Fast_tag_Fraud_detection.ipynb

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      "source": [
        "# Import necessary libraries"
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        "\n",
        "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, GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n"
      ]
    },
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      "source": [
        "# Load the dataset"
      ],
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      "source": [
        "\n",
        "data = pd.read_csv('https://raw.githubusercontent.com/bpandey369/Fast-tag-Fraud-detection/main/FastagFraudDetection.csv')"
      ],
      "metadata": {
        "id": "960r7f7HN1Uy"
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      "execution_count": 3,
      "outputs": []
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        "data.head()"
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              "   Transaction_ID       Timestamp Vehicle_Type         FastagID TollBoothID  \\\n",
              "0               1  1/6/2023 11:20         Bus   FTG-001-ABC-121       A-101   \n",
              "1               2  1/7/2023 14:55          Car  FTG-002-XYZ-451       B-102   \n",
              "2               3  1/8/2023 18:25   Motorcycle              NaN       D-104   \n",
              "3               4   1/9/2023 2:05        Truck  FTG-044-LMN-322       C-103   \n",
              "4               5  1/10/2023 6:35          Van  FTG-505-DEF-652       B-102   \n",
              "\n",
              "  Lane_Type Vehicle_Dimensions  Transaction_Amount  Amount_paid  \\\n",
              "0   Express              Large                 350          120   \n",
              "1   Regular              Small                 120          100   \n",
              "2   Regular              Small                   0            0   \n",
              "3   Regular              Large                 350          120   \n",
              "4   Express             Medium                 140          100   \n",
              "\n",
              "                   Geographical_Location  Vehicle_Speed Vehicle_Plate_Number  \\\n",
              "0  13.059816123454882, 77.77068662374292             65           KA11AB1234   \n",
              "1  13.059816123454882, 77.77068662374292             78           KA66CD5678   \n",
              "2  13.059816123454882, 77.77068662374292             53           KA88EF9012   \n",
              "3  13.059816123454882, 77.77068662374292             92           KA11GH3456   \n",
              "4  13.059816123454882, 77.77068662374292             60           KA44IJ6789   \n",
              "\n",
              "  Fraud_indicator  \n",
              "0           Fraud  \n",
              "1           Fraud  \n",
              "2       Not Fraud  \n",
              "3           Fraud  \n",
              "4           Fraud  "
            ],
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              "      <td>92</td>\n",
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              "    <tr>\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5000 entries, 0 to 4999\n",
            "Data columns (total 13 columns):\n",
            " #   Column                 Non-Null Count  Dtype \n",
            "---  ------                 --------------  ----- \n",
            " 0   Transaction_ID         5000 non-null   int64 \n",
            " 1   Timestamp              5000 non-null   object\n",
            " 2   Vehicle_Type           5000 non-null   object\n",
            " 3   FastagID               4451 non-null   object\n",
            " 4   TollBoothID            5000 non-null   object\n",
            " 5   Lane_Type              5000 non-null   object\n",
            " 6   Vehicle_Dimensions     5000 non-null   object\n",
            " 7   Transaction_Amount     5000 non-null   int64 \n",
            " 8   Amount_paid            5000 non-null   int64 \n",
            " 9   Geographical_Location  5000 non-null   object\n",
            " 10  Vehicle_Speed          5000 non-null   int64 \n",
            " 11  Vehicle_Plate_Number   5000 non-null   object\n",
            " 12  Fraud_indicator        5000 non-null   object\n",
            "dtypes: int64(4), object(9)\n",
            "memory usage: 507.9+ KB\n"
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              "       Transaction_ID  Transaction_Amount  Amount_paid  Vehicle_Speed\n",
              "count     5000.000000          5000.00000  5000.000000    5000.000000\n",
              "mean      2500.500000           161.06200   141.261000      67.851200\n",
              "std       1443.520003           112.44995   106.480996      16.597547\n",
              "min          1.000000             0.00000     0.000000      10.000000\n",
              "25%       1250.750000           100.00000    90.000000      54.000000\n",
              "50%       2500.500000           130.00000   120.000000      67.000000\n",
              "75%       3750.250000           290.00000   160.000000      82.000000\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Transaction_ID</th>\n",
              "      <th>Transaction_Amount</th>\n",
              "      <th>Amount_paid</th>\n",
              "      <th>Vehicle_Speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.00000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2500.500000</td>\n",
              "      <td>161.06200</td>\n",
              "      <td>141.261000</td>\n",
              "      <td>67.851200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1443.520003</td>\n",
              "      <td>112.44995</td>\n",
              "      <td>106.480996</td>\n",
              "      <td>16.597547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1250.750000</td>\n",
              "      <td>100.00000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>54.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2500.500000</td>\n",
              "      <td>130.00000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>67.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3750.250000</td>\n",
              "      <td>290.00000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>82.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5000.000000</td>\n",
              "      <td>350.00000</td>\n",
              "      <td>350.000000</td>\n",
              "      <td>118.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "    </div>\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\": \"Transaction_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1801.7194007539213,\n        \"min\": 1.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5000.0,\n          2500.5,\n          3750.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transaction_Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1713.5695231513625,\n        \"min\": 0.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          161.062,\n          130.0,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_paid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1721.7163283112593,\n        \"min\": 0.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          141.261,\n          120.0,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vehicle_Speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1747.1260737470761,\n        \"min\": 10.0,\n        \"max\": 5000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          67.8512,\n          67.0,\n          5000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Engineering\n"
      ],
      "metadata": {
        "id": "6eVxeskNBHh4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Crosstabulation\n",
        "from scipy.stats import chi2_contingency\n",
        "crosstab = pd.crosstab(data['Vehicle_Type'], data['Fraud_indicator'])\n",
        "\n",
        "# Chi-square test\n",
        "chi2, p, dof, expected = chi2_contingency(crosstab)\n",
        "print(\"Chi-square Statistic:\", chi2)\n",
        "print(\"p-value:\", p)\n",
        "# Visualization\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='Vehicle_Type', hue='Fraud_indicator', data=data)\n",
        "plt.title('Relationship between Vehicle Type and Fraud Indicator')\n",
        "plt.xlabel('Vehicle Type')\n",
        "plt.ylabel('Count')\n",
        "plt.legend(title='Fraud Indicator', loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "Auc2gRvQkB99",
        "outputId": "e5cadace-0a94-4aa0-d979-ff7ce309739e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chi-square Statistic: 227.36756639387707\n",
            "p-value: 2.791048100424726e-46\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crosstabulation\n",
        "from scipy.stats import chi2_contingency\n",
        "crosstab = pd.crosstab(data['Lane_Type'], data['Fraud_indicator'])\n",
        "\n",
        "# Chi-square test\n",
        "chi2, p, dof, expected = chi2_contingency(crosstab)\n",
        "print(\"Chi-square Statistic:\", chi2)\n",
        "print(\"p-value:\", p)\n",
        "# Visualization\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='Lane_Type', hue='Fraud_indicator', data=data)\n",
        "plt.title('Relationship between Lane_Type and Fraud Indicator')\n",
        "plt.xlabel('Lane_Type')\n",
        "plt.ylabel('Count')\n",
        "plt.legend(title='Fraud Indicator', loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "gs9g_pixkbYs",
        "outputId": "e581c330-585a-41d9-ac63-72b2f1b6705f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chi-square Statistic: 24.180580982131332\n",
            "p-value: 8.771224262158539e-07\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crosstabulation\n",
        "from scipy.stats import chi2_contingency\n",
        "crosstab = pd.crosstab(data['Vehicle_Dimensions'], data['Fraud_indicator'])\n",
        "\n",
        "# Chi-square test\n",
        "chi2, p, dof, expected = chi2_contingency(crosstab)\n",
        "print(\"Chi-square Statistic:\", chi2)\n",
        "print(\"p-value:\", p)\n",
        "# Visualization\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='Vehicle_Dimensions', hue='Fraud_indicator', data=data)\n",
        "plt.title('Relationship between Vehicle_Dimensions and Fraud Indicator')\n",
        "plt.xlabel('Vehicle_Dimensions')\n",
        "plt.ylabel('Count')\n",
        "plt.legend(title='Fraud Indicator', loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "3P5RDSfnk3Na",
        "outputId": "4755d469-6c04-487a-dfff-464b3cf512ea"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chi-square Statistic: 156.4278000403458\n",
            "p-value: 1.076799420376296e-34\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "WGSV2-Sgls5c",
        "outputId": "aae65dbd-8569-4ca8-be30-c601ef864a36"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Transaction_ID       Timestamp Vehicle_Type         FastagID TollBoothID  \\\n",
              "0               1  1/6/2023 11:20         Bus   FTG-001-ABC-121       A-101   \n",
              "1               2  1/7/2023 14:55          Car  FTG-002-XYZ-451       B-102   \n",
              "2               3  1/8/2023 18:25   Motorcycle              NaN       D-104   \n",
              "3               4   1/9/2023 2:05        Truck  FTG-044-LMN-322       C-103   \n",
              "4               5  1/10/2023 6:35          Van  FTG-505-DEF-652       B-102   \n",
              "\n",
              "  Lane_Type Vehicle_Dimensions  Transaction_Amount  Amount_paid  \\\n",
              "0   Express              Large                 350          120   \n",
              "1   Regular              Small                 120          100   \n",
              "2   Regular              Small                   0            0   \n",
              "3   Regular              Large                 350          120   \n",
              "4   Express             Medium                 140          100   \n",
              "\n",
              "                   Geographical_Location  Vehicle_Speed Vehicle_Plate_Number  \\\n",
              "0  13.059816123454882, 77.77068662374292             65           KA11AB1234   \n",
              "1  13.059816123454882, 77.77068662374292             78           KA66CD5678   \n",
              "2  13.059816123454882, 77.77068662374292             53           KA88EF9012   \n",
              "3  13.059816123454882, 77.77068662374292             92           KA11GH3456   \n",
              "4  13.059816123454882, 77.77068662374292             60           KA44IJ6789   \n",
              "\n",
              "  Fraud_indicator  \n",
              "0           Fraud  \n",
              "1           Fraud  \n",
              "2       Not Fraud  \n",
              "3           Fraud  \n",
              "4           Fraud  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff84aa05-c87d-4c45-8242-e3b72954fda3\" 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>Transaction_ID</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>Vehicle_Type</th>\n",
              "      <th>FastagID</th>\n",
              "      <th>TollBoothID</th>\n",
              "      <th>Lane_Type</th>\n",
              "      <th>Vehicle_Dimensions</th>\n",
              "      <th>Transaction_Amount</th>\n",
              "      <th>Amount_paid</th>\n",
              "      <th>Geographical_Location</th>\n",
              "      <th>Vehicle_Speed</th>\n",
              "      <th>Vehicle_Plate_Number</th>\n",
              "      <th>Fraud_indicator</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1/6/2023 11:20</td>\n",
              "      <td>Bus</td>\n",
              "      <td>FTG-001-ABC-121</td>\n",
              "      <td>A-101</td>\n",
              "      <td>Express</td>\n",
              "      <td>Large</td>\n",
              "      <td>350</td>\n",
              "      <td>120</td>\n",
              "      <td>13.059816123454882, 77.77068662374292</td>\n",
              "      <td>65</td>\n",
              "      <td>KA11AB1234</td>\n",
              "      <td>Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1/7/2023 14:55</td>\n",
              "      <td>Car</td>\n",
              "      <td>FTG-002-XYZ-451</td>\n",
              "      <td>B-102</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Small</td>\n",
              "      <td>120</td>\n",
              "      <td>100</td>\n",
              "      <td>13.059816123454882, 77.77068662374292</td>\n",
              "      <td>78</td>\n",
              "      <td>KA66CD5678</td>\n",
              "      <td>Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1/8/2023 18:25</td>\n",
              "      <td>Motorcycle</td>\n",
              "      <td>NaN</td>\n",
              "      <td>D-104</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Small</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.059816123454882, 77.77068662374292</td>\n",
              "      <td>53</td>\n",
              "      <td>KA88EF9012</td>\n",
              "      <td>Not Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1/9/2023 2:05</td>\n",
              "      <td>Truck</td>\n",
              "      <td>FTG-044-LMN-322</td>\n",
              "      <td>C-103</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Large</td>\n",
              "      <td>350</td>\n",
              "      <td>120</td>\n",
              "      <td>13.059816123454882, 77.77068662374292</td>\n",
              "      <td>92</td>\n",
              "      <td>KA11GH3456</td>\n",
              "      <td>Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1/10/2023 6:35</td>\n",
              "      <td>Van</td>\n",
              "      <td>FTG-505-DEF-652</td>\n",
              "      <td>B-102</td>\n",
              "      <td>Express</td>\n",
              "      <td>Medium</td>\n",
              "      <td>140</td>\n",
              "      <td>100</td>\n",
              "      <td>13.059816123454882, 77.77068662374292</td>\n",
              "      <td>60</td>\n",
              "      <td>KA44IJ6789</td>\n",
              "      <td>Fraud</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff84aa05-c87d-4c45-8242-e3b72954fda3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ff84aa05-c87d-4c45-8242-e3b72954fda3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ff84aa05-c87d-4c45-8242-e3b72954fda3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-deca20cb-f105-476e-b765-e584a083f8c5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-deca20cb-f105-476e-b765-e584a083f8c5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-deca20cb-f105-476e-b765-e584a083f8c5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "repr_error": "'str' object has no attribute 'empty'"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization\n",
        "from scipy.stats import ttest_ind\n",
        "\n",
        " #T-test\n",
        "fraudulent_speed = data[data['Fraud_indicator'] == 'Fraud']['Vehicle_Speed']\n",
        "non_fraudulent_speed = data[data['Fraud_indicator'] == 'Not Fraud']['Vehicle_Speed']\n",
        "t_statistic, p_value = ttest_ind(fraudulent_speed, non_fraudulent_speed)\n",
        "print(\"T-test statistic:\", t_statistic)\n",
        "print(\"p-value:\", p_value)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='Fraud_indicator', y='Vehicle_Speed', data=data)\n",
        "plt.title('Relationship between Vehicle Speed and Fraud Indicator')\n",
        "plt.xlabel('Fraud Indicator')\n",
        "plt.ylabel('Vehicle Speed')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "1Es17F5Bkbf8",
        "outputId": "7201d72f-51a5-4408-a971-3b1fb75a8382"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-test statistic: 1.0318234415659226\n",
            "p-value: 0.30220476179872036\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization\n",
        "from scipy.stats import ttest_ind\n",
        "\n",
        " #T-test\n",
        "fraudulent_amount = data[data['Fraud_indicator'] == 'Fraud']['Amount_paid']\n",
        "non_fraudulent_amount = data[data['Fraud_indicator'] == 'Not Fraud']['Amount_paid']\n",
        "t_statistic, p_value = ttest_ind(fraudulent_amount, non_fraudulent_amount)\n",
        "print(\"T-test statistic:\", t_statistic)\n",
        "print(\"p-value:\", p_value)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='Fraud_indicator', y='Amount_paid', data=data)\n",
        "plt.title('Relationship between Amount_paid and Fraud Indicator')\n",
        "plt.xlabel('Fraud Indicator')\n",
        "plt.ylabel('Amount_paid')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "BopGvelkkbjH",
        "outputId": "5aa66b27-fbd6-4f51-dbfb-64173bd63a63"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-test statistic: -16.32390038253155\n",
            "p-value: 2.1152726464470254e-58\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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9N+PGnpAXX3xRkmzvLcn+s5yUlKQ//vhDTZo00a+//qqkpKSbrnv16tXy9/dX586dbfPc3d3Vt2/fbNV2/XavXr2qP//8UxUrVlSxYsX0448/Zmp/u8/7d999pytXrujFF1+0azdkyJBs1ZPhiSee0Lp16+weYWFhtuXlypWzm85qf8wcx5tZvXq1GjRooIcfftg2z8fHR927dze9rusVKVLE7howFxcXPfzww3bvvbt5bT/77DNZLBaNHTs207LrX5e7PV6rV6/Www8/rEaNGtntW9++fRUTE6ODBw/ate/Zs+d9fS0bCg5O7QPygcjISFWuXFlJSUlasGCBtmzZYjs1Rfr71CLDMDRmzBiNGTMmy3UkJCSoTJkyWS6LjY3VG2+8oa+++irTNTB38kvD2bNndenSJdtpMterVq2a0tPTderUKbvTvgIDA+3aZQShjHqaNGmiTp06afz48XrvvfcUGhqqDh066JlnnrE7FtlZ161UqlQp07zKlSvr0qVLOnv27G2HTr7x+RaLRRUrVrSdjvnLL79I+vsXgZtJSkqyC4I3cnZ2VkhIiLZu3Srp7yD16KOPqlGjRkpLS9POnTvl6+urxMREu1/wf/nlF+3bt08+Pj5ZrjchIcHWLikpSaVKlbpluww3Hm/p72OeneP98ccfy8PDQ+XLl9exY8ck/T1wSXBwsKKiotSmTRu79mXLls10/ZKXl5cCAgIyzZP+ec3v5D2ZXXez/2bc+N6qUKGCnJyc7K5D2r59u8aOHavo6GhdunTJrn1SUpLtuNzo5MmTqlixYqZjm9Xxysrly5cVERGhhQsX6vfff5dhGHbbvdHtPqMZgerGffbx8bnlZ+NGZcuWVfPmzW+6vFy5clnOv9PjeDMnT57M8tTR7B7fm8nq81C8eHHt27fPbtt3+toeP35cpUuXlre39y3b3e3xutnxyTj99eTJk3anVt/sdQPyG4IUkA88/PDDtnPRO3TooEaNGumZZ57RkSNHVKRIEVsPxvDhw7P866okVaxYMcv5aWlpevzxx5WYmKiRI0eqatWq8vDw0O+//67w8PBcHQr3es7OzlnOz/iFLONmtzt37tTXX3+ttWvX6rnnntM777yjnTt3qkiRItlelyNlHM+3335btWvXzrLN9ftyM40aNdLkyZOVkpKirVu36vXXX1exYsX0wAMPaOvWrbZrD64PUunp6Xr88cf1yiuvZLnOypUr29qVKlVKUVFRWba7MYjd6fE2DEOffPKJLl68qOrVq2danpCQoAsXLmTrtc3J1/xmN2q+WY+ko95vN9Z5/PhxNWvWTFWrVtW7776rgIAAubi4aPXq1Xrvvfdy9bP84osvauHChRoyZIhCQkLk5eUli8Wirl27Zrnd/PIZzapXIyeOY3Z6r3NCfjiOjnjf0RuFgoIgBeQzzs7OioiIUNOmTfX+++/r1VdftZ2qVLhw4Vv+9TUr+/fv19GjR7V48WK7+62sW7cuU9ub/YJ5Ix8fH7m7u+vIkSOZlh0+fFhOTk6ZehCyq0GDBmrQoIEmT56sJUuWqHv37lq6dKmef/75O1rfjTJ6jK539OhRubu737Qn51bPNwxDx44dU61atST93YsgSZ6enrd9rW51vB999FFduXJFn3zyiX7//XdbYGrcuLEtSFWuXNkWqDK2feHChdtut0KFCvruu+/UsGHDXP2FZfPmzfrtt980YcKETAMv/PXXX+rbt6+++OKLbA9ffStm3pMZPR7nzp2zu39XRi/JncjuZ+dWfvnlF7u/xB87dkzp6em2Uda+/vprpaam6quvvrLr8bnxVMysBAUF6eeff5ZhGHa1ZnW8srJixQr17NlT77zzjm1eSkrKHd9MOigoSNLf+3z9qZhnz57N8Z6+G5k5jsWLF8+0j1euXNGZM2fs5gUFBWX5syW7x/du3M1rW6FCBa1du1aJiYk37ZUyc7xu9jkICgq66WczYzlQEHGNFJAPhYaG6uGHH9b06dOVkpKiUqVKKTQ0VB988EGmL3BJmYYVvl7GXzSv/wumYRiaMWNGprYeHh6SdNtfjpydndWiRQt9+eWXdqcdxcfHa8mSJWrUqJE8PT1vuY4b/fXXX5n+yprRo5PdIZqzIzo62u6ajlOnTunLL79UixYtbvrX3+t9+OGHOn/+vG16xYoVOnPmjG0Erbp166pChQqaNm2aLly4kOn5179Wtzre9evXV+HChTVlyhR5e3vbTkl79NFHtXPnTm3evNmuN0r6e9S36OjoLK89OnfunK5du2Zrl5aWpokTJ2Zqd+3atTv+5fhGGaf1jRgxQp07d7Z79OnTR5UqVbppr5hZZt6TGWH3+mHvL168qMWLF9/x9rP72bmVjNsPZJg1a5Yk2d5bWX2Wk5KStHDhwtuuu3Xr1jp9+rRtKHnp71Ee582bl63anJ2dM30+Z82adcc9M82bN1fhwoU1a9Ysu/VeP7JkbjFzHCtUqJDp9gjz5s3LtN+tW7fWzp07tXv3btu8s2fP5tj7+1bu5rXt1KmTDMPQ+PHjMy3LOD5mjpeHh0eWn4HWrVtr9+7dio6Ots27ePGi5s2bp+Dg4Cx7rIGCgB4pIJ8aMWKEnnrqKS1atEgvvPCCIiMj1ahRI9WsWVN9+vRR+fLlFR8fr+joaP3222/63//+l+V6qlatqgoVKmj48OH6/fff5enpqc8++yzLv/pmDMIwePBghYWFydnZ2e7C/+tNmjRJ69atU6NGjTRgwAAVKlRIH3zwgVJTU7O8z8ntLF68WLNnz9aTTz6pChUq6Pz58/r3v/8tT09PtW7d2vT6buaBBx5QWFiY3fDnkrL8RSIr3t7eatSokXr16qX4+HhNnz5dFStWVJ8+fSRJTk5O+s9//qNWrVqpRo0a6tWrl8qUKaPff/9dGzdulKenp77++mtJ/xzv119/XV27dlXhwoXVrl07eXh4yN3dXXXr1tXOnTtt95CS/u6Runjxoi5evJgpSI0YMUJfffWV2rZtaxue++LFi9q/f79WrFihmJgYlSxZUk2aNFG/fv0UERGhvXv3qkWLFipcuLB++eUXLV++XDNmzLC7cP1OpKam6rPPPtPjjz9+05s5t2/fXjNmzFBCQsJNr9cyI7vvyRYtWigwMFC9e/fWiBEj5OzsrAULFsjHx0exsbF3tO3atWvL2dlZU6ZMUVJSkqxWq+2+O9l14sQJtW/fXi1btlR0dLQ+/vhjPfPMM3rwwQdtdbu4uKhdu3bq16+fLly4oH//+98qVapUln9guV6fPn30/vvvq0ePHtqzZ4/8/f310UcfZRq2/Gbatm2rjz76SF5eXqpevbqio6P13XffqUSJEtnev+v5+Pho+PDhioiIUNu2bdW6dWv99NNPWrNmje1WALnFzHF8/vnn9cILL6hTp056/PHH9b///U9r167NVOMrr7yijz76SC1bttRLL71kG/48KCjI7nqm3HA3r23Tpk317LPPaubMmfrll1/UsmVLpaena+vWrWratKkGDRpk6njVrVtXc+bM0aRJk1SxYkWVKlVKjz32mF599VV98sknatWqlQYPHixvb28tXrxYJ06c0GeffcbNdlFw5dn4gAAyyRgqOKshv9PS0owKFSoYFSpUMK5du2YYhmEcP37c6NGjh+Hn52cULlzYKFOmjNG2bVtjxYoVtudlNfz5wYMHjebNmxtFihQxSpYsafTp08f43//+l2n46GvXrhkvvvii4ePjY1gsFrshopXFMMA//vijERYWZhQpUsRwd3c3mjZtauzYsSNb+3hjnT/++KPRrVs3IzAw0LBarUapUqWMtm3b2g1VnjH8+dtvv53peN1Y382GPx84cKDx8ccfG5UqVTKsVqtRp06dbA1ZnVHvJ598YowaNcooVaqU4ebmZrRp08ZuCPkMP/30k9GxY0ejRIkShtVqNYKCgowuXboY69evt2s3ceJEo0yZMoaTk1OmYZJHjBhhSDKmTJli95yMYY2vH+Y7w/nz541Ro0YZFStWNFxcXIySJUsajzzyiDFt2jTjypUrdm3nzZtn1K1b13BzczOKFi1q1KxZ03jllVeM06dP29oEBQUZbdq0ybSdG4fXvtFnn31mSDLmz59/0zabNm2yGzq+SZMmRo0aNTK1u1kNGa/n9bLznjQMw9izZ49Rv359w8XFxQgMDDTefffdmw5/nt39//e//22UL1/ecHZ2NjUUesZ79eDBg0bnzp2NokWLGsWLFzcGDRpkXL582a7tV199ZdSqVctwdXU1goODjSlTphgLFizIVHdW9Z08edJo37694e7ubpQsWdJ46aWXbEPe367Wv/76y+jVq5dRsmRJo0iRIkZYWJhx+PDhTMOAZ/fzbhh//4wbP3684e/vb7i5uRmhoaHGzz//nGmdN5PV63+9m712hpH945iWlmaMHDnSKFmypOHu7m6EhYUZx44dy7LGffv2GU2aNDFcXV2NMmXKGBMnTjTmz59/V8OfZ/V5uHEIc8PI/mub1XOvXbtmvP3220bVqlUNFxcXw8fHx2jVqpWxZ88e08crLi7OaNOmjVG0aFFDkt178Pjx40bnzp2NYsWKGa6ursbDDz9srFq1yq6WjPfJ8uXLb3m8gPzCYhj54OpsAMgDFotFAwcO1Pvvv+/oUgCbcePGafz48Tp79myu98YAAHIOfakAAAAAYBLXSAEAkAsuXLiQ5YAj18vOSJEAgPyJIAUAQC6YNm3abQcxOXHiRB5VAwDIaVwjBQBALvj111/166+/3rJNo0aNbjqqIQAgfyNIAQAAAIBJDDYBAAAAACZxjZSk9PR0nT59WkWLFrXd9BIAAADA/ccwDJ0/f16lS5e+5Q2jCVKSTp8+rYCAAEeXAQAAACCfOHXqlMqWLXvT5QQpSUWLFpX098Hy9PR0cDUAAAAAHCU5OVkBAQG2jHAzBCnJdjqfp6cnQQoAAADAbS/5YbAJAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGCSQ4PUnDlzVKtWLXl6esrT01MhISFas2aNbXloaKgsFovd44UXXrBbR2xsrNq0aSN3d3eVKlVKI0aM0LVr1/J6VwAAAADcRwo5cuNly5bVW2+9pUqVKskwDC1evFhPPPGEfvrpJ9WoUUOS1KdPH02YMMH2HHd3d9v/09LS1KZNG/n5+WnHjh06c+aMevToocKFC+vNN9/M8/0BAAAAcH+wGIZhOLqI63l7e+vtt99W7969FRoaqtq1a2v69OlZtl2zZo3atm2r06dPy9fXV5I0d+5cjRw5UmfPnpWLi0u2tpmcnCwvLy8lJSXJ09Mzp3YFAAAAQAGT3Wzg0B6p66WlpWn58uW6ePGiQkJCbPOjoqL08ccfy8/PT+3atdOYMWNsvVLR0dGqWbOmLURJUlhYmPr3768DBw6oTp06WW4rNTVVqamptunk5ORc2isAAOAoKSkpio2NdXQZuI8EBgbK1dXV0WUgjzg8SO3fv18hISFKSUlRkSJFtHLlSlWvXl2S9MwzzygoKEilS5fWvn37NHLkSB05ckSff/65JCkuLs4uREmyTcfFxd10mxERERo/fnwu7REAAMgPYmNj1bdvX0eXgfvIvHnzVLlyZUeXgTzi8CBVpUoV7d27V0lJSVqxYoV69uypzZs3q3r16nY//GrWrCl/f381a9ZMx48fV4UKFe54m6NGjdKwYcNs08nJyQoICLir/QAAAPlLYGCg5s2b5+gyCpSTJ09q8uTJev311xUUFOTocgqcwMBAR5eAPOTwIOXi4qKKFStKkurWravvv/9eM2bM0AcffJCpbf369SVJx44dU4UKFeTn56fdu3fbtYmPj5ck+fn53XSbVqtVVqs1p3YBAADkQ66urvQO3KGgoCCOHXAb+e4+Uunp6XbXL11v7969kiR/f39JUkhIiPbv36+EhARbm3Xr1snT09N2eiAAAAAA5DSH9kiNGjVKrVq1UmBgoM6fP68lS5Zo06ZNWrt2rY4fP64lS5aodevWKlGihPbt26ehQ4eqcePGqlWrliSpRYsWql69up599llNnTpVcXFxGj16tAYOHEiPEwAAAIBc49AglZCQoB49eujMmTPy8vJSrVq1tHbtWj3++OM6deqUvvvuO02fPl0XL15UQECAOnXqpNGjR9ue7+zsrFWrVql///4KCQmRh4eHevbsaXffKQAAAADIaQ4NUvPnz7/psoCAAG3evPm26wgKCtLq1atzsiwAAAAAuKV8d40UAAAAAOR3BCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJDg1Sc+bMUa1ateTp6SlPT0+FhIRozZo1tuUpKSkaOHCgSpQooSJFiqhTp06Kj4+3W0dsbKzatGkjd3d3lSpVSiNGjNC1a9fyelcAAAAA3EccGqTKli2rt956S3v27NEPP/ygxx57TE888YQOHDggSRo6dKi+/vprLV++XJs3b9bp06fVsWNH2/PT0tLUpk0bXblyRTt27NDixYu1aNEivfHGG47aJQAAAAD3AYthGIaji7iet7e33n77bXXu3Fk+Pj5asmSJOnfuLEk6fPiwqlWrpujoaDVo0EBr1qxR27Ztdfr0afn6+kqS5s6dq5EjR+rs2bNycXHJ1jaTk5Pl5eWlpKQkeXp65tq+AQAA5GdHjx5V3759NW/ePFWuXNnR5QAOkd1skG+ukUpLS9PSpUt18eJFhYSEaM+ePbp69aqaN29ua1O1alUFBgYqOjpakhQdHa2aNWvaQpQkhYWFKTk52darlZXU1FQlJyfbPQAAAAAguxwepPbv368iRYrIarXqhRde0MqVK1W9enXFxcXJxcVFxYoVs2vv6+uruLg4SVJcXJxdiMpYnrHsZiIiIuTl5WV7BAQE5OxOAQAAALinOTxIValSRXv37tWuXbvUv39/9ezZUwcPHszVbY4aNUpJSUm2x6lTp3J1ewAAAADuLYUcXYCLi4sqVqwoSapbt66+//57zZgxQ08//bSuXLmic+fO2fVKxcfHy8/PT5Lk5+en3bt3260vY1S/jDZZsVqtslqtObwnAAAAAO4XDu+RulF6erpSU1NVt25dFS5cWOvXr7ctO3LkiGJjYxUSEiJJCgkJ0f79+5WQkGBrs27dOnl6eqp69ep5XjsAAACA+4NDe6RGjRqlVq1aKTAwUOfPn9eSJUu0adMmrV27Vl5eXurdu7eGDRsmb29veXp66sUXX1RISIgaNGggSWrRooWqV6+uZ599VlOnTlVcXJxGjx6tgQMH0uMEAAAAINc4NEglJCSoR48eOnPmjLy8vFSrVi2tXbtWjz/+uCTpvffek5OTkzp16qTU1FSFhYVp9uzZtuc7Oztr1apV6t+/v0JCQuTh4aGePXtqwoQJjtolAAAAAPeBfHcfKUfgPlIAAADcRwqQCuB9pAAAAACgoCBIAQAAAIBJBCkAAAAAMIkgBQAAAAAmOfyGvAAA4Pbi4+OVlJTk6DJwjzt58qTdv0Bu8vLykq+vr6PLuGOM2idG7QMA5G/x8fH6v2d76OqVVEeXAgA5prCLVR9/9GG+C1PZzQb0SAEAkM8lJSXp6pVUXS7fROmuXo4uBwDumlNKkvTrZiUlJeW7IJVdBCkAAAqIdFcvpXuUdHQZAAAx2AQAAAAAmEaQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACY5NAgFRERoYceekhFixZVqVKl1KFDBx05csSuTWhoqCwWi93jhRdesGsTGxurNm3ayN3dXaVKldKIESN07dq1vNwVAAAAAPeRQo7c+ObNmzVw4EA99NBDunbtml577TW1aNFCBw8elIeHh61dnz59NGHCBNu0u7u77f9paWlq06aN/Pz8tGPHDp05c0Y9evRQ4cKF9eabb+bp/gAAAAC4Pzg0SH3zzTd204sWLVKpUqW0Z88eNW7c2Dbf3d1dfn5+Wa7j22+/1cGDB/Xdd9/J19dXtWvX1sSJEzVy5EiNGzdOLi4uuboPAAAAAO4/+eoaqaSkJEmSt7e33fyoqCiVLFlSDzzwgEaNGqVLly7ZlkVHR6tmzZry9fW1zQsLC1NycrIOHDiQ5XZSU1OVnJxs9wAAAACA7HJoj9T10tPTNWTIEDVs2FAPPPCAbf4zzzyjoKAglS5dWvv27dPIkSN15MgRff7555KkuLg4uxAlyTYdFxeX5bYiIiI0fvz4XNoTAAAAAPe6fBOkBg4cqJ9//lnbtm2zm9+3b1/b/2vWrCl/f381a9ZMx48fV4UKFe5oW6NGjdKwYcNs08nJyQoICLizwgEAAADcd/LFqX2DBg3SqlWrtHHjRpUtW/aWbevXry9JOnbsmCTJz89P8fHxdm0ypm92XZXVapWnp6fdAwAAAACyy6FByjAMDRo0SCtXrtSGDRtUrly52z5n7969kiR/f39JUkhIiPbv36+EhARbm3Xr1snT01PVq1fPlboBAAAA3N8cemrfwIEDtWTJEn355ZcqWrSo7ZomLy8vubm56fjx41qyZIlat26tEiVKaN++fRo6dKgaN26sWrVqSZJatGih6tWr69lnn9XUqVMVFxen0aNHa+DAgbJarY7cPQAAAAD3KIf2SM2ZM0dJSUkKDQ2Vv7+/7fHpp59KklxcXPTdd9+pRYsWqlq1ql5++WV16tRJX3/9tW0dzs7OWrVqlZydnRUSEqL/+7//U48ePezuOwUAAAAAOcmhPVKGYdxyeUBAgDZv3nzb9QQFBWn16tU5VRYAAAAA3FK+GGwCAAAAAAoSghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmFcpOo5kzZ2Z7hYMHD77jYgAAAACgIMhWkHrvvffsps+ePatLly6pWLFikqRz587J3d1dpUqVIkgBAAAAuOdl69S+EydO2B6TJ09W7dq1dejQISUmJioxMVGHDh3Sv/71L02cODG36wUAAAAAhzN9jdSYMWM0a9YsValSxTavSpUqeu+99zR69OgcLQ4AAAAA8iPTQerMmTO6du1apvlpaWmKj4/PkaIAAAAAID8zHaSaNWumfv366ccff7TN27Nnj/r376/mzZvnaHEAAAAAkB+ZDlILFiyQn5+f6tWrJ6vVKqvVqocffli+vr76z3/+kxs1AgAAAEC+kq1R+67n4+Oj1atX6+jRozp8+LAkqWrVqqpcuXKOFwcAAAAA+ZHpIJWhcuXKhCcAAAAA96VsBalhw4Zp4sSJ8vDw0LBhw27Z9t133832xiMiIvT555/r8OHDcnNz0yOPPKIpU6bYjQiYkpKil19+WUuXLlVqaqrCwsI0e/Zs+fr62trExsaqf//+2rhxo4oUKaKePXsqIiJChQrdcU4EAAAAgJvKVtL46aefdPXqVdv/b8ZisZja+ObNmzVw4EA99NBDunbtml577TW1aNFCBw8elIeHhyRp6NCh+u9//6vly5fLy8tLgwYNUseOHbV9+3ZJf48W2KZNG/n5+WnHjh06c+aMevToocKFC+vNN980VQ8AAAAAZIfFMAzD0UVkOHv2rEqVKqXNmzercePGSkpKko+Pj5YsWaLOnTtLkg4fPqxq1aopOjpaDRo00Jo1a9S2bVudPn3a1ks1d+5cjRw5UmfPnpWLi8ttt5ucnCwvLy8lJSXJ09MzV/cRAACzjh49qr59++pi9fZK9yjp6HIA4K45XfxDHge/0rx58/Ld5ULZzQamR+3LTUlJSZIkb29vSX8Pq3716lW7YdWrVq2qwMBARUdHS5Kio6NVs2ZNu1P9wsLClJycrAMHDmS5ndTUVCUnJ9s9AAAAACC77ugioh9++EHLli1TbGysrly5Yrfs888/v6NC0tPTNWTIEDVs2FAPPPCAJCkuLk4uLi4qVqyYXVtfX1/FxcXZ2lwfojKWZyzLSkREhMaPH39HdQIAAACA6R6ppUuX6pFHHtGhQ4e0cuVKXb16VQcOHNCGDRvk5eV1x4UMHDhQP//8s5YuXXrH68iuUaNGKSkpyfY4depUrm8TAAAAwL3DdJB688039d577+nrr7+Wi4uLZsyYocOHD6tLly4KDAy8oyIGDRqkVatWaePGjSpbtqxtvp+fn65cuaJz587ZtY+Pj5efn5+tTXx8fKblGcuyYrVa5enpafcAAAAAgOwyHaSOHz+uNm3aSJJcXFx08eJFWSwWDR06VPPmzTO1LsMwNGjQIK1cuVIbNmxQuXLl7JbXrVtXhQsX1vr1623zjhw5otjYWIWEhEiSQkJCtH//fiUkJNjarFu3Tp6enqpevbrZ3QMAAACA2zJ9jVTx4sV1/vx5SVKZMmX0888/q2bNmjp37pwuXbpkal0DBw7UkiVL9OWXX6po0aK2a5q8vLzk5uYmLy8v9e7dW8OGDZO3t7c8PT314osvKiQkRA0aNJAktWjRQtWrV9ezzz6rqVOnKi4uTqNHj9bAgQNltVrN7h4AAAAA3JbpINW4cWOtW7dONWvW1FNPPaWXXnpJGzZs0Lp169SsWTNT65ozZ44kKTQ01G7+woULFR4eLkl677335OTkpE6dOtndkDeDs7OzVq1apf79+yskJEQeHh7q2bOnJkyYYHbXAAAAACBbTAep999/XykpKZKk119/XYULF9aOHTvUqVMnjR492tS6snMLK1dXV0VGRioyMvKmbYKCgrR69WpT2wYAAACAO2U6SGXc40mSnJyc9Oqrr+ZoQQDyXlpamvbt26fExER5e3urVq1acnZ2dnRZAAAA+dYd3UcqLS1NK1eu1KFDhyRJ1atX1xNPPKFChe5odQAcaMuWLZo9e7bdfdf8/Pw0YMAANW7c2IGVAQAA5F+mR+07cOCAKleurJ49e2rlypVauXKlevbsqUqVKunnn3/OjRoB5JItW7Zo7NixKl++vCIjI7V69WpFRkaqfPnyGjt2rLZs2eLoEgEAAPIl00Hq+eefV40aNfTbb7/pxx9/1I8//qhTp06pVq1a6tu3b27UCCAXpKWlafbs2QoJCdGkSZNUo0YNubu7q0aNGpo0aZJCQkI0Z84cpaWlObpUAACAfMd0kNq7d68iIiJUvHhx27zixYtr8uTJ+umnn3K0OAC5Z9++fYqLi1P37t3l5GT/o8DJyUndu3fXmTNntG/fPgdVCAAAkH+ZDlKVK1dWfHx8pvkJCQmqWLFijhQFIPclJiZKUqYbYWfImJ/RDgAAAP8wHaQiIiI0ePBgrVixQr/99pt+++03rVixQkOGDNGUKVOUnJxsewDIvzJG4Dxx4kSWyzPmXz9SJwAAAP5mepi9tm3bSpK6dOkii8Ui6Z/7QbVr1842bbFYuLYCyMdq1aolPz8/RUVFadKkSXan96WnpysqKkr+/v6qVauWA6sEAADIn0wHqY0bN+ZGHQDymLOzswYMGKCxY8dq9OjR6t69u8qVK6cTJ04oKipK0dHRGj9+PPeTAgAAyILpINWkSZNstRswYIBq1KihkiVLmi4KQN5o3Lixxo8fr9mzZ2vgwIG2+f7+/ho/fjz3kQIAALiJXLuD7scff6zhw4cTpIB8rnHjxmrYsKH27dunxMREeXt7q1atWvREAQAA3EKuBamM66YA5H/Ozs6qU6eOo8sAAAAoMEyP2gfg3hMTE6NmzZopNDRUzZo1U0xMjKNLAgAAyNdyrUcKQMHQtGlTux7ktLQ0hYeHy2KxMLgMAADATdAjBdzHrg9RVqtV/fr1k9VqlfT36blNmzZ1ZHkAAAD5Fj1SwH0qJibGFqKWLl0qPz8/SVK3bt0UFxenrl27yjAMxcTEKDg42IGVAgAA5D+me6RiY2OzHEjCMAzFxsbapv/v//5Pnp6ed1cdgFzTu3dvSX/3RGWEqAx+fn62nqmMdgAAAPiH6SBVrlw5nT17NtP8xMRElStXzjY9Z84chj4H8rG0tDRJUnh4eJbLu3fvbtcOAAAA/zAdpAzDkMViyTT/woULcnV1zZGiAOS+jPtELVq0KMvlUVFRdu0AAADwj2xfIzVs2DBJksVi0ZgxY+Tu7m5blpaWpl27dql27do5XiCA3DF//nyFh4crNTVVcXFxdqf3xcXFKTU11dYOAAAA9rIdpH766SdJf/dI7d+/Xy4uLrZlLi4uevDBBzV8+PCcrxBArggODpbFYpFhGOratausVqu6d++uqKgoW4iyWCwMNAEAAJCFbAepjPvJ9OrVSzNmzGAgCeAesHHjRtsQ6KmpqVqwYIFtGfeRAgAAuDnTw58vXLgwN+oA4CAbN25UTEyMevfurbS0NDk7O2v+/Pn0RAEAANyC6SB18eJFvfXWW1q/fr0SEhKUnp5ut/zXX3/NseIA5I3g4GCtX7/e0WUAAAAUGKaD1PPPP6/Nmzfr2Weflb+/f5Yj+AEAAADAvcx0kFqzZo3++9//qmHDhrlRDwAAAADke6bvI1W8eHF5e3vnRi0AAAAAUCCYDlITJ07UG2+8oUuXLuVGPQAAAACQ75k+te+dd97R8ePH5evrq+DgYBUuXNhu+Y8//phjxQEAAABAfmQ6SHXo0CEXygAAAACAgsN0kBo7dmxu1AEAAAAABYbpa6QAAAAA4H5nukfKycnplveOSktLu6uCAAAAACC/Mx2kVq5caTd99epV/fTTT1q8eLHGjx+fY4UBAAAAQH5lOkg98cQTmeZ17txZNWrU0KeffqrevXvnSGEAAAAAkF/l2DVSDRo00Pr163NqdQAAAACQb+VIkLp8+bJmzpypMmXK5MTqAOSxmJgYNWvWTKGhoWrWrJliYmIcXRIAAEC+ZvrUvuLFi9sNNmEYhs6fPy93d3d9/PHHOVocgNzXtGlTGYZhm05LS1N4eLgsFos2btzowMoAAADyL9NBavr06XbTTk5O8vHxUf369VW8ePGcqgtAHrg+RFmtVoWHh2vRokVKTU2VYRhq2rQpYQoAACALpoNUz549c6MOAHksJibGFqKWLl0qPz8/SVK3bt0UFxenrl27yjAMxcTEKDg42IGVAgAA5D+mg5QknTt3TvPnz9ehQ4ckSTVq1NBzzz0nLy+vHC0OQO7JGGHTarXaQlQGPz8/Wa1Wpaamqnfv3gwkAwAAcAPTg0388MMPqlChgt577z0lJiYqMTFR7777ripUqKAff/wxN2oEkAsybp4dHh6e5fLu3bvbtQMAAMA/TAepoUOHqn379oqJidHnn3+uzz//XCdOnFDbtm01ZMiQXCgRQG5wdnaWJC1atCjL5VFRUXbtAAAA8I876pEaOXKkChX656zAQoUK6ZVXXtEPP/yQo8UByD3z58+XJKWmpiouLs5uWVxcnFJTU+3aAQAA4B+mr5Hy9PRUbGysqlatajf/1KlTKlq0aI4VBiB3BQcHy2KxyDAMde3aVVarVd27d1dUVJQtRFksFgaaAAAAyILpHqmnn35avXv31qeffqpTp07p1KlTWrp0qZ5//nl169YtN2oEkEs2btxouy9camqqFixYYBeiGPocAAAga6Z7pKZNmyaLxaIePXro2rVrkqTChQurf//+euutt3K8QAC5a+PGjYqJiVHv3r2VlpYmZ2dnzZ8/n54oAACAWzAdpFxcXDRjxgxFRETo+PHjkqQKFSrI3d09x4sDkDeCg4MZ4hwAAMCEO7qPlCS5u7urZs2aOVkLAAdZtWqVpk2bZpsePny42rZt68CKAAAA8jfT10ilpKTo7bffVuvWrVWvXj3961//snuYsWXLFrVr106lS5eWxWLRF198Ybc8PDxcFovF7tGyZUu7NomJierevbs8PT1VrFgx9e7dWxcuXDC7W8B9KzQ01C5ESX+fwhsaGuqYggAAAAoA0z1SvXv31rfffqvOnTvr4Ycftl2oficuXryoBx98UM8995w6duyYZZuWLVtq4cKFtmmr1Wq3vHv37jpz5ozWrVunq1evqlevXurbt6+WLFlyx3UB94sbw1JgYKBiY2Ptlm/atClviwIAACgATAepVatWafXq1WrYsOFdb7xVq1Zq1arVLdtYrVb5+flluezQoUP65ptv9P3336tevXqSpFmzZql169aaNm2aSpcufdc1AveqVatW2f4/ZswYNWvWzDa9fv16TZw40daO0/wAAADsmT61r0yZMnl6v6hNmzapVKlSqlKlivr3768///zTtiw6OlrFihWzhShJat68uZycnLRr166brjM1NVXJycl2D+B+c/3pfNeHqBunbzztDwAAAHcQpN555x2NHDlSJ0+ezI167LRs2VIffvih1q9frylTpmjz5s1q1aqV0tLSJElxcXEqVaqU3XMKFSokb29vxcXF3XS9ERER8vLysj0CAgJydT+A/CwwMDDL+f7+/nlcCQAAQMFh+tS+evXqKSUlReXLl5e7u7sKFy5stzwxMTHHiuvatavt/zVr1lStWrVUoUIFbdq0KdNf0M0YNWqUhg0bZptOTk4mTOG+df01Udc7c+ZMHlcCAABQcJgOUt26ddPvv/+uN998U76+vnc12IRZ5cuXV8mSJXXs2DE1a9ZMfn5+SkhIsGtz7do1JSYm3vS6Kunv665uHLQCuN8MHz7cdtre+vXrM10jdX07AAAA2DMdpHbs2KHo6Gg9+OCDuVHPLf3222/6888/bacchYSE6Ny5c9qzZ4/q1q0rSdqwYYPS09NVv379PK8PKEjatm1rC1ITJ07UxIkT5e/vn6knioEmAAAAMjN9jVTVqlV1+fLlHNn4hQsXtHfvXu3du1eSdOLECe3du1exsbG6cOGCRowYoZ07dyomJkbr16/XE088oYoVKyosLEySVK1aNbVs2VJ9+vTR7t27tX37dg0aNEhdu3ZlxD4gG24c2vzGEMXQ5wAAAFkzHaTeeustvfzyy9q0aZP+/PPPuxr97ocfflCdOnVUp04dSdKwYcNUp04dvfHGG3J2dta+ffvUvn17Va5cWb1791bdunW1detWu9PyoqKiVLVqVTVr1kytW7dWo0aNNG/ePLO7Bdy3Nm3alOn0veHDhxOiAAAAbsFiGIZh5glOTn9nrxuvjTIMQxaLxTaiXkGSnJwsLy8vJSUlydPT09HlAABg5+jRo+rbt68uVm+vdI+Sji4HAO6a08U/5HHwK82bN0+VK1d2dDl2spsNTF8jtXHjxpsu279/v9nVAQAAAECBYzpINWnSxG76/Pnz+uSTT/Sf//xHe/bs0aBBg3KsOAAAAADIj0xfI5Vhy5Yt6tmzp/z9/TVt2jQ99thj2rlzZ07WBgAAAAD5kqkeqbi4OC1atEjz589XcnKyunTpotTUVH3xxReqXr16btUIAAAAAPlKtoNUu3bttGXLFrVp00bTp09Xy5Yt5ezsrLlz5+ZmfYBpKSkpio2NdXQZuI8EBgbK1dXV0WUAAIA8lO0gtWbNGg0ePFj9+/dXpUqVcrMm4K7Exsaqb9++ji4D95H8OOIQAADIXdkOUtu2bdP8+fNVt25dVatWTc8++6y6du2am7UBdyQwMJB7id2BkydPavLkyXr99dcVFBTk6HIKlMDAQEeXAAAA8li2g1SDBg3UoEEDTZ8+XZ9++qkWLFigYcOGKT09XevWrVNAQICKFi2am7UC2eLq6krvwF0ICgri+AEAANyG6VH7PDw89Nxzz2nbtm3av3+/Xn75Zb311lsqVaqU2rdvnxs1AgAAAEC+csfDn0tSlSpVNHXqVP3222/65JNPcqomAAAAAMjX7ipIZXB2dlaHDh301Vdf5cTqAAAAACBfy5EgBQAAAAD3E4IUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMKOboAAACQPU6Xzzm6BADIEffCzzOCFAAABYTbiS2OLgEA8P8RpAAAKCAul2usdLdiji4DAO6a0+VzBf6PQwQpAAAKiHS3Ykr3KOnoMgAAYrAJAAAAADCNIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGBSIUcXgNuLj49XUlKSo8vAPe7kyZN2/wK5ycvLS76+vo4uAwCAO0aQyufi4+P1f8/20NUrqY4uBfeJyZMnO7oE3AcKu1j18UcfEqYAAAUWQSqfS0pK0tUrqbpcvonSXb0cXQ4A3DWnlCTp181KSkoiSAEACiyCVAGR7uqldI+Sji4DAAAAgBhsAgAAAABMI0gBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIcGqS1btqhdu3YqXbq0LBaLvvjiC7vlhmHojTfekL+/v9zc3NS8eXP98ssvdm0SExPVvXt3eXp6qlixYurdu7cuXLiQh3sBAAAA4H7j0CB18eJFPfjgg4qMjMxy+dSpUzVz5kzNnTtXu3btkoeHh8LCwpSSkmJr0717dx04cEDr1q3TqlWrtGXLFvXt2zevdgEAAADAfcih95Fq1aqVWrVqleUywzA0ffp0jR49Wk888YQk6cMPP5Svr6+++OILde3aVYcOHdI333yj77//XvXq1ZMkzZo1S61bt9a0adNUunTpPNsXAAAAAPePfHtD3hMnTiguLk7Nmze3zfPy8lL9+vUVHR2trl27Kjo6WsWKFbOFKElq3ry5nJyctGvXLj355JNZrjs1NVWpqam26eTk5NzbkRzidPmco0sAgBzBzzMAwL0g3wapuLg4SZKvr6/dfF9fX9uyuLg4lSpVym55oUKF5O3tbWuTlYiICI0fPz6HK85dbie2OLoEAAAAAP9fvg1SuWnUqFEaNmyYbTo5OVkBAQEOrOj2LpdrrHS3Yo4uAwDumtPlc/xxCABQ4OXbIOXn5ydJio+Pl7+/v21+fHy8ateubWuTkJBg97xr164pMTHR9vysWK1WWa3WnC86F6W7FVO6R0lHlwEAAABA+fg+UuXKlZOfn5/Wr19vm5ecnKxdu3YpJCREkhQSEqJz585pz549tjYbNmxQenq66tevn+c1AwAAALg/OLRH6sKFCzp27Jht+sSJE9q7d6+8vb0VGBioIUOGaNKkSapUqZLKlSunMWPGqHTp0urQoYMkqVq1amrZsqX69OmjuXPn6urVqxo0aJC6du3KiH0AAAAAco1Dg9QPP/ygpk2b2qYzrlvq2bOnFi1apFdeeUUXL15U3759de7cOTVq1EjffPONXF1dbc+JiorSoEGD1KxZMzk5OalTp06aOXNmnu8LAAAAgPuHQ4NUaGioDMO46XKLxaIJEyZowoQJN23j7e2tJUuW5EZ5AAAAAJClfHuNFAAAAADkVwQpAAAAADCJIAUAAAAAJhGkAAAAAMCkfHtDXthzSklydAkAkCP4eXbnOHYA7hX3ws8zglQ+5+XlpcIuVunXzY4uBQByTGEXq7y8vBxdRoHBdwGAe1FB/y6wGLcaf/w+kZycLC8vLyUlJcnT09PR5WQSHx+vpKSCn9qRv508eVKTJ0/W66+/rqCgIEeXg3ucl5eXfH19HV1GgcJ3AfIC3wXIS/n1uyC72YAeqQLA19c3X77JcG8KCgpS5cqVHV0GgBvwXYC8xHcBcHsMNgEAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwqZCjCwByWkpKimJjYx1dRoFz8uRJu3+RfYGBgXJ1dXV0GQAAIA8RpHDPiY2NVd++fR1dRoE1efJkR5dQ4MybN0+VK1d2dBkAACAPEaRwzwkMDNS8efMcXQbuI4GBgY4uAQAA5DGCFO45rq6u9A4AAAAgVzHYBAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAExisAkASktL0759+5SYmChvb2/VqlVLzs7Oji4LAAAg38r3PVLjxo2TxWKxe1StWtW2PCUlRQMHDlSJEiVUpEgRderUSfHx8Q6sGChYtmzZou7du2vo0KGaOHGihg4dqu7du2vLli2OLg0AACDfyvdBSpJq1KihM2fO2B7btm2zLRs6dKi+/vprLV++XJs3b9bp06fVsWNHB1YLFBxbtmzR2LFjVb58eUVGRmr16tWKjIxU+fLlNXbsWMIUAADATRSIU/sKFSokPz+/TPOTkpI0f/58LVmyRI899pgkaeHChapWrZp27typBg0a5HWpQIGRlpam2bNnKyQkRJMmTZKT099/V6lRo4YmTZqk0aNHa86cOWrYsCGn+QEAANygQPRI/fLLLypdurTKly+v7t27KzY2VpK0Z88eXb16Vc2bN7e1rVq1qgIDAxUdHX3T9aWmpio5OdnuAdxv9u3bp7i4OHXv3t0WojI4OTmpe/fuOnPmjPbt2+egCgEAAPKvfB+k6tevr0WLFumbb77RnDlzdOLECT366KM6f/684uLi5OLiomLFitk9x9fXV3FxcTddZ0REhLy8vGyPgICAXN4LIP9JTEyUJJUrVy7L5RnzM9oBAADgH/n+1L5WrVrZ/l+rVi3Vr19fQUFBWrZsmdzc3O5onaNGjdKwYcNs08nJyYQp3He8vb0lSSdOnFCNGjUyLT9x4oRdOwAAAPwj3/dI3ahYsWKqXLmyjh07Jj8/P125ckXnzp2zaxMfH5/lNVUZrFarPD097R7A/aZWrVry8/NTVFSU0tPT7Zalp6crKipK/v7+qlWrloMqBAAAyL8KXJC6cOGCjh8/Ln9/f9WtW1eFCxfW+vXrbcuPHDmi2NhYhYSEOLBKIP9zdnbWgAEDFB0drdGjR+vAgQO6dOmSDhw4oNGjRys6Olr9+/dnoAkAAIAs5PtT+4YPH6527dopKChIp0+f1tixY+Xs7Kxu3brJy8tLvXv31rBhw+Tt7S1PT0+9+OKLCgkJYcQ+IBsaN26s8ePHa/bs2Ro4cKBtvr+/v8aPH6/GjRs7sDoAAID8K98Hqd9++03dunXTn3/+KR8fHzVq1Eg7d+6Uj4+PJOm9996Tk5OTOnXqpNTUVIWFhWn27NkOrhooOBo3bqyGDRtq3759SkxMlLe3t2rVqkVPFAAAwC3k+yC1dOnSWy53dXVVZGSkIiMj86gi4N7j7OysOnXqOLoMAACAAqPAXSMFAAAAAI5GkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADApEKOLgCA440ePVrbtm2zTTdq1EiTJk1yYEUAAAD52z3TIxUZGang4GC5urqqfv362r17t6NLAgqE0NBQuxAlSdu2bVNoaKhjCgIAACgA7okg9emnn2rYsGEaO3asfvzxRz344IMKCwtTQkKCo0sD8rXbhSXCFAAAQNbuiSD17rvvqk+fPurVq5eqV6+uuXPnyt3dXQsWLHB0aUC+NXr0aNv/n3rqKW3atMn2eOqpp7JsBwAAgL8V+Gukrly5oj179mjUqFG2eU5OTmrevLmio6OzfE5qaqpSU1Nt08nJybleJ5DfXH8638CBA+2WDRw4UMuXL8/UDgAKkpSUFMXGxjq6jALl5MmTdv/CnMDAQLm6ujq6DOSRAh+k/vjjD6WlpcnX19duvq+vrw4fPpzlcyIiIjR+/Pi8KA8AADhIbGys+vbt6+gyCqTJkyc7uoQCad68eapcubKjy0AeKfBB6k6MGjVKw4YNs00nJycrICDAgRUBAICcFhgYqHnz5jm6DNxHAgMDHV0C8lCBD1IlS5aUs7Oz4uPj7ebHx8fLz88vy+dYrVZZrda8KA/Itxo1amQ7bS8yMtLu9L7IyEi7dgBQELm6utI7ACDXWAzDMBxdxN2qX7++Hn74Yc2aNUuSlJ6ersDAQA0aNEivvvrqbZ+fnJwsLy8vJSUlydPTM7fLBfKN7IzKt2nTplyvAwAAIL/Ibja4J0btGzZsmP79739r8eLFOnTokPr376+LFy+qV69eji4NyNduF5IIUQAAAFm7J4LU008/rWnTpumNN95Q7dq1tXfvXn3zzTeZBqAAkNmmTZsynb7XqFEjQhQAAMAt3BOn9t0tTu0DAAAAIN1np/YBAAAAQF4iSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADApEKOLiA/MAxDkpScnOzgSgAAAAA4UkYmyMgIN0OQknT+/HlJUkBAgIMrAQAAAJAfnD9/Xl5eXjddbjFuF7XuA+np6Tp9+rSKFi0qi8Xi6HIAh0hOTlZAQIBOnTolT09PR5cDAHAAvguAv3uizp8/r9KlS8vJ6eZXQtEjJcnJyUlly5Z1dBlAvuDp6cmXJwDc5/guwP3uVj1RGRhsAgAAAABMIkgBAAAAgEkEKQCSJKvVqrFjx8pqtTq6FACAg/BdAGQfg00AAAAAgEn0SAEAAACASQQpAAAAADCJIAUAAAAAJhGkAOSI8PBwdejQwdFlAAAKsODgYE2fPt3RZQDZQpACCrjw8HBZLJZMj2PHjjm6NACASRk/09966y27+V988YUsFoupdWU3lAQHB2f6DilbtqypbQH3I4IUcA9o2bKlzpw5Y/coV66cXZsrV644qDoAgBmurq6aMmWK/vrrrzzb5oQJE+y+Q3766acs2129ejXPagLyO4IUcA+wWq3y8/OzezRr1kyDBg3SkCFDVLJkSYWFhUmS3n33XdWsWVMeHh4KCAjQgAEDdOHCBdu6xo0bp9q1a9utf/r06QoODrZNp6WladiwYSpWrJhKlCihV155RdxJAQByRvPmzeXn56eIiIhbtvvss89Uo0YNWa1WBQcH65133rEtCw0N1cmTJzV06FBbL9OtFC1a1O47xMfHR5JksVg0Z84ctW/fXh4eHpo8ebLS0tLUu3dvlStXTm5ubqpSpYpmzJhht77Q0FANGTLEbl6HDh0UHh5um05ISFC7du3k5uamcuXKKSoqKhtHB8g/CFLAPWzx4sVycXHR9u3bNXfuXEmSk5OTZs6cqQMHDmjx4sXasGGDXnnlFVPrfeedd7Ro0SItWLBA27ZtU2JiolauXJkbuwAA9x1nZ2e9+eabmjVrln777bcs2+zZs0ddunRR165dtX//fo0bN05jxozRokWLJEmff/65ypYta9fTdKfGjRunJ598Uvv379dzzz2n9PR0lS1bVsuXL9fBgwf1xhtv6LXXXtOyZctMrTc8PFynTp3Sxo0btWLFCs2ePVsJCQl3XCeQ1wo5ugAAd2/VqlUqUqSIbbpVq1aSpEqVKmnq1Kl2ba//C2FwcLAmTZqkF154QbNnz8729qZPn65Ro0apY8eOkqS5c+dq7dq1d7EHAIDrPfnkk6pdu7bGjh2r+fPnZ1r+7rvvqlmzZhozZowkqXLlyjp48KDefvtthYeHy9vbW87OzraeptsZOXKkRo8ebZt+8803NXjwYEnSM888o169etm1Hz9+vO3/5cqVU3R0tJYtW6YuXbpka/+OHj2qNWvWaPfu3XrooYckSfPnz1e1atWy9XwgPyBIAfeApk2bas6cObZpDw8PdevWTXXr1s3U9rvvvlNERIQOHz6s5ORkXbt2TSkpKbp06ZLc3d1vu62kpCSdOXNG9evXt80rVKiQ6tWrx+l9AJCDpkyZoscee0zDhw/PtOzQoUN64okn7OY1bNhQ06dPV1pampydnU1ta8SIEXan3ZUsWdL2/3r16mVqHxkZqQULFig2NlaXL1/WlStXMp0WfiuHDh1SoUKF7L6nqlatqmLFipmqG3AkTu0D7gEeHh6qWLGi7eHv72+bf72YmBi1bdtWtWrV0meffaY9e/YoMjJS0j+DUTg5OWUKRFxcDAB5r3HjxgoLC9OoUaNyfVslS5a0+x65PtDc+F2ydOlSDR8+XL1799a3336rvXv3qlevXnaDGvFdgvsBQQq4j+zZs0fp6el655131KBBA1WuXFmnT5+2a+Pj46O4uDi7L8C9e/fa/u/l5SV/f3/t2rXLNu/atWvas2dPrtcPAPebt956S19//bWio6Pt5lerVk3bt2+3m7d9+3ZVrlzZ1hvl4uKitLS0HK9p+/bteuSRRzRgwADVqVNHFStW1PHjx+3a+Pj42F2XlZaWpp9//tk2XbVq1UzfHUeOHNG5c+dyvF4gtxCkgPtIxYoVdfXqVc2aNUu//vqrPvroI9sgFBlCQ0N19uxZTZ06VcePH1dkZKTWrFlj1+all17SW2+9pS+++EKHDx/WgAED+PIDgFxQs2ZNde/eXTNnzrSb//LLL2v9+vWaOHGijh49qsWLF+v999+3Ow0wODhYW7Zs0e+//64//vgjx2qqVKmSfvjhB61du1ZHjx7VmDFj9P3339u1eeyxx/Tf//5X//3vf3X48GH179/f7nuiSpUqatmypfr166ddu3Zpz549ev755+Xm5pZjdQK5jSAF3EcefPBBvfvuu5oyZYoeeOABRUVFZRpet1q1apo9e7YiIyP14IMPavfu3ZnOz3/55Zf17LPPqmfPngoJCVHRokX15JNP5uWuAMB9Y8KECUpPT7eb969//UvLli3T0qVL9cADD+iNN97QhAkT7K5zmjBhgmJiYlShQgXbcOY5oV+/furYsaOefvpp1a9fX3/++acGDBhg1+a5555Tz5491aNHDzVp0kTly5dX06ZN7dosXLhQpUuXVpMmTdSxY0f17dtXpUqVyrE6gdxmMbg6HAAAAABMoUcKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgBwzwgPD1eHDh3yfDuhoaEaMmRIrm8XAJB/EKQAAHclPDxcFosl0+PYsWOOLi2TTZs2yWKx6Ny5czm63s8//1wTJ07MsfXlVSAEANy5Qo4uAABQ8LVs2VILFy60m+fj45Op3ZUrV+Ti4pJXZeUZb29vR5eQpXv1eANAfkCPFADgrlmtVvn5+dk9nJ2dFRoaqkGDBmnIkCEqWbKkwsLCJEnvvvuuatasKQ8PDwUEBGjAgAG6cOGCbX3jxo1T7dq17bYxffp0BQcH26bT0tI0bNgwFStWTCVKlNArr7wiwzBM1b1o0SIVK1ZMa9euVbVq1VSkSBG1bNlSZ86cMbWdG0/tS01N1ciRIxUQECCr1aqKFStq/vz5tvX17t1b5cqVk5ubm6pUqaIZM2bY7fvixYv15Zdf2nr3Nm3aJEnav3+/HnvsMbm5ualEiRLq27ev3XHL6MmaPHmySpcurSpVqpg6HgCA7CNIAQBy1eLFi+Xi4qLt27dr7ty5kiQnJyfNnDlTBw4c0OLFi7Vhwwa98sorptb7zjvvaNGiRVqwYIG2bdumxMRErVy50nR9ly5d0rRp0/TRRx9py5Ytio2N1fDhw+9qOz169NAnn3yimTNn6tChQ/rggw9UpEgRSVJ6errKli2r5cuX6+DBg3rjjTf02muvadmyZZKk4cOHq0uXLrZAd+bMGT3yyCO6ePGiwsLCVLx4cX3//fdavny5vvvuOw0aNMhu2+vXr9eRI0e0bt06rVq1yvTxAABkD6f2AQDu2qpVq2xBQZJatWql5cuXS5IqVaqkqVOn2rW/vvcmODhYkyZN0gsvvKDZs2dne5vTp0/XqFGj1LFjR0nS3LlztXbtWtO1X716VXPnzlWFChUkSYMGDdKECRPueDtHjx7VsmXLtG7dOjVv3lySVL58edvywoULa/z48bbpcuXKKTo6WsuWLVOXLl1UpEgRubm5KTU1VX5+frZ2ixcvVkpKij788EN5eHhIkt5//321a9dOU6ZMka+vryTJw8ND//nPfzilDwByGUEKAHDXmjZtqjlz5timM37Rl6S6detmav/dd98pIiJChw8fVnJysq5du6aUlBRdunRJ7u7ut91eUlKSzpw5o/r169vmFSpUSPXq1TN9ep+7u7stREmSv7+/EhIS7ng7e/fulbOzs5o0aXLTbUZGRmrBggWKjY3V5cuXdeXKlUynMt7o0KFDevDBB+2ObcOGDZWenq4jR47YglTNmjUJUQCQBzi1DwBw1zw8PFSxYkXbw9/f327Z9WJiYtS2bVvVqlVLn332mfbs2aPIyEhJfw+OIP196t+NQeXq1au5UnvhwoXtpi0Wi+kwdj03N7dbLl+6dKmGDx+u3r1769tvv9XevXvVq1cv277frRuPNwAgdxCkAAB5as+ePUpPT9c777yjBg0aqHLlyjp9+rRdGx8fH8XFxdkFmr1799r+7+XlJX9/f+3atcs279q1a9qzZ0+O1non26lZs6bS09O1efPmLJdv375djzzyiAYMGKA6deqoYsWKOn78uF0bFxcXpaWl2c2rVq2a/ve//+nixYt263JycmJQCQBwAIIUACBPVaxYUVevXtWsWbP066+/6qOPPrINQpEhNDRUZ8+e1dSpU3X8+HFFRkZqzZo1dm1eeuklvfXWW/riiy90+PBhDRgwIMfvD3Un2wkODlbPnj313HPP6YsvvtCJEye0adMm22ASlSpV0g8//KC1a9fq6NGjGjNmjL7//vtM69i3b5+OHDmiP/74Q1evXlX37t3l6uqqnj176ueff9bGjRv14osv6tlnn7Wd1gcAyDsEKQBAnnrwwQf17rvvasqUKXrggQcUFRWliIgIuzbVqlXT7NmzFRkZqQcffFC7d++2G0lPkl5++WU9++yz6tmzp0JCQlS0aFE9+eSTOV7vnWxnzpw56ty5swYMGKCqVauqT58+tp6kfv36qWPHjnr66adVv359/fnnnxowYIDd8/v06aMqVaqoXr168vHx0fbt2+Xu7q61a9cqMTFRDz30kDp37qxmzZrp/fffz/F9BgDcnsW4mxPBAQAAAOA+RI8UAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABg0v8DMK4QgNzwSBkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualization\n",
        "from scipy.stats import ttest_ind\n",
        "\n",
        " #T-test\n",
        "fraudulent_amount = data[data['Fraud_indicator'] == 'Fraud']['Transaction_Amount']\n",
        "non_fraudulent_amount = data[data['Fraud_indicator'] == 'Not Fraud']['Transaction_Amount']\n",
        "t_statistic, p_value = ttest_ind(fraudulent_amount, non_fraudulent_amount)\n",
        "print(\"T-test statistic:\", t_statistic)\n",
        "print(\"p-value:\", p_value)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x='Fraud_indicator', y='Transaction_Amount', data=data)\n",
        "plt.title('Relationship between Transaction_Amount and Fraud Indicator')\n",
        "plt.xlabel('Fraud Indicator')\n",
        "plt.ylabel('Transaction_Amount')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "uF9EWHl-kbmp",
        "outputId": "64149fff-9b70-40cc-c644-e322e84dbc1a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "T-test statistic: 10.21145475752237\n",
            "p-value: 3.0439767702089614e-24\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#important features\n",
        "data[['Vehicle_Type', 'Lane_Type', 'Vehicle_Dimensions',\n",
        "      'Transaction_Amount', 'Amount_paid', 'Fraud_indicator']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "elkMGMLjruLb",
        "outputId": "a8f8893f-ecdd-49ab-d9e1-17c47f0108be"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Vehicle_Type Lane_Type Vehicle_Dimensions  Transaction_Amount  \\\n",
              "0            Bus    Express              Large                 350   \n",
              "1             Car   Regular              Small                 120   \n",
              "2      Motorcycle   Regular              Small                   0   \n",
              "3           Truck   Regular              Large                 350   \n",
              "4             Van   Express             Medium                 140   \n",
              "...           ...       ...                ...                 ...   \n",
              "4995        Truck   Regular              Large                 330   \n",
              "4996          Van   Express             Medium                 125   \n",
              "4997        Sedan   Regular             Medium                 115   \n",
              "4998          SUV   Express              Large                 145   \n",
              "4999         Bus    Regular              Large                 330   \n",
              "\n",
              "      Amount_paid Fraud_indicator  \n",
              "0             120           Fraud  \n",
              "1             100           Fraud  \n",
              "2               0       Not Fraud  \n",
              "3             120           Fraud  \n",
              "4             100           Fraud  \n",
              "...           ...             ...  \n",
              "4995          330       Not Fraud  \n",
              "4996          125       Not Fraud  \n",
              "4997          115       Not Fraud  \n",
              "4998          145       Not Fraud  \n",
              "4999          125           Fraud  \n",
              "\n",
              "[5000 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c2534694-f531-420a-9935-2d4dbd744c4a\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Vehicle_Type</th>\n",
              "      <th>Lane_Type</th>\n",
              "      <th>Vehicle_Dimensions</th>\n",
              "      <th>Transaction_Amount</th>\n",
              "      <th>Amount_paid</th>\n",
              "      <th>Fraud_indicator</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bus</td>\n",
              "      <td>Express</td>\n",
              "      <td>Large</td>\n",
              "      <td>350</td>\n",
              "      <td>120</td>\n",
              "      <td>Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Car</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Small</td>\n",
              "      <td>120</td>\n",
              "      <td>100</td>\n",
              "      <td>Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Motorcycle</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Small</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Truck</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Large</td>\n",
              "      <td>350</td>\n",
              "      <td>120</td>\n",
              "      <td>Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Van</td>\n",
              "      <td>Express</td>\n",
              "      <td>Medium</td>\n",
              "      <td>140</td>\n",
              "      <td>100</td>\n",
              "      <td>Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>Truck</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Large</td>\n",
              "      <td>330</td>\n",
              "      <td>330</td>\n",
              "      <td>Not Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4996</th>\n",
              "      <td>Van</td>\n",
              "      <td>Express</td>\n",
              "      <td>Medium</td>\n",
              "      <td>125</td>\n",
              "      <td>125</td>\n",
              "      <td>Not Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4997</th>\n",
              "      <td>Sedan</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Medium</td>\n",
              "      <td>115</td>\n",
              "      <td>115</td>\n",
              "      <td>Not Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4998</th>\n",
              "      <td>SUV</td>\n",
              "      <td>Express</td>\n",
              "      <td>Large</td>\n",
              "      <td>145</td>\n",
              "      <td>145</td>\n",
              "      <td>Not Fraud</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4999</th>\n",
              "      <td>Bus</td>\n",
              "      <td>Regular</td>\n",
              "      <td>Large</td>\n",
              "      <td>330</td>\n",
              "      <td>125</td>\n",
              "      <td>Fraud</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 6 columns</p>\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c2534694-f531-420a-9935-2d4dbd744c4a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c2534694-f531-420a-9935-2d4dbd744c4a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b4252fc1-41ea-448b-bdc0-c7268270b14c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b4252fc1-41ea-448b-bdc0-c7268270b14c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b4252fc1-41ea-448b-bdc0-c7268270b14c 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\": \"      'Transaction_Amount', 'Amount_paid', 'Fraud_indicator']]\",\n  \"rows\": 5000,\n  \"fields\": [\n    {\n      \"column\": \"Vehicle_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Bus \",\n          \"Car\",\n          \"Sedan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lane_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Regular\",\n          \"Express\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vehicle_Dimensions\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Large\",\n          \"Small\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transaction_Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112,\n        \"min\": 0,\n        \"max\": 350,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          350,\n          330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount_paid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106,\n        \"min\": 0,\n        \"max\": 350,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          340,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fraud_indicator\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Not Fraud\",\n          \"Fraud\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model training and Pipeline"
      ],
      "metadata": {
        "id": "ZglKnx-zBPyp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define column transformer for preprocessing\n",
        "preprocessor = ColumnTransformer([\n",
        "    ('categorical_onehot', OneHotEncoder(), ['Vehicle_Type', 'Lane_Type']),\n",
        "    ('ordinal_encoding', OrdinalEncoder(categories=[['Small', 'Medium', 'Large']]), ['Vehicle_Dimensions']),\n",
        "    ('numeric_scaling', StandardScaler(), ['Transaction_Amount', 'Amount_paid'])\n",
        "])\n",
        "\n",
        "# Create Random Forest Classifier\n",
        "rf_classifier = RandomForestClassifier(random_state=42)\n",
        "\n",
        "# Create pipeline\n",
        "pipeline = Pipeline([\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('classifier', rf_classifier)\n",
        "])\n",
        "\n",
        "# Define parameter grid for GridSearchCV\n",
        "param_grid = {\n",
        "    'classifier__n_estimators': [100, 200, 300],\n",
        "    'classifier__max_depth': [None, 10, 20],\n",
        "    'classifier__min_samples_split': [2, 5, 10],\n",
        "    'classifier__min_samples_leaf': [1, 2, 4],\n",
        "    'classifier__bootstrap': [True, False]\n",
        "}\n",
        "\n",
        "# Create GridSearchCV\n",
        "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy')\n",
        "\n",
        "# Assuming 'X_train' is your feature matrix and 'y_train' is your target variable\n",
        "grid_search.fit(data[['Vehicle_Type', 'Lane_Type', 'Vehicle_Dimensions',\n",
        "      'Transaction_Amount', 'Amount_paid']], data['Fraud_indicator'])\n",
        "\n",
        "# Get the best parameters and best score\n",
        "print(\"Best Parameters:\", grid_search.best_params_)\n",
        "print(\"Best Accuracy:\", grid_search.best_score_)\n",
        "\n",
        "# Optionally, you can also retrieve the best model from grid search\n",
        "best_model = grid_search.best_estimator_\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uS8H0BsuwjFj",
        "outputId": "17686d34-a388-4098-8987-4c30e28df18d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'classifier__bootstrap': False, 'classifier__max_depth': None, 'classifier__min_samples_leaf': 1, 'classifier__min_samples_split': 2, 'classifier__n_estimators': 100}\n",
            "Best Accuracy: 0.9968\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data[['Vehicle_Type', 'Lane_Type', 'Vehicle_Dimensions',\n",
        "      'Transaction_Amount', 'Amount_paid']],  data['Fraud_indicator'], test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "GX4SSfqX8VM0"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessor.fit(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        },
        "id": "4ZxiLnXC99Gb",
        "outputId": "02327d07-b81e-49b0-aa12-477aba1aa69d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(transformers=[('categorical_onehot', OneHotEncoder(),\n",
              "                                 ['Vehicle_Type', 'Lane_Type']),\n",
              "                                ('ordinal_encoding',\n",
              "                                 OrdinalEncoder(categories=[['Small', 'Medium',\n",
              "                                                             'Large']]),\n",
              "                                 ['Vehicle_Dimensions']),\n",
              "                                ('numeric_scaling', StandardScaler(),\n",
              "                                 ['Transaction_Amount', 'Amount_paid'])])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#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>ColumnTransformer(transformers=[(&#x27;categorical_onehot&#x27;, OneHotEncoder(),\n",
              "                                 [&#x27;Vehicle_Type&#x27;, &#x27;Lane_Type&#x27;]),\n",
              "                                (&#x27;ordinal_encoding&#x27;,\n",
              "                                 OrdinalEncoder(categories=[[&#x27;Small&#x27;, &#x27;Medium&#x27;,\n",
              "                                                             &#x27;Large&#x27;]]),\n",
              "                                 [&#x27;Vehicle_Dimensions&#x27;]),\n",
              "                                (&#x27;numeric_scaling&#x27;, StandardScaler(),\n",
              "                                 [&#x27;Transaction_Amount&#x27;, &#x27;Amount_paid&#x27;])])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;categorical_onehot&#x27;, OneHotEncoder(),\n",
              "                                 [&#x27;Vehicle_Type&#x27;, &#x27;Lane_Type&#x27;]),\n",
              "                                (&#x27;ordinal_encoding&#x27;,\n",
              "                                 OrdinalEncoder(categories=[[&#x27;Small&#x27;, &#x27;Medium&#x27;,\n",
              "                                                             &#x27;Large&#x27;]]),\n",
              "                                 [&#x27;Vehicle_Dimensions&#x27;]),\n",
              "                                (&#x27;numeric_scaling&#x27;, StandardScaler(),\n",
              "                                 [&#x27;Transaction_Amount&#x27;, &#x27;Amount_paid&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">categorical_onehot</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Vehicle_Type&#x27;, &#x27;Lane_Type&#x27;]</pre></div></div></div><div class=\"sk-serial\"><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\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ordinal_encoding</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Vehicle_Dimensions&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;Small&#x27;, &#x27;Medium&#x27;, &#x27;Large&#x27;]])</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numeric_scaling</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Transaction_Amount&#x27;, &#x27;Amount_paid&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preprocessor.fit_transform(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9TTP64Z4-zBx",
        "outputId": "5d956357-c6ea-48a4-8223-53b1b4614f8f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.        ,  0.        ,  0.        , ...,  2.        ,\n",
              "         1.18508016,  1.46490674],\n",
              "       [ 0.        ,  0.        ,  1.        , ...,  0.        ,\n",
              "        -1.42897114, -1.3133966 ],\n",
              "       [ 0.        ,  0.        ,  0.        , ...,  2.        ,\n",
              "         0.13945964,  0.3535854 ],\n",
              "       ...,\n",
              "       [ 1.        ,  0.        ,  0.        , ...,  2.        ,\n",
              "         1.09794511,  1.37229663],\n",
              "       [ 0.        ,  1.        ,  0.        , ...,  0.        ,\n",
              "        -0.38335062, -0.20207526],\n",
              "       [ 0.        ,  1.        ,  0.        , ...,  0.        ,\n",
              "        -0.81902584, -0.57251571]])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf = RandomForestClassifier(n_estimators=100, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, random_state=42)"
      ],
      "metadata": {
        "id": "qcYDejS4qmOT"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(preprocessor.fit_transform(X_train), y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "7hJ_2_ZX7Tvj",
        "outputId": "f6f9e614-4491-4cb1-ba2f-2d4c369928ca"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#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(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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(preprocessor.fit_transform(X_train))"
      ],
      "metadata": {
        "id": "gLSXeeF3_jW6"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XmiKJQXx_3u4",
        "outputId": "97ddb27b-4e19-49ca-b8ec-2be10f21b357"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1501    Not Fraud\n",
              "2586    Not Fraud\n",
              "2653    Not Fraud\n",
              "1055    Not Fraud\n",
              "705     Not Fraud\n",
              "          ...    \n",
              "4711    Not Fraud\n",
              "2313    Not Fraud\n",
              "3214    Not Fraud\n",
              "2732    Not Fraud\n",
              "1926        Fraud\n",
              "Name: Fraud_indicator, Length: 1000, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Metrics"
      ],
      "metadata": {
        "id": "lA5PMfhQBiH5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "\n",
        "y_pred = best_model.predict(X_test)\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "\n",
        "# Calculate precision\n",
        "precision = precision_score(y_test, y_pred, pos_label='Fraud')\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "# Calculate recall\n",
        "recall = recall_score(y_test, y_pred, pos_label='Fraud')\n",
        "print(\"Recall:\", recall)\n",
        "\n",
        "# Calculate F1 score\n",
        "f1 = f1_score(y_test, y_pred, pos_label='Fraud')\n",
        "print(\"F1 Score:\", f1)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5p9cYtYNOMK5",
        "outputId": "b0368543-496e-405e-9bdf-f3db9d603591"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n",
            "Precision: 1.0\n",
            "Recall: 1.0\n",
            "F1 Score: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the distribution of fraud indicators\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.countplot(x='Fraud_indicator', data=data)\n",
        "plt.title('Distribution of Fraud Indicators')\n",
        "plt.xlabel('Fraud Indicator')\n",
        "plt.ylabel('Count')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "3eM6s3ExOk48",
        "outputId": "1a19eb8c-c0c3-499c-8dff-99eaca7ebc6e"
      },
      "execution_count": 57,
      "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": [
        "# Classification report and confusion matrix\n",
        "print(classification_report(y_test, y_pred))\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(conf_matrix, annot=True, cmap=\"Blues\")\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": 628
        },
        "id": "r2YtdcQEGJE0",
        "outputId": "5a546edf-c309-4b27-a298-5fbb6f26f982"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Fraud       1.00      1.00      1.00       217\n",
            "   Not Fraud       1.00      1.00      1.00       783\n",
            "\n",
            "    accuracy                           1.00      1000\n",
            "   macro avg       1.00      1.00      1.00      1000\n",
            "weighted avg       1.00      1.00      1.00      1000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}