sample code implementation

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":7786909,"sourceType":"datasetVersion","datasetId":4557717}],"dockerImageVersionId":30664,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-09-30T17:27:46.256721Z","iopub.execute_input":"2024-09-30T17:27:46.257658Z","iopub.status.idle":"2024-09-30T17:27:47.355159Z","shell.execute_reply.started":"2024-09-30T17:27:46.257621Z","shell.execute_reply":"2024-09-30T17:27:47.354219Z"},"trusted":true},"outputs":[],"execution_count":1},{"cell_type":"code","source":"fin_fraud_data = pd.read_csv('/kaggle/input/financial-fraud-detection-dataset/Synthetic_Financial_datasets_log.csv')\nfin_fraud_copy = fin_fraud_data.copy()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:27:47.357169Z","iopub.execute_input":"2024-09-30T17:27:47.357741Z","iopub.status.idle":"2024-09-30T17:28:07.410812Z","shell.execute_reply.started":"2024-09-30T17:27:47.357703Z","shell.execute_reply":"2024-09-30T17:28:07.409801Z"},"trusted":true},"outputs":[],"execution_count":2},{"cell_type":"code","source":"fin_fraud_copy.head()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:07.412062Z","iopub.execute_input":"2024-09-30T17:28:07.412368Z","iopub.status.idle":"2024-09-30T17:28:07.436695Z","shell.execute_reply.started":"2024-09-30T17:28:07.412342Z","shell.execute_reply":"2024-09-30T17:28:07.435679Z"},"trusted":true},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n\n      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n0  M1979787155             0.0             0.0        0               0  \n1  M2044282225             0.0             0.0        0               0  \n2   C553264065             0.0             0.0        1               0  \n3    C38997010         21182.0             0.0        1               0  \n4  M1230701703             0.0             0.0        0               0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>step</th>\n      <th>type</th>\n      <th>amount</th>\n      <th>nameOrig</th>\n      <th>oldbalanceOrg</th>\n      <th>newbalanceOrig</th>\n      <th>nameDest</th>\n      <th>oldbalanceDest</th>\n      <th>newbalanceDest</th>\n      <th>isFraud</th>\n      <th>isFlaggedFraud</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>9839.64</td>\n      <td>C1231006815</td>\n      <td>170136.0</td>\n      <td>160296.36</td>\n      <td>M1979787155</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>1864.28</td>\n      <td>C1666544295</td>\n      <td>21249.0</td>\n      <td>19384.72</td>\n      <td>M2044282225</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>TRANSFER</td>\n      <td>181.00</td>\n      <td>C1305486145</td>\n      <td>181.0</td>\n      <td>0.00</td>\n      <td>C553264065</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>CASH_OUT</td>\n      <td>181.00</td>\n      <td>C840083671</td>\n      <td>181.0</td>\n      <td>0.00</td>\n      <td>C38997010</td>\n      <td>21182.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>11668.14</td>\n      <td>C2048537720</td>\n      <td>41554.0</td>\n      <td>29885.86</td>\n      <td>M1230701703</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"fin_fraud_copy.info()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:07.439550Z","iopub.execute_input":"2024-09-30T17:28:07.440501Z","iopub.status.idle":"2024-09-30T17:28:07.465323Z","shell.execute_reply.started":"2024-09-30T17:28:07.440461Z","shell.execute_reply":"2024-09-30T17:28:07.464286Z"},"trusted":true},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 6362620 entries, 0 to 6362619\nData columns (total 11 columns):\n #   Column          Dtype  \n---  ------          -----  \n 0   step            int64  \n 1   type            object \n 2   amount          float64\n 3   nameOrig        object \n 4   oldbalanceOrg   float64\n 5   newbalanceOrig  float64\n 6   nameDest        object \n 7   oldbalanceDest  float64\n 8   newbalanceDest  float64\n 9   isFraud         int64  \n 10  isFlaggedFraud  int64  \ndtypes: float64(5), int64(3), object(3)\nmemory usage: 534.0+ MB\n","output_type":"stream"}],"execution_count":4},{"cell_type":"code","source":"selected_columns = ['step', 'type', 'amount', 'nameOrig', 'oldbalanceOrg', 'newbalanceOrig', 'nameDest', 'oldbalanceDest', 'newbalanceDest','isFraud']\ndf = fin_fraud_copy[selected_columns]\ndf.info()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:07.466801Z","iopub.execute_input":"2024-09-30T17:28:07.467639Z","iopub.status.idle":"2024-09-30T17:28:07.854350Z","shell.execute_reply.started":"2024-09-30T17:28:07.467601Z","shell.execute_reply":"2024-09-30T17:28:07.853304Z"},"trusted":true},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 6362620 entries, 0 to 6362619\nData columns (total 10 columns):\n #   Column          Dtype  \n---  ------          -----  \n 0   step            int64  \n 1   type            object \n 2   amount          float64\n 3   nameOrig        object \n 4   oldbalanceOrg   float64\n 5   newbalanceOrig  float64\n 6   nameDest        object \n 7   oldbalanceDest  float64\n 8   newbalanceDest  float64\n 9   isFraud         int64  \ndtypes: float64(5), int64(2), object(3)\nmemory usage: 485.4+ MB\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"# Calculate the differences between originating and destination balances\ndf.loc[:, 'orgDiff'] = df['newbalanceOrig'] - df['oldbalanceOrg']\ndf.loc[:, 'destDiff'] = df['newbalanceDest'] - df['oldbalanceDest']","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:07.855628Z","iopub.execute_input":"2024-09-30T17:28:07.855945Z","iopub.status.idle":"2024-09-30T17:28:07.930886Z","shell.execute_reply.started":"2024-09-30T17:28:07.855912Z","shell.execute_reply":"2024-09-30T17:28:07.929804Z"},"trusted":true},"outputs":[],"execution_count":6},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:07.932129Z","iopub.execute_input":"2024-09-30T17:28:07.932484Z","iopub.status.idle":"2024-09-30T17:28:07.950992Z","shell.execute_reply.started":"2024-09-30T17:28:07.932454Z","shell.execute_reply":"2024-09-30T17:28:07.949904Z"},"trusted":true},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n\n      nameDest  oldbalanceDest  newbalanceDest  isFraud   orgDiff  destDiff  \n0  M1979787155             0.0             0.0        0  -9839.64       0.0  \n1  M2044282225             0.0             0.0        0  -1864.28       0.0  \n2   C553264065             0.0             0.0        1   -181.00       0.0  \n3    C38997010         21182.0             0.0        1   -181.00  -21182.0  \n4  M1230701703             0.0             0.0        0 -11668.14       0.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>step</th>\n      <th>type</th>\n      <th>amount</th>\n      <th>nameOrig</th>\n      <th>oldbalanceOrg</th>\n      <th>newbalanceOrig</th>\n      <th>nameDest</th>\n      <th>oldbalanceDest</th>\n      <th>newbalanceDest</th>\n      <th>isFraud</th>\n      <th>orgDiff</th>\n      <th>destDiff</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>9839.64</td>\n      <td>C1231006815</td>\n      <td>170136.0</td>\n      <td>160296.36</td>\n      <td>M1979787155</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>-9839.64</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>1864.28</td>\n      <td>C1666544295</td>\n      <td>21249.0</td>\n      <td>19384.72</td>\n      <td>M2044282225</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>-1864.28</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>TRANSFER</td>\n      <td>181.00</td>\n      <td>C1305486145</td>\n      <td>181.0</td>\n      <td>0.00</td>\n      <td>C553264065</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>-181.00</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>CASH_OUT</td>\n      <td>181.00</td>\n      <td>C840083671</td>\n      <td>181.0</td>\n      <td>0.00</td>\n      <td>C38997010</td>\n      <td>21182.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>-181.00</td>\n      <td>-21182.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>11668.14</td>\n      <td>C2048537720</td>\n      <td>41554.0</td>\n      <td>29885.86</td>\n      <td>M1230701703</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>-11668.14</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"data1 = df.groupby('type').size().reset_index(name='count')","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:07.952443Z","iopub.execute_input":"2024-09-30T17:28:07.952768Z","iopub.status.idle":"2024-09-30T17:28:08.542114Z","shell.execute_reply.started":"2024-09-30T17:28:07.952740Z","shell.execute_reply":"2024-09-30T17:28:08.541077Z"},"trusted":true},"outputs":[],"execution_count":8},{"cell_type":"code","source":"df_trans_type = data1.copy()\ndf_trans_type.rename(columns={'type':'transactions', 'count':'trans_total'}, inplace=True)\ndf_trans_type.head(5)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:08.543496Z","iopub.execute_input":"2024-09-30T17:28:08.543883Z","iopub.status.idle":"2024-09-30T17:28:08.556438Z","shell.execute_reply.started":"2024-09-30T17:28:08.543849Z","shell.execute_reply":"2024-09-30T17:28:08.555457Z"},"trusted":true},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"  transactions  trans_total\n0      CASH_IN      1399284\n1     CASH_OUT      2237500\n2        DEBIT        41432\n3      PAYMENT      2151495\n4     TRANSFER       532909","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>transactions</th>\n      <th>trans_total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CASH_IN</td>\n      <td>1399284</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CASH_OUT</td>\n      <td>2237500</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>DEBIT</td>\n      <td>41432</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>PAYMENT</td>\n      <td>2151495</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>TRANSFER</td>\n      <td>532909</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"labels = df_trans_type.transactions\nvolume = df_trans_type.trans_total\nexplode = (0.2, 0.1, 0.1, 0.1, 0.1) # 5 volume to be exploded\n#pie\nplt.pie(volume, labels=labels, startangle=90,\n        shadow=True, autopct='%1.2f%%')\n#title\nplt.title('Type of Transactions')\nplt.axis('equal')","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:08.560325Z","iopub.execute_input":"2024-09-30T17:28:08.560700Z","iopub.status.idle":"2024-09-30T17:28:08.808799Z","shell.execute_reply.started":"2024-09-30T17:28:08.560667Z","shell.execute_reply":"2024-09-30T17:28:08.807473Z"},"trusted":true},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"(-1.0999973225351762,\n 1.0999998663212909,\n -1.0999991922384087,\n 1.0999999615351623)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"data2 = df.groupby('type')['amount'].sum().reset_index(name='total_amount')","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:08.810603Z","iopub.execute_input":"2024-09-30T17:28:08.811462Z","iopub.status.idle":"2024-09-30T17:28:09.425120Z","shell.execute_reply.started":"2024-09-30T17:28:08.811417Z","shell.execute_reply":"2024-09-30T17:28:09.424052Z"},"trusted":true},"outputs":[],"execution_count":11},{"cell_type":"code","source":"from math import log, floor\n\ndef number_format(number):\n    #function format number to B\n    units = 'B'\n    k = 1000000000.0\n    #m = int(floor(log(number, k)))\n    return '%.3f' % (number / k)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:09.426475Z","iopub.execute_input":"2024-09-30T17:28:09.426796Z","iopub.status.idle":"2024-09-30T17:28:09.432353Z","shell.execute_reply.started":"2024-09-30T17:28:09.426768Z","shell.execute_reply":"2024-09-30T17:28:09.431188Z"},"trusted":true},"outputs":[],"execution_count":12},{"cell_type":"code","source":"df_total_money = data2.copy()\ndf_total_money.rename(columns={'type':'transactions', 'total_amount':'money_total_in_Billion'}, inplace=True)\n#format to Billion\ndf_total_money['money_total_in_Billion'] = \\\n            df_total_money['money_total_in_Billion'].apply(lambda x: number_format(x))\n## format to float\ndf_total_money['money_total_in_Billion'] = \\\n            df_total_money['money_total_in_Billion'].apply(lambda x: float(x))","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:09.434102Z","iopub.execute_input":"2024-09-30T17:28:09.434422Z","iopub.status.idle":"2024-09-30T17:28:09.444787Z","shell.execute_reply.started":"2024-09-30T17:28:09.434375Z","shell.execute_reply":"2024-09-30T17:28:09.443864Z"},"trusted":true},"outputs":[],"execution_count":13},{"cell_type":"code","source":"#sort the data\ndf_total_money = df_total_money.sort_values(by='money_total_in_Billion')","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:09.445982Z","iopub.execute_input":"2024-09-30T17:28:09.446269Z","iopub.status.idle":"2024-09-30T17:28:09.455442Z","shell.execute_reply.started":"2024-09-30T17:28:09.446244Z","shell.execute_reply":"2024-09-30T17:28:09.454546Z"},"trusted":true},"outputs":[],"execution_count":14},{"cell_type":"code","source":"labels = df_total_money.transactions\ny_pos = np.arange(len(labels))\nvolume = df_total_money.money_total_in_Billion\n#bar chart\nfig, ax = plt.subplots()\n\nhbars = ax.barh(y_pos, volume, align='center')\nax.set_yticks(y_pos, labels=labels)\nax.invert_yaxis()\nax.set_xlabel('total money (Billion) each type trans')\nax.set_title('Total money (Billion) of each type of trans')\n\n#Label with specially formatted floats\nax.bar_label(ax.containers[0])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:09.456798Z","iopub.execute_input":"2024-09-30T17:28:09.457187Z","iopub.status.idle":"2024-09-30T17:28:09.696807Z","shell.execute_reply.started":"2024-09-30T17:28:09.457151Z","shell.execute_reply":"2024-09-30T17:28:09.695649Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"def number_format_M(number):\n    #function format number to Million\n    units = 'B'\n    k = 1000000.0\n    return '%.3f' % (number / k)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:09.698117Z","iopub.execute_input":"2024-09-30T17:28:09.698454Z","iopub.status.idle":"2024-09-30T17:28:09.703557Z","shell.execute_reply.started":"2024-09-30T17:28:09.698425Z","shell.execute_reply":"2024-09-30T17:28:09.702338Z"},"trusted":true},"outputs":[],"execution_count":16},{"cell_type":"code","source":"df_money_recd_each_destacc = df.groupby('nameDest').agg(\n    count_trans=('amount', 'size'),  # 'size' gives the count of rows (including NaN), which matches COUNT(1) in SQL\n    total_amount=('amount', 'sum')   # 'sum' for the total amount\n).reset_index()\n\ndf_money_recd_each_destacc = df_money_recd_each_destacc.sort_values(by='count_trans', ascending=False).reset_index(drop=True)\ndf_money_recd_each_destacc.rename(columns={'nameDest':'Dest_account',\n                                     'count_trans':'count_trans',\n                                     'total_amount':'total_amount_Million'}, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:09.705013Z","iopub.execute_input":"2024-09-30T17:28:09.705481Z","iopub.status.idle":"2024-09-30T17:28:21.956709Z","shell.execute_reply.started":"2024-09-30T17:28:09.705441Z","shell.execute_reply":"2024-09-30T17:28:21.955659Z"},"trusted":true},"outputs":[],"execution_count":17},{"cell_type":"code","source":"#format to Million column TotalOrgDiff, TotalDestDiff\ndf_money_recd_each_destacc = df_money_recd_each_destacc.set_index('Dest_account')\ndf_money_recd_each_destacc['total_amount_Million'] = \\\n            df_money_recd_each_destacc['total_amount_Million'].apply(lambda x: float(number_format_M(x)))","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:21.957924Z","iopub.execute_input":"2024-09-30T17:28:21.958246Z","iopub.status.idle":"2024-09-30T17:28:24.324947Z","shell.execute_reply.started":"2024-09-30T17:28:21.958218Z","shell.execute_reply":"2024-09-30T17:28:24.323678Z"},"trusted":true},"outputs":[],"execution_count":18},{"cell_type":"code","source":"df_money_recd_each_destacc.head(5)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:24.326291Z","iopub.execute_input":"2024-09-30T17:28:24.326649Z","iopub.status.idle":"2024-09-30T17:28:24.337529Z","shell.execute_reply.started":"2024-09-30T17:28:24.326618Z","shell.execute_reply":"2024-09-30T17:28:24.336384Z"},"trusted":true},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"              count_trans  total_amount_Million\nDest_account                                   \nC1286084959           113                77.429\nC985934102            109                42.423\nC665576141            105                88.749\nC2083562754           102                53.074\nC248609774            101                40.680","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count_trans</th>\n      <th>total_amount_Million</th>\n    </tr>\n    <tr>\n      <th>Dest_account</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>C1286084959</th>\n      <td>113</td>\n      <td>77.429</td>\n    </tr>\n    <tr>\n      <th>C985934102</th>\n      <td>109</td>\n      <td>42.423</td>\n    </tr>\n    <tr>\n      <th>C665576141</th>\n      <td>105</td>\n      <td>88.749</td>\n    </tr>\n    <tr>\n      <th>C2083562754</th>\n      <td>102</td>\n      <td>53.074</td>\n    </tr>\n    <tr>\n      <th>C248609774</th>\n      <td>101</td>\n      <td>40.680</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"# Set of rules to identify known fraud-based\nrules_fraud_based_df = df.copy()\nconditions = (\n    ((df['oldbalanceOrg'] <= 56900) & \n     (df['type'] == 'TRANSFER') & \n     (df['newbalanceDest'] <= 105)) |\n    ((df['oldbalanceOrg'] > 56900) & \n     (df['newbalanceOrig'] <= 12)) |\n    ((df['oldbalanceOrg'] > 56900) & \n     (df['newbalanceOrig'] > 12) & \n     (df['amount'] > 1160000))\n)\n\nrules_fraud_based_df['label'] = np.where(conditions, 1, 0)\n\n# Calculate proportions\nfraud_cases = rules_fraud_based_df['label'].sum()\ntotal_cases = len(rules_fraud_based_df)\nfraud_pct = fraud_cases / total_cases\n\n# Provide a quick statistics\nprint(f\"Based on these rules, we have flagged {fraud_cases} ({fraud_pct:.2%}) fraud cases out of a total of {total_cases} cases.\")\n","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:24.339060Z","iopub.execute_input":"2024-09-30T17:28:24.339664Z","iopub.status.idle":"2024-09-30T17:28:25.721139Z","shell.execute_reply.started":"2024-09-30T17:28:24.339627Z","shell.execute_reply":"2024-09-30T17:28:25.720013Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Based on these rules, we have flagged 255640 (4.02%) fraud cases out of a total of 6362620 cases.\n","output_type":"stream"}],"execution_count":20},{"cell_type":"code","source":"rules_fraud_based_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:25.722592Z","iopub.execute_input":"2024-09-30T17:28:25.722983Z","iopub.status.idle":"2024-09-30T17:28:25.744244Z","shell.execute_reply.started":"2024-09-30T17:28:25.722945Z","shell.execute_reply":"2024-09-30T17:28:25.743039Z"},"trusted":true},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n0     1   PAYMENT   9839.64  C1231006815       170136.0       160296.36   \n1     1   PAYMENT   1864.28  C1666544295        21249.0        19384.72   \n2     1  TRANSFER    181.00  C1305486145          181.0            0.00   \n3     1  CASH_OUT    181.00   C840083671          181.0            0.00   \n4     1   PAYMENT  11668.14  C2048537720        41554.0        29885.86   \n\n      nameDest  oldbalanceDest  newbalanceDest  isFraud   orgDiff  destDiff  \\\n0  M1979787155             0.0             0.0        0  -9839.64       0.0   \n1  M2044282225             0.0             0.0        0  -1864.28       0.0   \n2   C553264065             0.0             0.0        1   -181.00       0.0   \n3    C38997010         21182.0             0.0        1   -181.00  -21182.0   \n4  M1230701703             0.0             0.0        0 -11668.14       0.0   \n\n   label  \n0      0  \n1      0  \n2      1  \n3      0  \n4      0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>step</th>\n      <th>type</th>\n      <th>amount</th>\n      <th>nameOrig</th>\n      <th>oldbalanceOrg</th>\n      <th>newbalanceOrig</th>\n      <th>nameDest</th>\n      <th>oldbalanceDest</th>\n      <th>newbalanceDest</th>\n      <th>isFraud</th>\n      <th>orgDiff</th>\n      <th>destDiff</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>9839.64</td>\n      <td>C1231006815</td>\n      <td>170136.0</td>\n      <td>160296.36</td>\n      <td>M1979787155</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>-9839.64</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>1864.28</td>\n      <td>C1666544295</td>\n      <td>21249.0</td>\n      <td>19384.72</td>\n      <td>M2044282225</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>-1864.28</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>TRANSFER</td>\n      <td>181.00</td>\n      <td>C1305486145</td>\n      <td>181.0</td>\n      <td>0.00</td>\n      <td>C553264065</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>-181.00</td>\n      <td>0.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>CASH_OUT</td>\n      <td>181.00</td>\n      <td>C840083671</td>\n      <td>181.0</td>\n      <td>0.00</td>\n      <td>C38997010</td>\n      <td>21182.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>-181.00</td>\n      <td>-21182.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>PAYMENT</td>\n      <td>11668.14</td>\n      <td>C2048537720</td>\n      <td>41554.0</td>\n      <td>29885.86</td>\n      <td>M1230701703</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>-11668.14</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":21},{"cell_type":"code","source":"data3 = rules_fraud_based_df.groupby('label').agg(\n    count=('label', 'size'), \n    total_amount=('amount', 'sum')\n).reset_index()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:25.747883Z","iopub.execute_input":"2024-09-30T17:28:25.748586Z","iopub.status.idle":"2024-09-30T17:28:25.940377Z","shell.execute_reply.started":"2024-09-30T17:28:25.748551Z","shell.execute_reply":"2024-09-30T17:28:25.939340Z"},"trusted":true},"outputs":[],"execution_count":22},{"cell_type":"code","source":"df_based_rules = data3.copy()\ndf_based_rules.rename(columns={'count':'transactions'}, inplace=True)\ndf_based_rules['flagged_label'] = np.where(df_based_rules['label']==1, 'fraud', 'normal')\ndf_based_rules","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:25.941878Z","iopub.execute_input":"2024-09-30T17:28:25.942928Z","iopub.status.idle":"2024-09-30T17:28:25.956504Z","shell.execute_reply.started":"2024-09-30T17:28:25.942891Z","shell.execute_reply":"2024-09-30T17:28:25.955335Z"},"trusted":true},"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"   label  transactions  total_amount flagged_label\n0      0       6106980  1.015069e+12        normal\n1      1        255640  1.293243e+11         fraud","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>label</th>\n      <th>transactions</th>\n      <th>total_amount</th>\n      <th>flagged_label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>6106980</td>\n      <td>1.015069e+12</td>\n      <td>normal</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>255640</td>\n      <td>1.293243e+11</td>\n      <td>fraud</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"labels = df_based_rules.flagged_label\nvolume = df_based_rules.transactions\namount = df_based_rules.total_amount\nexplode = (0.1, 0.0) #2 volumes to be exploded \n\n# double pie charts\n# Make figure and axes\nfig, axs = plt.subplots(1,2, figsize=(10, 5))\n#first pie\naxs[0].pie(volume, labels=labels, autopct='%1.2f%%',\\\n            wedgeprops= {\"edgecolor\":\"white\", 'linewidth': 1, 'antialiased': True})\naxs[0].set_title(\"Fraud percentage in total transactions\")\n\n# Adding Circle in first Pie chart\ncentre_circle = plt.Circle((0, 0), 0.55, fc='white')\naxs[0].add_artist(centre_circle)\n#second pie\naxs[1].pie(amount, labels=labels, autopct='%1.2f%%',\\\n             wedgeprops= {\"edgecolor\":\"white\", 'linewidth': 1, 'antialiased': True})\naxs[1].set_title(\"Fraud percentage in total amount of money\")\n\n# Adding Circle in second Pie chart\ncentre_circle = plt.Circle((0, 0), 0.55, fc='white')\naxs[1].add_artist(centre_circle)\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:25.958080Z","iopub.execute_input":"2024-09-30T17:28:25.958480Z","iopub.status.idle":"2024-09-30T17:28:26.197262Z","shell.execute_reply.started":"2024-09-30T17:28:25.958443Z","shell.execute_reply":"2024-09-30T17:28:26.196136Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x500 with 2 Axes>","image/png":"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"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"aggregated_df = rules_fraud_based_df.groupby(['nameOrig', 'nameDest', 'label']).agg(\n    TotalOrgDiff=('orgDiff', 'sum'),\n    TotalDestDiff=('destDiff', 'sum')\n).reset_index()\ndata4 =  aggregated_df[aggregated_df['TotalDestDiff'] >= 1000000].reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:28:26.198758Z","iopub.execute_input":"2024-09-30T17:28:26.199865Z","iopub.status.idle":"2024-09-30T17:29:06.730184Z","shell.execute_reply.started":"2024-09-30T17:28:26.199821Z","shell.execute_reply":"2024-09-30T17:29:06.729151Z"},"trusted":true},"outputs":[],"execution_count":25},{"cell_type":"code","source":"data4.head(10)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:29:06.731651Z","iopub.execute_input":"2024-09-30T17:29:06.732042Z","iopub.status.idle":"2024-09-30T17:29:06.747862Z","shell.execute_reply.started":"2024-09-30T17:29:06.732006Z","shell.execute_reply":"2024-09-30T17:29:06.746767Z"},"trusted":true},"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"      nameOrig     nameDest  label  TotalOrgDiff  TotalDestDiff\n0  C1000005353   C292963054      0     -24996.00     3228390.11\n1  C1000005749  C1247252665      0          0.00     3229333.12\n2   C100002734   C776253559      0          0.00     1288884.49\n3  C1000028246  C1902027015      0          0.00     1467941.17\n4  C1000044288  C1105638219      0          0.00     1306702.73\n5  C1000069949  C1116776735      0     121108.11     1526158.74\n6  C1000077677  C1483097104      1    -175841.00     1627720.86\n7  C1000084237    C55040366      1    -108404.00     3433978.54\n8  C1000097327   C642584095      0          0.00     1003721.94\n9  C1000104995  C1409969112      1    -101733.00     2084473.48","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nameOrig</th>\n      <th>nameDest</th>\n      <th>label</th>\n      <th>TotalOrgDiff</th>\n      <th>TotalDestDiff</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>C1000005353</td>\n      <td>C292963054</td>\n      <td>0</td>\n      <td>-24996.00</td>\n      <td>3228390.11</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C1000005749</td>\n      <td>C1247252665</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>3229333.12</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>C100002734</td>\n      <td>C776253559</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>1288884.49</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>C1000028246</td>\n      <td>C1902027015</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>1467941.17</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>C1000044288</td>\n      <td>C1105638219</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>1306702.73</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>C1000069949</td>\n      <td>C1116776735</td>\n      <td>0</td>\n      <td>121108.11</td>\n      <td>1526158.74</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>C1000077677</td>\n      <td>C1483097104</td>\n      <td>1</td>\n      <td>-175841.00</td>\n      <td>1627720.86</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>C1000084237</td>\n      <td>C55040366</td>\n      <td>1</td>\n      <td>-108404.00</td>\n      <td>3433978.54</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>C1000097327</td>\n      <td>C642584095</td>\n      <td>0</td>\n      <td>0.00</td>\n      <td>1003721.94</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>C1000104995</td>\n      <td>C1409969112</td>\n      <td>1</td>\n      <td>-101733.00</td>\n      <td>2084473.48</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"df_top_diff_Org_Dest = data4.copy()\ndf_top_diff_Org_Dest['flagged_label'] = np.where(df_top_diff_Org_Dest['label']==1, 'fraud', 'normal')\ndf_top_diff_Org_Dest['name'] = df_top_diff_Org_Dest.nameOrig + df_top_diff_Org_Dest.nameDest\ndf_top_diff_Org_Dest.head()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:29:06.749380Z","iopub.execute_input":"2024-09-30T17:29:06.749867Z","iopub.status.idle":"2024-09-30T17:29:06.847335Z","shell.execute_reply.started":"2024-09-30T17:29:06.749828Z","shell.execute_reply":"2024-09-30T17:29:06.846305Z"},"trusted":true},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"      nameOrig     nameDest  label  TotalOrgDiff  TotalDestDiff flagged_label  \\\n0  C1000005353   C292963054      0      -24996.0     3228390.11        normal   \n1  C1000005749  C1247252665      0           0.0     3229333.12        normal   \n2   C100002734   C776253559      0           0.0     1288884.49        normal   \n3  C1000028246  C1902027015      0           0.0     1467941.17        normal   \n4  C1000044288  C1105638219      0           0.0     1306702.73        normal   \n\n                     name  \n0   C1000005353C292963054  \n1  C1000005749C1247252665  \n2    C100002734C776253559  \n3  C1000028246C1902027015  \n4  C1000044288C1105638219  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nameOrig</th>\n      <th>nameDest</th>\n      <th>label</th>\n      <th>TotalOrgDiff</th>\n      <th>TotalDestDiff</th>\n      <th>flagged_label</th>\n      <th>name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>C1000005353</td>\n      <td>C292963054</td>\n      <td>0</td>\n      <td>-24996.0</td>\n      <td>3228390.11</td>\n      <td>normal</td>\n      <td>C1000005353C292963054</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C1000005749</td>\n      <td>C1247252665</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>3229333.12</td>\n      <td>normal</td>\n      <td>C1000005749C1247252665</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>C100002734</td>\n      <td>C776253559</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1288884.49</td>\n      <td>normal</td>\n      <td>C100002734C776253559</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>C1000028246</td>\n      <td>C1902027015</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1467941.17</td>\n      <td>normal</td>\n      <td>C1000028246C1902027015</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>C1000044288</td>\n      <td>C1105638219</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1306702.73</td>\n      <td>normal</td>\n      <td>C1000044288C1105638219</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":27},{"cell_type":"code","source":"def number_format_M(number):\n    #function format number to Million\n    units = 'B'\n    k = 1000000.0\n    return '%.3f' % (number / k)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:29:06.853907Z","iopub.execute_input":"2024-09-30T17:29:06.854279Z","iopub.status.idle":"2024-09-30T17:29:06.859729Z","shell.execute_reply.started":"2024-09-30T17:29:06.854248Z","shell.execute_reply":"2024-09-30T17:29:06.858455Z"},"trusted":true},"outputs":[],"execution_count":28},{"cell_type":"code","source":"#format to Million column TotalOrgDiff, TotalDestDiff\ndf_top_diff_Org_Dest['TotalOrgDiff'] = \\\n            df_top_diff_Org_Dest['TotalOrgDiff'].apply(lambda x: number_format_M(x))\ndf_top_diff_Org_Dest['TotalDestDiff'] = \\\n            df_top_diff_Org_Dest['TotalDestDiff'].apply(lambda x: number_format_M(x))\n\ndf_top_diff_Org_Dest.head()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:29:06.860808Z","iopub.execute_input":"2024-09-30T17:29:06.861119Z","iopub.status.idle":"2024-09-30T17:29:07.077442Z","shell.execute_reply.started":"2024-09-30T17:29:06.861086Z","shell.execute_reply":"2024-09-30T17:29:07.076448Z"},"trusted":true},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"      nameOrig     nameDest  label TotalOrgDiff TotalDestDiff flagged_label  \\\n0  C1000005353   C292963054      0       -0.025         3.228        normal   \n1  C1000005749  C1247252665      0        0.000         3.229        normal   \n2   C100002734   C776253559      0        0.000         1.289        normal   \n3  C1000028246  C1902027015      0        0.000         1.468        normal   \n4  C1000044288  C1105638219      0        0.000         1.307        normal   \n\n                     name  \n0   C1000005353C292963054  \n1  C1000005749C1247252665  \n2    C100002734C776253559  \n3  C1000028246C1902027015  \n4  C1000044288C1105638219  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>nameOrig</th>\n      <th>nameDest</th>\n      <th>label</th>\n      <th>TotalOrgDiff</th>\n      <th>TotalDestDiff</th>\n      <th>flagged_label</th>\n      <th>name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>C1000005353</td>\n      <td>C292963054</td>\n      <td>0</td>\n      <td>-0.025</td>\n      <td>3.228</td>\n      <td>normal</td>\n      <td>C1000005353C292963054</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>C1000005749</td>\n      <td>C1247252665</td>\n      <td>0</td>\n      <td>0.000</td>\n      <td>3.229</td>\n      <td>normal</td>\n      <td>C1000005749C1247252665</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>C100002734</td>\n      <td>C776253559</td>\n      <td>0</td>\n      <td>0.000</td>\n      <td>1.289</td>\n      <td>normal</td>\n      <td>C100002734C776253559</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>C1000028246</td>\n      <td>C1902027015</td>\n      <td>0</td>\n      <td>0.000</td>\n      <td>1.468</td>\n      <td>normal</td>\n      <td>C1000028246C1902027015</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>C1000044288</td>\n      <td>C1105638219</td>\n      <td>0</td>\n      <td>0.000</td>\n      <td>1.307</td>\n      <td>normal</td>\n      <td>C1000044288C1105638219</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":29},{"cell_type":"markdown","source":"## What type of transactions are associated with fraud?","metadata":{}},{"cell_type":"code","source":"data5 = rules_fraud_based_df.groupby(['type', 'label']).size().reset_index(name='transactions')","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:29:07.079023Z","iopub.execute_input":"2024-09-30T17:29:07.079344Z","iopub.status.idle":"2024-09-30T17:29:07.875072Z","shell.execute_reply.started":"2024-09-30T17:29:07.079315Z","shell.execute_reply":"2024-09-30T17:29:07.874073Z"},"trusted":true},"outputs":[],"execution_count":30},{"cell_type":"code","source":"df_fraud_in_type_trans = data5.copy()\ndf_fraud_in_type_trans = df_fraud_in_type_trans.rename(columns={'transactions':'sum_trans'})\ndf_fraud_in_type_trans['label'] = np.where(df_fraud_in_type_trans['label'] == 1, 'fraud', 'regular')\ndf_fraud_in_type_trans","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:29:07.876285Z","iopub.execute_input":"2024-09-30T17:29:07.876715Z","iopub.status.idle":"2024-09-30T17:29:07.890656Z","shell.execute_reply.started":"2024-09-30T17:29:07.876678Z","shell.execute_reply":"2024-09-30T17:29:07.889463Z"},"trusted":true},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"       type    label  sum_trans\n0   CASH_IN  regular    1399219\n1   CASH_IN    fraud         65\n2  CASH_OUT  regular    2052790\n3  CASH_OUT    fraud     184710\n4     DEBIT  regular      41401\n5     DEBIT    fraud         31\n6   PAYMENT  regular    2150909\n7   PAYMENT    fraud        586\n8  TRANSFER  regular     462661\n9  TRANSFER    fraud      70248","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>type</th>\n      <th>label</th>\n      <th>sum_trans</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CASH_IN</td>\n      <td>regular</td>\n      <td>1399219</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CASH_IN</td>\n      <td>fraud</td>\n      <td>65</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CASH_OUT</td>\n      <td>regular</td>\n      <td>2052790</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CASH_OUT</td>\n      <td>fraud</td>\n      <td>184710</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>DEBIT</td>\n      <td>regular</td>\n      <td>41401</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>DEBIT</td>\n      <td>fraud</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>PAYMENT</td>\n      <td>regular</td>\n      <td>2150909</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>PAYMENT</td>\n      <td>fraud</td>\n      <td>586</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>TRANSFER</td>\n      <td>regular</td>\n      <td>462661</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>TRANSFER</td>\n      <td>fraud</td>\n      <td>70248</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"outer = df_fraud_in_type_trans.groupby('type').sum(numeric_only=True)\ndisplay(outer)\ninner = df_fraud_in_type_trans.groupby(['type', 'label']).sum()\ndisplay(inner)\ninner_labels = inner.index.get_level_values(1)\ndisplay(inner_labels)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:29:07.892057Z","iopub.execute_input":"2024-09-30T17:29:07.892471Z","iopub.status.idle":"2024-09-30T17:29:07.919073Z","shell.execute_reply.started":"2024-09-30T17:29:07.892440Z","shell.execute_reply":"2024-09-30T17:29:07.917867Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"          sum_trans\ntype               \nCASH_IN     1399284\nCASH_OUT    2237500\nDEBIT         41432\nPAYMENT     2151495\nTRANSFER     532909","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>sum_trans</th>\n    </tr>\n    <tr>\n      <th>type</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>CASH_IN</th>\n      <td>1399284</td>\n    </tr>\n    <tr>\n      <th>CASH_OUT</th>\n      <td>2237500</td>\n    </tr>\n    <tr>\n      <th>DEBIT</th>\n      <td>41432</td>\n    </tr>\n    <tr>\n      <th>PAYMENT</th>\n      <td>2151495</td>\n    </tr>\n    <tr>\n      <th>TRANSFER</th>\n      <td>532909</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"                  sum_trans\ntype     label             \nCASH_IN  fraud           65\n         regular    1399219\nCASH_OUT fraud       184710\n         regular    2052790\nDEBIT    fraud           31\n         regular      41401\nPAYMENT  fraud          586\n         regular    2150909\nTRANSFER fraud        70248\n         regular     462661","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>sum_trans</th>\n    </tr>\n    <tr>\n      <th>type</th>\n      <th>label</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">CASH_IN</th>\n      <th>fraud</th>\n      <td>65</td>\n    </tr>\n    <tr>\n      <th>regular</th>\n      <td>1399219</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">CASH_OUT</th>\n      <th>fraud</th>\n      <td>184710</td>\n    </tr>\n    <tr>\n      <th>regular</th>\n      <td>2052790</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">DEBIT</th>\n      <th>fraud</th>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>regular</th>\n      <td>41401</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">PAYMENT</th>\n      <th>fraud</th>\n      <td>586</td>\n    </tr>\n    <tr>\n      <th>regular</th>\n      <td>2150909</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">TRANSFER</th>\n      <th>fraud</th>\n      <td>70248</td>\n    </tr>\n    <tr>\n      <th>regular</th>\n      <td>462661</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Index(['fraud', 'regular', 'fraud', 'regular', 'fraud', 'regular', 'fraud',\n       'regular', 'fraud', 'regular'],\n      dtype='object', name='label')"},"metadata":{}}],"execution_count":32},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(24,12))\nsize = 0.3\n#cmap color\ncmap = plt.colormaps[\"tab20c\"]\nouter_colors = cmap(np.arange(5)*4)\ninner_colors = cmap([1,2, 5,6, 9,10, 13,14, 17,18])\n#outter pie\nax.pie(outer.values.flatten(), \n       radius=1.1, labels=outer.index, autopct='%1.5f%%', colors=outer_colors,\n      wedgeprops=dict(width=size, edgecolor='w'))\n#inner pie\nax.pie(inner.values.flatten(), radius=1.1-size, labels=inner_labels, colors=inner_colors, \n       autopct='%1.5f%%', wedgeprops=dict(width=size, edgecolor='w'))\n#tittle\nax.set(title=\"Fraud/not Fraud in each type of transactions associated with\")\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:29:07.920466Z","iopub.execute_input":"2024-09-30T17:29:07.920775Z","iopub.status.idle":"2024-09-30T17:29:08.347927Z","shell.execute_reply.started":"2024-09-30T17:29:07.920748Z","shell.execute_reply":"2024-09-30T17:29:08.346735Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2400x1200 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":33},{"cell_type":"markdown","source":"## Rules vs. ML model\nInstead of creating specific rules that will change over time, we can be more precise and more productive by using ML model\n\n### Create training and testing datasets\nUsing randomplit to create our training and testing datsets, to build and vilidate our generalized ML model","metadata":{}},{"cell_type":"code","source":"!pip install pyspark -q","metadata":{"execution":{"iopub.status.busy":"2024-10-12T13:28:42.274136Z","iopub.execute_input":"2024-10-12T13:28:42.275430Z","iopub.status.idle":"2024-10-12T13:28:52.605906Z","shell.execute_reply.started":"2024-10-12T13:28:42.275381Z","shell.execute_reply":"2024-10-12T13:28:52.604236Z"},"trusted":true},"outputs":[{"name":"stdout","text":"^C\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"pip install pyspark","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:33:13.307606Z","iopub.execute_input":"2024-09-30T17:33:13.308402Z"},"trusted":true},"outputs":[{"name":"stdout","text":"\u001b[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7d15df291ab0>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/pyspark/\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7d15df291db0>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/pyspark/\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}],"execution_count":null},{"cell_type":"code","source":"#import pyspark\nimport pyspark\nfrom pyspark import SparkContext, SparkConf\nfrom pyspark.sql import SparkSession, SQLContext\nprint(f\"PySpark Version : {pyspark.__version__}\")","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:32:03.238527Z","iopub.execute_input":"2024-09-30T17:32:03.238916Z","iopub.status.idle":"2024-09-30T17:32:03.264530Z","shell.execute_reply.started":"2024-09-30T17:32:03.238886Z","shell.execute_reply":"2024-09-30T17:32:03.263247Z"},"trusted":true},"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","Cell \u001b[0;32mIn[36], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#import pyspark\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpyspark\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyspark\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SparkContext, SparkConf\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyspark\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msql\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SparkSession, SQLContext\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyspark'"],"ename":"ModuleNotFoundError","evalue":"No module named 'pyspark'","output_type":"error"}],"execution_count":36},{"cell_type":"code","source":"#Create a spark Context class, with custom config\nconf = SparkConf()\nconf.set('spark.default.parallelism', 700)\nconf.set('spark.sql.shuffle.partitions', 700)\nconf.set('spark.driver.memory', '30g')\nconf.set('spark.driver.cores', 8)\nconf.set('spark.executor.cores', 8)\nconf.set('spark.executor.memory', '30g')\nsc = SparkContext.getOrCreate(conf)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.750912Z","iopub.status.idle":"2024-09-30T17:31:38.751347Z","shell.execute_reply.started":"2024-09-30T17:31:38.751139Z","shell.execute_reply":"2024-09-30T17:31:38.751158Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"## Create spark session\nspark = SparkSession.builder.master('local[*]').\\\n                config('spark.sql.debug.maxToStringFields', '100').\\\n                appName(\"Python Spark Dataframes Financial Fruad\").getOrCreate()","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.753701Z","iopub.status.idle":"2024-09-30T17:31:38.754276Z","shell.execute_reply.started":"2024-09-30T17:31:38.753986Z","shell.execute_reply":"2024-09-30T17:31:38.754012Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df = spark.createDataFrame(df)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.756507Z","iopub.status.idle":"2024-09-30T17:31:38.756923Z","shell.execute_reply.started":"2024-09-30T17:31:38.756731Z","shell.execute_reply":"2024-09-30T17:31:38.756748Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#initially split our dataset\n(train, test) = df.randomSplit([0.8, 0.2], seed=12345)\n\n#Cache the training and test datasets\ntrain.cache()\ntest.cache()\n\n#print out dataset counts\nprint(\"Total rows: %s, Training rows: %s, Test rows: %s\" %\\\n      (df.count(), train.count(), test.count()))","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.758677Z","iopub.status.idle":"2024-09-30T17:31:38.759068Z","shell.execute_reply.started":"2024-09-30T17:31:38.758874Z","shell.execute_reply":"2024-09-30T17:31:38.758889Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Create ML pipeline\nWhen creating an ML model, there are typically a set of repeated steps(e.g. StringIndexer, VectorAssembler, etc.). By creating a ML pipeline, we can reuse this pipeline( and all of its steps) to retrain on a new and /or updated dataset.","metadata":{}},{"cell_type":"code","source":"from pyspark.ml import Pipeline\nfrom pyspark.ml.feature import StringIndexer, OneHotEncoder, VectorAssembler\nfrom pyspark.ml.classification import DecisionTreeClassifier\n\n#Encodes a string column of labels to a column of label indices\nindexer = StringIndexer(inputCol=\"type\", outputCol=\"typeIndexed\")\n\n#VectorAssembler is a transformer that combines\n#a given list of columns into a single vector column\nva = VectorAssembler(inputCols=[\"typeIndexed\", \"amount\", \"oldbalanceOrg\", \"newbalanceOrig\",\n                               \"oldbalanceDest\", \"newbalanceDest\", \"orgDiff\", \"destDiff\"], outputCol=\"features\")\n\n#Using the DecisionTree classifier model\ndt = DecisionTreeClassifier(labelCol='isFraud', featuresCol='features', seed=54321, maxDepth=5)\n\n# Create our pipeline stages\npipeline = Pipeline(stages=[indexer, va, dt])","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.760547Z","iopub.status.idle":"2024-09-30T17:31:38.760987Z","shell.execute_reply.started":"2024-09-30T17:31:38.760764Z","shell.execute_reply":"2024-09-30T17:31:38.760781Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#view the Decision Tree model\ndt_model = pipeline.fit(train)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.762284Z","iopub.status.idle":"2024-09-30T17:31:38.762687Z","shell.execute_reply.started":"2024-09-30T17:31:38.762498Z","shell.execute_reply":"2024-09-30T17:31:38.762516Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(dt_model.stages[-1].toDebugString)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.764420Z","iopub.status.idle":"2024-09-30T17:31:38.764818Z","shell.execute_reply.started":"2024-09-30T17:31:38.764630Z","shell.execute_reply":"2024-09-30T17:31:38.764646Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#extract features input and target variable\nfeatures_input = [\"typeIndexed\", \"amount\",\"oldbalanceOrg\", \"newbalanceOrig\",\"oldbalanceDest\",\"newbalanceDest\", \"orgDiff\", \"destDiff\"]\ntarget = \"isFraud\"\n\n#extract decision tree from our model\ndt_extracted = dt_model.stages[2]\n\n#recompute the dataset on which the model was trained, with StringIndexer encoding only\ndataset = Pipeline(stages=[indexer]).fit(train).transform(train).toPandas()[features_input+[target]]","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.766148Z","iopub.status.idle":"2024-09-30T17:31:38.766555Z","shell.execute_reply.started":"2024-09-30T17:31:38.766339Z","shell.execute_reply":"2024-09-30T17:31:38.766354Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install dtreeviz -q","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.768052Z","iopub.status.idle":"2024-09-30T17:31:38.768435Z","shell.execute_reply.started":"2024-09-30T17:31:38.768237Z","shell.execute_reply":"2024-09-30T17:31:38.768252Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#import viz library\nimport dtreeviz\nviz_model = dtreeviz.model(dt_extracted,\n                          X_train=dataset[features_input],\n                          y_train=dataset[target],\n                          feature_names=features_input,\n                          target_name=target, class_names=['0', '1'])","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.769725Z","iopub.status.idle":"2024-09-30T17:31:38.770095Z","shell.execute_reply.started":"2024-09-30T17:31:38.769913Z","shell.execute_reply":"2024-09-30T17:31:38.769927Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#take long to viz large scale data\nviz_model.view(fancy=False)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.771349Z","iopub.status.idle":"2024-09-30T17:31:38.771798Z","shell.execute_reply.started":"2024-09-30T17:31:38.771575Z","shell.execute_reply":"2024-09-30T17:31:38.771591Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Use BinaryClassificationEvaluator\nDetermine the accuracy of the model by reviewing the areaUnderPR and areaUnderROC","metadata":{}},{"cell_type":"code","source":"from pyspark.ml.evaluation import BinaryClassificationEvaluator\n\n#evaluate the model\nevaluatorPR = BinaryClassificationEvaluator(labelCol=\"isFraud\",\n                                            rawPredictionCol='prediction',\n                                            metricName='areaUnderPR')\nevaluatorAUC = BinaryClassificationEvaluator(labelCol=\"isFraud\",\n                                             rawPredictionCol=\"prediction\",\n                                             metricName=\"areaUnderROC\")","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.773689Z","iopub.status.idle":"2024-09-30T17:31:38.774304Z","shell.execute_reply.started":"2024-09-30T17:31:38.774026Z","shell.execute_reply":"2024-09-30T17:31:38.774048Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Setup CrossValidation\nTo potentially improve our model, we will use CrossValidator in conjunction with the ParamGriBuilder to automate trying out different parameters.\n\nNOTE: We are using evaluatorPR as our evaluator as the Precision-Recall curve is often better for an imbalanced distribution.","metadata":{}},{"cell_type":"code","source":"##40 minutes running time sections\nfrom pyspark.ml.tuning import CrossValidator, ParamGridBuilder\n\n#Build the grid of different parameters\nparamGird = ParamGridBuilder().addGrid(dt.maxDepth, [5, 10, 15]).addGrid(dt.maxBins, [10, 20, 30]).build()\n\n#Build out the cross validation\ncrossval = CrossValidator(estimator=dt,\n                         estimatorParamMaps= paramGird,\n                         evaluator=evaluatorPR,\n                         numFolds=3)\n\npipelineCV = Pipeline(stages=[indexer, va, crossval])\n\n#Train the model using the pipeline, params grid, and preceding BinaryClassificationEvaluator\ncvModel_u = pipelineCV.fit(train)","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.777366Z","iopub.status.idle":"2024-09-30T17:31:38.777843Z","shell.execute_reply.started":"2024-09-30T17:31:38.777637Z","shell.execute_reply":"2024-09-30T17:31:38.777657Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Review the results\nreview the areaUnderPR (Area Under Precision Recall curve) and areaUnderROC(Area Under Receiver Operating Characteristic) or AUC (Area Under Curve) metrics.","metadata":{}},{"cell_type":"code","source":"# Build the best model (training and test datasets)\ntrain_pred = cvModel_u.transform(train)\ntest_pred = cvModel_u.transform(test)\n\n#Evaluate the model on training datasets\npr_train = evaluatorPR.evaluate(train_pred)\nauc_train = evaluatorAUC.evaluate(train_pred)\n\n#Evaluate the model on test datasets\npr_test = evaluatorPR.evaluate(test_pred)\nauc_test = evaluatorAUC.evaluate(test_pred)\n\n#show the results\nprint(\"PR train:\", pr_train)\nprint(\"AUC train:\", auc_train)\nprint(\"PR test:\", pr_test)\nprint(\"AUC test:\", auc_test)\n###","metadata":{"execution":{"iopub.status.busy":"2024-09-30T17:31:38.779734Z","iopub.status.idle":"2024-09-30T17:31:38.780161Z","shell.execute_reply.started":"2024-09-30T17:31:38.779969Z","shell.execute_reply":"2024-09-30T17:31:38.779986Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{},"outputs":[],"execution_count":null}]}