiNg6k0qjhjEkGT169KgRlxURERGRp4zmjIqIiIjN09ZOxnkqktGkpCSioqK4cOECqampFueCgzM+lUNEREREcgbDk9EjR47Qvn17kpOTSU5OxsXFhStXrpAnTx4KFSqkZFRERESsToVR4xi+z2h4eDgvvvgiUVFR5MqVi8WLF7N582ZeeOEF+vTpY3R4IiIiYgtMVnzJIxmejMbExNC2bVvs7Oywt7cnJSWFokWL0qtXLyZMmGB0eCIiIiJiRYYnow4ODtjZ3Q3D1dWVCxcuAODs7Mwff/xhZGgiIiJiI0xW/J88muFzRitWrMihQ4coVaoU1apVY/LkyVy5coXvvvuO559/3ujwRERERMSKDK+Mdu/eHXd3d/OfCxQowNChQ7ly5QrDhw83ODoRERGxBSaT9V7yaKb09PR0o4PIbkkpOe6WROT/2dnpb3aRnCq3geO1+89ct1rfPiXzW63vnMDwYXoRERERo+nXXOMYnozWr18f0yNq2Bs3bnyC0YiIiIjIk2R4Mvr+++9bvL99+zZHjhxh+/bthISEGBSViIiI2BSVRg3z1CWj98yfP5/o6OgnHI2IiIjYIm3BZBzDV9M/TJ06dVi3bp3RYYiIiIiIFRleGX2Y77//nmeeecboMERERMQGaAsm4xiejL7++usWC5jS09OJi4vj8uXLDBkyxMDIRERERMTaDE9GGzRoYJGMmkwmChUqRPXq1fH09DQwMhEREbEVKowaR5vei8g/ija9F8m5jNz0PvpcotX6ruThbLW+cwLDFzBVqFCB+Pj4DMevXLlChQoVDIhIREREbI7Jii95JMOT0YcVZlNSUnB0dHzC0YiIiIjIk2RYQTwyMhK4O0d0yZIl5M2b13wuLS2NqKgoypQpY1R4IiIiYkO0z6hxDEtGZ8+eDdytjC5cuBA7u/8VaR0dHfHw8CAsLMyg6ERERETkSTB8AVObNm2YOnUqLi4u2danFjCJ5FxawCSScxm5gOnIhRtW67tisXxW6zsnMDwZtQYloyI5l5JRkZzLyGQ0xorJaAUlo49k+AKm0NBQvvjiiwzHZ8yYQbdu3QyISERERESeFMOT0aioKOrWrZvheJ06ddizZ48BEYmIiIjN0dZOhjE8GU1KSnrgFk4ODg4kJlpvA1oRERERMZ7hyWi5cuVYs2ZNhuNr1qyhbNmyBkQkIiIitsZkxf/Joxn+bPrOnTsTGhrK2bNnqVmzJgA7d+5k9erVTJo0yeDoRERERMSanorV9Fu2bGH69OkcPXqUXLly4eXlRdeuXalevfpj9afV9CI5l1bTi+RcRq6mP/ZHktX69no27183smFPRTL6MMePH6dcuXJZ/pySUZGcS8moSM6lZNQ2GT5M/2eJiYmsXr2aJUuWcPjwYWJiYowOSURERHI4/ZprnKcmGY2KimLJkiWsX7+ewoUL06hRIwYPHmx0WCIiImILlI0axtBkNDY2lmXLlvHNN9+QmJjIK6+8QkpKCtOmTdNKehEREREbYFgy2rFjR6KioqhXrx79+/endu3a2Nvbs3DhQqNCEhERERulLZiMY1gy+sMPP9CmTRveeecdSpUqZVQYIiIiImIgwza9//rrr7lx4wYtWrTgrbfeYt68eVy+fNmocERERMSGmUzWe8mjGb61U1JSEmvWrOHbb7/l0KFD3Llzh759+/LGG2/g7Oz8eH1qayeRHEtbO4nkXEZu7fTrpWSr9V22cB6r9Z0TGP440Lx58/Lmm2+yYMECVqxYQdu2bZkxYwYBAQF07NjR6PBERETEBpis+MqKiIgI3njjDXx9falVqxadO3fmt99+s2hz69YtwsLCqFGjBr6+voSGhhIXF2fR5sKFC7Rv354qVapQq1YtxowZw+3bty3a7N69m+bNm1OpUiUaNWrE0qVLM8Qzf/586tevj7e3N2+99RYHDx7M4h39NcOT0fuVKVOG3r17s3XrViZMmGB0OCIiIiJP1E8//cR7773H4sWLmTVrFrdv3yYkJISkpP9tyj9q1Cg2b97MxIkTmTt3LpcuXaJr167m83fu3KFDhw6kpqaycOFCRo8ezbJly5g8ebK5zdmzZ+nQoQM1atTgu+++4/3332fgwIFs27bN3GbNmjWEh4fTpUsXli1bRvny5QkJCSE+Pj5b79nwYXpr0DC9SM6lYXqRnMvIYfoTsdYbpvd0f/xh+suXL1OrVi3mzZtHtWrVuH79OrVq1WLcuHE0btwYgBMnTtCkSRMWLVqEj48PW7dupWPHjmzbtg03NzcAFixYwLhx49i5cydOTk589tlnbN26lVWrVpmv1b17dxISEvjqq68AeOutt/D29jbv+56WlkbdunVp06YN7du3f+x7+rOnqjIqIiIiYgSTFf+XkpJCYmKixSslJSVTcV2/fh0AFxcXAKKjo0lNTSUgIMDcxtPTk2LFirF//34A9u/fT7ly5cyJKEBgYCCJiYn8+uuv5ja1atWyuFZgYKC5j5SUFA4fPmxxHTs7OwICAti3b1/Wvty/oGRURERExIoiIiKoWrWqxSsiIuIvP5eWlsaoUaPw8/OjXLlyAMTFxeHo6EiBAgUs2rq6uhIbG2tuc38iCpjf/1WbxMREbt68yZUrV7hz5w6urq4ZrvPn+al/11PzOFARERERo1hzC6YOHTrQtm1bi2NOTk5/+bmwsDB++eUXvv76a2uF9lRQMioiIiJiRU5OTplKPu83bNgwtmzZwrx583j22WfNx93c3EhNTSUhIcGiOhofH4+7u7u5zZ9Xvd+rZt7f5s8Vzri4OJydncmdOzd2dnbY29tnWKwUHx+foaL6d2mYXkRERGze07K1U3p6OsOGDWP9+vXMmTOHEiVKWJyvVKkSjo6O7Ny503zst99+48KFC/j4+ADg4+PD8ePHLRLJHTt24OzsTNmyZc1tdu3aZdH3jh07zH04OTnxwgsvWFwnLS2NnTt34uvrm8W7ejQloyIiIiJPibCwMFasWMH48ePJly8fsbGxxMbGcvPmTQDy58/PG2+8wejRo9m1axfR0dH0798fX19fcyIZGBhI2bJl6d27N0ePHmXbtm1MnDiR9957z1yhbdWqFWfPnmXs2LGcOHGC+fPns3btWj744ANzLG3btmXx4sUsW7aMEydOMHToUJKTk2nRokW23rO2dhKRfxRt7SSScxm5tdOp+JtW67uUa+5Mt/Xy8nrg8fDwcHMSeOvWLUaPHs3q1atJSUkhMDCQIUOGmIfgAc6fP8/QoUP56aefyJMnD82bN6dHjx44OPzvS969ezfh4eH8+uuvPPvss3Tu3DlDojlv3jy++uorYmNjqVChAgMHDqRKlSpZuf2/pGRURP5RlIyK5FxKRm2TFjCJiIiIzTNleXanZBcloyIiImLzrLm1kzyaFjCJiIiIiGFUGRURERGbp8KocVQZFRERERHDqDIqIiIiNk9zRo2jyqiIiIiIGEaVURERERHNGjWMKqMiIiIiYhhVRkVERMTmac6ocZSMioiIiM1TLmocDdOLiIiIiGFUGRURERGbp2F646gyKiIiIiKGUWVUREREbJ5Js0YNo8qoiIiIiBhGlVERERERFUYNo8qoiIiIiBhGlVERERGxeSqMGkfJqIiIiNg8be1kHA3Ti4iIiIhhVBkVERERm6etnYyjyqiIiIiIGEaVUREREREVRg2jyqiIiIiIGEaVUREREbF5KowaR5VRERERETGMKqMiIiJi87TPqHGUjIqIiIjN09ZOxtEwvYiIiIgYRpVRERERsXkapjeOKqMiIiIiYhgloyIiIiJiGCWjIiIiImIYzRkVERERm6c5o8ZRZVREREREDKPKqIiIiNg87TNqHCWjIiIiYvM0TG8cDdOLiIiIiGFUGRURERGbp8KocVQZFRERERHDqDIqIiIiotKoYVQZFRERERHDqDIqIiIiNk9bOxlHlVERERERMYwqoyIiImLztM+ocVQZFRERERHDqDIqIiIiNk+FUeMoGRURERFRNmoYDdOLiIiIiGFUGRURERGbp62djKPKqIiIiIgYRpVRERERsXna2sk4qoyKiIiIiGFM6enp6UYHISIiIiK2SZVRERERETGMklERERERMYySURERERExjJJRERERETGMklERERERMYySURERERExjJJRERERETGMklERERERMYySURERERExjJJRybS+ffvSuXNn8/s2bdowcuTIJx7H7t278fLyIiEh4YlfOzudO3cOLy8vYmJijA5F5IH0M3/XlClTaNas2SPb6OdZ5PEpGf2H69u3L15eXnh5eVGpUiUaNWrE1KlTuX37ttWvPWXKFD7++ONMtX3S/5jUr18fLy8v9u/fb3F85MiRtGnT5onEcL8//6MOULRoUbZv387zzz//xOORfy79zD/YvZ95Ly8vfHx8aN68OWvXrs2Wvtu1a8fs2bPN7/XzLJK9lIzmALVr12b79u2sW7eOtm3bMnXqVL766qsHtk1JScm26z7zzDM4OztnW3/ZLVeuXIwbN87oMB7K3t4ed3d3HBwcjA5F/mH0M/9g3bp1Y/v27Sxbtgxvb2+6d+/O3r17/3a/+fLlo2DBgo9so59nkcenZDQHcHJywt3dneLFi/Puu+8SEBDApk2bgP/9Bv/5558TGBhI48aNAfj999/5+OOP8ff3p3r16nTq1Ilz586Z+7xz5w7h4eH4+/tTo0YNxo4dS3p6usV1/zxkl5KSwmeffUbdunXNFZslS5Zw7tw5goODAahWrRpeXl707dsXgLS0NCIiIqhfvz6VK1fmtdde4/vvv7e4ztatW3n55ZepXLkybdq04fz585n6Xt5++23279/P1q1bH9luyZIlvPLKK3h7e9O4cWPmz59vcX7v3r00a9YMb29vWrRowYYNGyyG4+7cuUP//v3N9/Dyyy8zZ84c8+enTJnCsmXL2Lhxo7lys3v3bothvbS0NOrUqcPXX39tce0jR45Qvnx58z0nJCQwYMAAatasiZ+fH8HBwRw9ejRT34fkHPqZf7B8+fLh7u5O6dKlGTx4MLlz52bz5s0AHDt2jODgYCpXrkyNGjUYNGgQN27cMH929+7dvPnmm/j4+ODv70+rVq3M171/mF4/zyLZT7/C5UC5cuXi6tWr5vc7d+7E2dmZWbNmAZCamkpISAg+Pj7Mnz8fBwcH/vOf//Dhhx+yYsUKnJycmDlzJsuWLWPUqFF4enoyc+ZM1q9fT82aNR963d69e7N//34GDhxI+fLlOXfuHFeuXKFo0aJMmTKF0NBQvv/+e5ydncmdOzcAERERrFixgrCwMEqVKkVUVBS9evWiUKFCVK9end9//52uXbvy3nvv8fbbbxMdHc2YMWMy9T14eHjQqlUrxo8fT+3atbGzy/i714oVK5g0aRKDBw+mQoUKxMTEMGjQIPLmzUvz5s1JTEykU6dO1KlTh/Hjx3P+/HlGjRpl0UdaWhrPPvsskyZN4plnnmHfvn0MHjwYd3d3mjRpQrt27Thx4gSJiYmEh4cD4OLiwqVLl8x92NnZ0bRpU1atWsW7775rPr5y5Ur8/PwoXrw4AB9//DG5cuVixowZ5M+fn0WLFvH++++zbt06nnnmmUx9L5Lz6Gc+IwcHBxwcHEhNTSUpKYmQkBB8fX355ptviI+PZ+DAgQwfPpzRo0dz+/ZtunTpwltvvcWECRNITU3l4MGDmEymDP3q51kk+ykZzUHS09PZuXMn27dvp3Xr1ubjefPmZcSIETg5OQHw3XffkZaWxsiRI81/2YaHh1OtWjV++uknAgMDmTNnDu3bt+ell14CICwsjO3btz/02idPnmTt2rXMmjWLgIAAAEqUKGE+7+LiAoCrqysFChQA7lZVIiIimDVrFr6+vubP/PzzzyxatIjq1auzYMECSpYsaa6qlClThuPHjzNjxoxMfSedO3dm6dKlrFixgtdffz3D+SlTptC3b1/zfZYoUYJff/2VRYsW0bx5c1auXAnAiBEjyJUrF2XLluXSpUsMHDjQ3IejoyPdunUzvy9RogT79+/n+++/p0mTJuTLl4/cuXOTkpKCu7v7Q2N97bXXmDVrFhcuXKBYsWKkpaWxevVqOnXqBMCePXs4ePAgO3fuNP+37NOnDxs2bGDdunW0bNkyU9+J5Bz6mX+wlJQUZs2axfXr16lZsyarVq0iJSWFMWPGkDdvXgAGDx5Mx44d6dmzJw4ODly/fp0XX3yRkiVLAuDp6fnAvvXzLJL9lIzmAFu2bMHX15fU1FTS09N59dVXCQ0NNZ8vV66c+S87gKNHj3LmzBn8/Pws+rl16xZnzpzh+vXrxMbGUqVKFfM5BwcHKlWqlGHY7p6YmBjs7e2pVq1apuM+ffo0ycnJtGvXzuJ4amoqFSpUAODEiRNUrlzZ4ryPj0+mr1GoUCHatWvH5MmTadKkicW5pKQkzpw5w4ABAxg0aJD5+O3bt8mfPz9w9x9cLy8vcuXKZT7v7e2d4Trz58/n22+/5cKFC9y6dYvU1FTKly+f6TgBKlSogKenJ6tWraJ9+/b89NNPXL582TzMeuzYMZKSkqhRo4bF527evMmZM2eydC35Z9PP/IONGzeOSZMmcevWLfLmzUuPHj2oV68e4eHheHl5mRNRAD8/P9LS0jh58iTVqlWjRYsWhISE8K9//YtatWrxyiuvULhw4Uzf25/p51kk85SM5gA1atRg6NChODo6Urhw4QwT6PPkyWPxPikpiRdeeOGBi3sKFSr0WDHcG4LLiqSkJODusF2RIkUszt3/D+nf1bZtWxYsWJBh/ta96w8fPtziH2HggUP6D7N69WrGjBlDnz598PX1JV++fHz11VccOHAgy7EGBQWxcuVK2rdvz6pVqwgMDDQvnLhx4wbu7u7MnTs3w+fuJc9iG/Qz/2AhISG0aNGCvHnz4ubm9sBh9ocJDw+nTZs2bNu2jbVr1zJx4kRmzZqVpV9+/0w/zyKZo2Q0B8iTJw/PPfdcptu/8MILrF27FldX14eujHV3d+fAgQPmqsft27c5fPgwFStWfGD7cuXKkZaWRlRUlHnI7n6Ojo7A3UUS93h6euLk5MSFCxeoXr36A/v19PQ0L8y4J6tJXr58+ejcuTNTpkyhfv365uNubm4ULlyYs2fP8tprrz3ws6VLl2bFihWkpKSY/7E8dOiQRZu9e/fi6+vLe++9Zz7258qGo6MjaWlpfxnrq6++ysSJE4mOjmbdunWEhYWZz73wwgvExcVhb2+Ph4fHX9+45Fj6mX+wggULPvB78fT0ZNmyZSQlJZmro3v37sXOzo7SpUub21WsWJGKFSvSoUMHWrZsyapVqx6YjOrnWSR7aTW9DQoKCqJgwYJ06tSJPXv2cPbsWXbv3s2IESP4448/AAgODmbGjBls2LCBEydOEBYW9sj9Aj08PGjevDn9+/dnw4YN5j7XrFkDQPHixTGZTGzZsoXLly9z48YNnJ2dadeuHeHh4SxbtowzZ85w+PBh5s6dy7JlywBo1aoVp06dYsyYMfz222+sXLnSfC4r3n77bfLnz8+qVassjnfr1o0vvviCyMhITp48ybFjx/j222/NCz+CgoJIT09n0KBBnDhxgm3btjFz5kwAc9XlueeeIzo6mm3btnHy5EkmTpyYIWEtXrw4x44d47fffuPy5cukpqY+9Hv09fVlwIAB3LlzxyJ5DggIwMfHhy5durB9+3bOnTvH3r17+fe//53heiL3s8Wf+T/fv5OTE3379uX48ePs2rWL4cOH06xZM9zc3Dh79izjx49n3759nD9/nu3bt3Pq1CnKlCnzwP708yySvZSM2qA8efIwb948ihUrRteuXWnSpAkDBgzg1q1b5qpJu3bteO211+jTpw+tWrUiX758NGrU6JH9Dh06lJdffpmhQ4fyyiuvMGjQIJKTkwEoUqQIoaGhjB8/noCAAIYPHw7AJ598QufOnYmIiKBJkyZ8+OGHbNmyxVwpKFasGFOmTGHjxo00a9aMhQsX0r179yzfs6OjIx9//DG3bt2yOP7WW28xYsQIli5dSlBQEG3atGHZsmXm6zs7O/P5558TExNDs2bN+Pe//02XLl2A/w0rtmrVipdeeonu3bvz9ttvc/XqVYsVtHA3GS5dujRvvPEGtWrVeuTeh0FBQRw9epRGjRpZDIWaTCa++OILqlWrRr9+/WjcuDGffvop58+fx83NLcvfidgOW/yZ//P9f/XVV1y9epU333yTjz/+mFq1apnniufJk4fffvuN0NBQXn75ZQYPHsx7771Hq1atHtiffp5Fspcp/WGz00XkgVasWEH//v3Zs2fPY82bExERkf/RnFGRv7B8+XI8PDwoUqQIx44dY9y4cTRu3FiJqIiISDZQMiryF2JjY5k8eTKxsbG4u7vTuHHjvz1sKCIiIndpmF5EREREDKMFTCIiIiJiGCWjIiIiImIYJaMiIiIiYhgloyIiIiJiGCWjIiIiImIYJaMi8tTq27cvnTt3Nr9v06YNI0eOfOJx7N69Gy8vr0c+HlNERB6P9hkVkSzr27ev+Xnhjo6OFC1alGbNmtGxY0ccHKz318qUKVMy3f/u3bsJDg4mKiqKAgUKWC0mERH5e5SMishjqV27NuHh4aSkpLB161aGDRuGo6MjHTp0sGiXkpKCk5NTtlzzmWeeyZZ+RETk6aFkVEQei5OTE+7u7gC8++67bNiwgU2bNnHy5EkSEhLw9vZm/vz5ODk5sWnTJn7//XdGjx7Njz/+iJ2dHVWrVmXAgAF4eHgAcOfOHcaOHcu3336Lvb09b7zxBn9+JkebNm0oX748AwYMAO4mupMmTWLVqlXEx8dTtGhR2rdvT61atQgODgagWrVqADRv3pzRo0eTlpbGjBkzWLRoEXFxcZQqVYrOnTvTuHFj83W2bt3KqFGj+P3336lSpQrNmze3+vcpImKrlIyKSLbIlSsXV69eBWDnzp04Ozsza9YsAFJTUwkJCcHHx4f58+fj4ODAf/7zHz788ENWrFiBk5MTM2fOZNmyZYwaNQpPT09mzpzJ+vXrqVmz5kOv2bt3b/bv38/AgQMpX748586d48qVKxQtWpQpU6YQGhrK999/j7OzM7lz5wYgIiKCFStWEBYWRqlSpYiKiqJXr14UKlSI6tWr8/vvv9O1a1fee+893n77baKjoxkzZozVvz8REVulZFRE/pb09HR27tzJ9u3bad26NVeuXCFv3ryMGDHCPDz/3XffkZaWxsiRIzGZTACEh4dTrVo1fvrpJwIDA5kzZw7t27fnpZdeAiAsLIzt27c/9LonT55k7dq1zJo1i4CAAABKlChhPu/i4gKAq6urec5oSkoKERERzJo1C19fX/Nnfv75ZxYtWkT16tVZsGABJUuWpG/fvgCUKVOG48ePM2PGjOz82kRE5P8pGRWRx7JlyxZ8fX1JTU0lPT2dV199ldDQUIYNG0a5cuUs5okePXqUM2fO4OfnZ9HHrVu3OHPmDNevXyc2NpYqVaqYzzk4OFCpUqUMQ/X3xMTEYG9vbx6Gz4zTp0+TnJxMu3btLI6npqZSoUIFAE6cOEHlypUtzvv4+GT6GiIikjVKRkXksdSoUYOhQ4fi6OhI4cKFLVa558mTx6JtUlISL7zwAuPGjcvQT6FChR7r+veG3bMiKSkJuDtUX6RIEYtz2bXISkREskbJqIg8ljx58vDcc89lqu0LL7zA2rVrcXV1xdnZ+YFt3N3dOXDggLnSefv2bQ4fPkzFihUf2L5cuXKkpaURFRVlHqa/n6OjI3B3YdQ9np6eODk5ceHCBapXr/7Afj09Pdm0aZPFsQMHDvz1TYqIyGPRpvciYnVBQUEULFiQTp06sWfPHs6ePcvu3bsZMWIEf/zxBwDBwcHMmDGDDRs2cOLECcLCwh65ybyHhwfNmzenf//+bNiwwdznmjVrAChevDgmk4ktW7Zw+fJlbty4gbOzM+3atSM8PJxly5Zx5swZDh8+zNy5c837prZq1YpTp04xZswYfvvtN1auXGk+JyIi2U/JqIhYXZ48eZg3bx7FihWja9euNGnShAEDBnDr1i1zpbRdu3a89tpr9OnTh1atWpEvXz4aNWr0yH6HDh3Kyy+/zNChQ3nllVcYNGgQycnJABQpUoTQ0FDGjx9PQEAAw4cPB+CTTz6hc+fORERE0KRJEz788EO2bNli3mKqWLFiTJkyhY0bN9KsWTMWLlxI9+7drfjtiIjYNlP6w1YHiIiIiIhYmSqjIiIiImIYJaMiIiIiYhgloyIiIiJiGCWjIiIiImIYJaMiIiIiYhgloyIiIiJiGCWjIiIiImIYJaMiIiIiYhgloyIiIiJiGCWjIiIiImIYJaMiIiIiYpj/A2X4T19IpRtAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming you have multiple F1 scores from cross-validation or different model runs\n",
        "# For example, let's assume these are your F1 scores:\n",
        "f1_scores = [0.72, 0.74, 0.76, 0.75, 0.77]  # Replace with your actual F1 scores\n",
        "\n",
        "# Plot F1 Score\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(range(1, len(f1_scores) + 1), f1_scores, marker='o', color='blue', linestyle='-', label='F1 Score')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('F1 Score Across Different Runs')\n",
        "plt.xlabel('Run')\n",
        "plt.ylabel('F1 Score')\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "kuQtJouXyu_R",
        "outputId": "7bda895c-159f-4ef7-cd57-14ac3328e827"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "# Assuming these are your predicted probabilities and true labels\n",
        "# Replace with your actual values\n",
        "y_test_final = [0, 1, 1, 0, 1]  # Example true labels\n",
        "y_pred_proba_final = [0.1, 0.9, 0.8, 0.2, 0.7]  # Example predicted probabilities\n",
        "\n",
        "# Calculate the ROC curve\n",
        "fpr, tpr, _ = roc_curve(y_test_final, y_pred_proba_final)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot ROC Curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='blue', marker='o', linestyle='-', label=f'ROC curve (area = {roc_auc:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='red', linestyle='--')  # Diagonal line\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "yVgirkGUy4zS",
        "outputId": "3541fbb1-3ebb-4212-c39c-19a149808e2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(max_depth=5, random_state=42)\n",
        "rf.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "00QNVt3oodbi",
        "outputId": "5097e763-ce2a-45a9-cd6c-44179230054c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  return fit_method(estimator, *args, **kwargs)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check training accuracy\n",
        "train_accuracy = rf.score(X_train, y_train)\n",
        "print(\"Training Accuracy:\", train_accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THDZl2IsOa6m",
        "outputId": "2f3f25a9-3a70-454f-bbd2-529afe92eb0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y.value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJVsYwwZMv9y",
        "outputId": "760f416f-5cf9-4b0f-f25c-45518692905a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fraud\n",
            "0    507443\n",
            "1      6200\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example of using a custom threshold\n",
        "threshold = 0.7\n",
        "y_pred_custom = (y_pred >= threshold).astype(int)\n"
      ],
      "metadata": {
        "id": "R00PeGWdl87t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ensure that feature_importances and feature_names have the same length\n",
        "if len(feature_importances) == len(feature_names):\n",
        "    # Create a DataFrame for easy plotting\n",
        "    importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n",
        "\n",
        "    # Sort the DataFrame by importance\n",
        "    importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "    # Plot the feature importance\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    sns.barplot(x='Importance', y='Feature', data=importance_df)\n",
        "    plt.title('Feature Importance')\n",
        "    plt.xlabel('Importance')\n",
        "    plt.ylabel('Feature')\n",
        "    plt.show()\n",
        "else:\n",
        "    print(\"The number of features does not match the number of importance values.\")\n",
        "    print(f\"Number of features: {len(feature_names)}\")\n",
        "    print(f\"Number of importance values: {len(feature_importances)}\")\n"
      ],
      "metadata": {
        "id": "wr_oslYumAuD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ccd6e54f-99bf-4468-9dcf-220244382124"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The number of features does not match the number of importance values.\n",
            "Number of features: 4186\n",
            "Number of importance values: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nc2WhJOJzzMi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "12N9T5uQzzSI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "j4viDnUgzzXb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MvLdtu1uzzbA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Testing the model with the test dataset"
      ],
      "metadata": {
        "id": "uKWRwsxRxunJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the test dataset\n",
        "# Replace 'your_test_dataset.csv' with your actual test dataset file\n",
        "test_data = pd.read_csv('/content/test_hsbc_df.csv')\n",
        "\n",
        "# Apply the same preprocessing steps as the training data\n",
        "test_data_encoded = pd.get_dummies(test_data, columns=categorical_columns, drop_first=True)\n",
        "\n",
        "# Ensure the test data has the same features as the training data\n",
        "missing_cols = set(X.columns) - set(test_data_encoded.columns)\n",
        "for c in missing_cols:\n",
        "    test_data_encoded[c] = 0\n",
        "\n",
        "# Reorder the columns to match the training data\n",
        "test_data_encoded = test_data_encoded[X.columns]\n",
        "\n",
        "# Define features for the test dataset\n",
        "X_test_final = test_data_encoded.drop('fraud', axis=1)  # Features\n",
        "y_test_final = test_data_encoded['fraud']  # Target variable\n",
        "\n",
        "# Make predictions on the test dataset\n",
        "y_pred_final = rf.predict(X_test_final)\n",
        "y_pred_proba_final = rf.predict_proba(X_test_final)[:, 1]  # Probability estimates for the positive class\n",
        "\n",
        "# Calculate Accuracy Metrics on the test dataset\n",
        "accuracy_test = accuracy_score(y_test_final, y_pred_final)\n",
        "precision_test = precision_score(y_test_final, y_pred_final, average='binary')\n",
        "recall_test = recall_score(y_test_final, y_pred_final, average='binary')\n",
        "f1_test = f1_score(y_test_final, y_pred_final, average='binary')\n",
        "roc_auc_test = roc_auc_score(y_test_final, y_pred_proba_final)\n",
        "conf_matrix_test = confusion_matrix(y_test_final, y_pred_final)\n",
        "\n",
        "# Print the metrics for the test dataset\n",
        "print(\"Test Accuracy:\", accuracy_test)\n",
        "print(\"Test Precision:\", precision_test)\n",
        "print(\"Test Recall:\", recall_test)\n",
        "print(\"Test F1 Score:\", f1_test)\n",
        "print(\"Test ROC AUC Score:\", roc_auc_test)\n",
        "print(\"Test Confusion Matrix:\\n\", conf_matrix_test)\n",
        "\n",
        "# Plot the confusion matrix using Seaborn\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix_test, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])\n",
        "plt.title('Confusion Matrix (Test Data)')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()\n",
        "\n",
        "# Plot ROC Curve for test data\n",
        "fpr_test, tpr_test, thresholds_test = roc_curve(y_test_final, y_pred_proba_final)\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr_test, tpr_test, color='blue', label='ROC curve (area = {:.2f})'.format(roc_auc_test))\n",
        "plt.plot([0, 1], [0, 1], color='red', linestyle='--')  # Diagonal line\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve (Test Data)')\n",
        "plt.legend(loc='lower right')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "RuLImfVNw0dT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report\n",
        "\n",
        "# Assuming you have your test dataset features and labels\n",
        "y_test_pred = rf.predict(X_test)\n",
        "y_test_pred_proba = rf.predict_proba(X_test)[:, 1]\n",
        "\n",
        "# Calculate metrics\n",
        "accuracy = accuracy_score(y_test, y_test_pred)\n",
        "precision = precision_score(y_test, y_test_pred, average='binary')\n",
        "recall = recall_score(y_test, y_test_pred, average='binary')\n",
        "f1 = f1_score(y_test, y_test_pred, average='binary')\n",
        "roc_auc = roc_auc_score(y_test, y_test_pred_proba)\n",
        "\n",
        "# Create a list of metrics for plotting\n",
        "metrics = ['Accuracy', 'Precision', 'Recall', 'F1 Score', 'ROC AUC']\n",
        "values = [accuracy, precision, recall, f1, roc_auc]\n",
        "\n",
        "# Set up the bar plot for metrics\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=metrics, y=values, palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('Model Performance Metrics')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metrics')\n",
        "plt.show()\n",
        "\n",
        "# Calculate confusion matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "\n",
        "# Plot the confusion matrix using Seaborn\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=['Class 0', 'Class 1'],\n",
        "            yticklabels=['Class 0', 'Class 1'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "I_g1i2FOw0kL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix\n",
        "\n",
        "# Load your dataset\n",
        "# Replace 'your_dataset.csv' with your actual dataset file\n",
        "data = pd.read_csv('/content/train_hsbc_df - train_hsbc_df.csv')\n",
        "\n",
        "# Identify categorical columns\n",
        "categorical_columns = data.select_dtypes(include=['object', 'category']).columns.tolist()\n",
        "\n",
        "# One-Hot Encoding for all categorical columns\n",
        "data_encoded = pd.get_dummies(data, columns=categorical_columns, drop_first=True)\n",
        "\n",
        "# Define Features and Target\n",
        "X = data_encoded.drop('fraud', axis=1)  # Features\n",
        "y = data_encoded['fraud']                  # Target variable\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",
        "\n",
        "# Fit the Random Forest Model\n",
        "rf = RandomForestClassifier(random_state=42)\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_test_pred = rf.predict(X_test)\n",
        "y_test_pred_proba = rf.predict_proba(X_test)[:, 1]  # Probability estimates for the positive class\n",
        "\n",
        "# Calculate metrics\n",
        "accuracy = accuracy_score(y_test, y_test_pred)\n",
        "precision = precision_score(y_test, y_test_pred, average='binary')\n",
        "recall = recall_score(y_test, y_test_pred, average='binary')\n",
        "f1 = f1_score(y_test, y_test_pred, average='binary')\n",
        "roc_auc = roc_auc_score(y_test, y_test_pred_proba)\n",
        "\n",
        "# Create a list of metrics for plotting\n",
        "metrics = ['Accuracy', 'Precision', 'Recall', 'F1 Score', 'ROC AUC']\n",
        "values = [accuracy, precision, recall, f1, roc_auc]\n",
        "\n",
        "# Set up the bar plot for metrics\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=metrics, y=values, palette='viridis')\n",
        "plt.ylim(0, 1)\n",
        "plt.title('Model Performance Metrics on Test Dataset')\n",
        "plt.ylabel('Score')\n",
        "plt.xlabel('Metrics')\n",
        "plt.show()\n",
        "\n",
        "# Calculate confusion matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_test_pred)\n",
        "\n",
        "# Plot the confusion matrix using Seaborn\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=['Class 0', 'Class 1'],\n",
        "            yticklabels=['Class 0', 'Class 1'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('\n"
      ],
      "metadata": {
        "id": "75ZSOyVEw0qG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "x3lVD-55w0wf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f96KbQ1Jw02J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cve7JYt2w07a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VXyBg6fcw1AT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "La7lW5ahw1Ez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tDu8rcH-w1Jj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1bw5NOqsw1Nv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XjO-ydfrmlc8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AlKKzw-Pomqa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h1>DOCUMENTATION FOR THE Project</h1>"
      ],
      "metadata": {
        "id": "ljx1MT1Dw2cE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Documentation](https://docs.google.com/document/d/1V5aGlvrgVjQE7PJ4n6y3ZG32mJ6YvTrDJWT1TzEqZrM/edit?usp=sharing)"
      ],
      "metadata": {
        "id": "-1O2Inavw_Hp"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}