fraud-detection.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":10312379,"sourceType":"datasetVersion","datasetId":6383953}],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Fraud Detection Using AI\n","metadata":{}},{"cell_type":"code","source":"print(\"Hello World\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T15:52:53.219660Z","iopub.execute_input":"2024-12-27T15:52:53.220061Z","iopub.status.idle":"2024-12-27T15:52:53.225993Z","shell.execute_reply.started":"2024-12-27T15:52:53.219997Z","shell.execute_reply":"2024-12-27T15:52:53.224439Z"}},"outputs":[{"name":"stdout","text":"Hello World\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"import pandas as pd\ndata = pd.read_csv(\"/kaggle/input/fraud-detection-dataset/dataset.csv\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:00:12.358366Z","iopub.execute_input":"2024-12-27T16:00:12.358853Z","iopub.status.idle":"2024-12-27T16:00:12.828143Z","shell.execute_reply.started":"2024-12-27T16:00:12.358815Z","shell.execute_reply":"2024-12-27T16:00:12.826922Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:00:12.829695Z","iopub.execute_input":"2024-12-27T16:00:12.830015Z","iopub.status.idle":"2024-12-27T16:00:12.873128Z","shell.execute_reply.started":"2024-12-27T16:00:12.829987Z","shell.execute_reply":"2024-12-27T16:00:12.872044Z"}},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"   Dummy Policy No  ASSURED_AGE NOMINEE_RELATION     OCCUPATION  \\\n0                1           20           Mother        Service   \n1                2           33          Husband        Service   \n2                3           34             Wife  Self-Employed   \n3                4           34             Wife        Service   \n4                5           54          Husband      Housewife   \n\n  POLICY SUMASSURED     Premium PREMIUMPAYMENTMODE Annual Income  \\\n0        1,200,000     120,000           Quarterly      420,000    \n1          350,000       5,000              Yearly      180,000    \n2          350,000       5,000              Yearly      249,000    \n3          325,000       5,000              Yearly      360,000    \n4          100,000       5,000              Yearly      600,000    \n\n  HOLDERMARITALSTATUS INDIV_REQUIREMENTFLAG  ...  CORRESPONDENCEPOSTCODE  \\\n0              Single           Non Medical  ...                  816115   \n1             Married           Non Medical  ...                  464001   \n2             Married           Non Medical  ...                  731238   \n3             Married           Non Medical  ...                  281504   \n4             Married           Non Medical  ...                  606001   \n\n   Product Type        CHANNEL Bank code POLICYRISKCOMMENCEMENTDATE  \\\n0          ULIP  Retail Agency       NaN                 30-08-2023   \n1   Traditional  Retail Agency       NaN                 02-03-2022   \n2   Traditional  Retail Agency       NaN                 08-07-2022   \n3   Traditional  Retail Agency       NaN                 27-12-2022   \n4   Traditional  Retail Agency       NaN                 24-03-2023   \n\n  Date of Death INTIMATIONDATE  STATUS              SUB_STATUS  \\\n0    26-10-2023     12-12-2023   Claim  Death Claim Repudiated   \n1    31-03-2022     10-02-2023   Claim  Death Claim Repudiated   \n2    11-11-2022     26-06-2023   Claim  Death Claim Repudiated   \n3    08-04-2023     14-08-2023   Claim  Death Claim Repudiated   \n4    11-04-2023     06-12-2023   Claim  Death Claim Repudiated   \n\n      Fraud Category  \n0  Misrepresentation  \n1  Misrepresentation  \n2  Misrepresentation  \n3  Misrepresentation  \n4  Misrepresentation  \n\n[5 rows x 24 columns]","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>Dummy Policy No</th>\n      <th>ASSURED_AGE</th>\n      <th>NOMINEE_RELATION</th>\n      <th>OCCUPATION</th>\n      <th>POLICY SUMASSURED</th>\n      <th>Premium</th>\n      <th>PREMIUMPAYMENTMODE</th>\n      <th>Annual Income</th>\n      <th>HOLDERMARITALSTATUS</th>\n      <th>INDIV_REQUIREMENTFLAG</th>\n      <th>...</th>\n      <th>CORRESPONDENCEPOSTCODE</th>\n      <th>Product Type</th>\n      <th>CHANNEL</th>\n      <th>Bank code</th>\n      <th>POLICYRISKCOMMENCEMENTDATE</th>\n      <th>Date of Death</th>\n      <th>INTIMATIONDATE</th>\n      <th>STATUS</th>\n      <th>SUB_STATUS</th>\n      <th>Fraud Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20</td>\n      <td>Mother</td>\n      <td>Service</td>\n      <td>1,200,000</td>\n      <td>120,000</td>\n      <td>Quarterly</td>\n      <td>420,000</td>\n      <td>Single</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>816115</td>\n      <td>ULIP</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>30-08-2023</td>\n      <td>26-10-2023</td>\n      <td>12-12-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>33</td>\n      <td>Husband</td>\n      <td>Service</td>\n      <td>350,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>180,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>464001</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>02-03-2022</td>\n      <td>31-03-2022</td>\n      <td>10-02-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>34</td>\n      <td>Wife</td>\n      <td>Self-Employed</td>\n      <td>350,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>249,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>731238</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>08-07-2022</td>\n      <td>11-11-2022</td>\n      <td>26-06-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>34</td>\n      <td>Wife</td>\n      <td>Service</td>\n      <td>325,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>360,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>281504</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>27-12-2022</td>\n      <td>08-04-2023</td>\n      <td>14-08-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>54</td>\n      <td>Husband</td>\n      <td>Housewife</td>\n      <td>100,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>600,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>606001</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>24-03-2023</td>\n      <td>11-04-2023</td>\n      <td>06-12-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 24 columns</p>\n</div>"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"len(data)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:00:20.227578Z","iopub.execute_input":"2024-12-27T16:00:20.228057Z","iopub.status.idle":"2024-12-27T16:00:20.235444Z","shell.execute_reply.started":"2024-12-27T16:00:20.228013Z","shell.execute_reply":"2024-12-27T16:00:20.234019Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"1321"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"data = pd.DataFrame(data)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:42:15.140783Z","iopub.execute_input":"2024-12-27T16:42:15.141224Z","iopub.status.idle":"2024-12-27T16:42:15.147571Z","shell.execute_reply.started":"2024-12-27T16:42:15.141185Z","shell.execute_reply":"2024-12-27T16:42:15.146416Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:42:20.715597Z","iopub.execute_input":"2024-12-27T16:42:20.716093Z","iopub.status.idle":"2024-12-27T16:42:20.747598Z","shell.execute_reply.started":"2024-12-27T16:42:20.716054Z","shell.execute_reply":"2024-12-27T16:42:20.743550Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"   Dummy Policy No  ASSURED_AGE NOMINEE_RELATION     OCCUPATION  \\\n0                1           20           Mother        Service   \n1                2           33          Husband        Service   \n2                3           34             Wife  Self-Employed   \n3                4           34             Wife        Service   \n4                5           54          Husband      Housewife   \n\n  POLICY SUMASSURED     Premium PREMIUMPAYMENTMODE Annual Income  \\\n0        1,200,000     120,000           Quarterly      420,000    \n1          350,000       5,000              Yearly      180,000    \n2          350,000       5,000              Yearly      249,000    \n3          325,000       5,000              Yearly      360,000    \n4          100,000       5,000              Yearly      600,000    \n\n  HOLDERMARITALSTATUS INDIV_REQUIREMENTFLAG  ...  CORRESPONDENCEPOSTCODE  \\\n0              Single           Non Medical  ...                  816115   \n1             Married           Non Medical  ...                  464001   \n2             Married           Non Medical  ...                  731238   \n3             Married           Non Medical  ...                  281504   \n4             Married           Non Medical  ...                  606001   \n\n   Product Type        CHANNEL Bank code POLICYRISKCOMMENCEMENTDATE  \\\n0          ULIP  Retail Agency       NaN                 30-08-2023   \n1   Traditional  Retail Agency       NaN                 02-03-2022   \n2   Traditional  Retail Agency       NaN                 08-07-2022   \n3   Traditional  Retail Agency       NaN                 27-12-2022   \n4   Traditional  Retail Agency       NaN                 24-03-2023   \n\n  Date of Death INTIMATIONDATE  STATUS              SUB_STATUS  \\\n0    26-10-2023     12-12-2023   Claim  Death Claim Repudiated   \n1    31-03-2022     10-02-2023   Claim  Death Claim Repudiated   \n2    11-11-2022     26-06-2023   Claim  Death Claim Repudiated   \n3    08-04-2023     14-08-2023   Claim  Death Claim Repudiated   \n4    11-04-2023     06-12-2023   Claim  Death Claim Repudiated   \n\n      Fraud Category  \n0  Misrepresentation  \n1  Misrepresentation  \n2  Misrepresentation  \n3  Misrepresentation  \n4  Misrepresentation  \n\n[5 rows x 24 columns]","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>Dummy Policy No</th>\n      <th>ASSURED_AGE</th>\n      <th>NOMINEE_RELATION</th>\n      <th>OCCUPATION</th>\n      <th>POLICY SUMASSURED</th>\n      <th>Premium</th>\n      <th>PREMIUMPAYMENTMODE</th>\n      <th>Annual Income</th>\n      <th>HOLDERMARITALSTATUS</th>\n      <th>INDIV_REQUIREMENTFLAG</th>\n      <th>...</th>\n      <th>CORRESPONDENCEPOSTCODE</th>\n      <th>Product Type</th>\n      <th>CHANNEL</th>\n      <th>Bank code</th>\n      <th>POLICYRISKCOMMENCEMENTDATE</th>\n      <th>Date of Death</th>\n      <th>INTIMATIONDATE</th>\n      <th>STATUS</th>\n      <th>SUB_STATUS</th>\n      <th>Fraud Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20</td>\n      <td>Mother</td>\n      <td>Service</td>\n      <td>1,200,000</td>\n      <td>120,000</td>\n      <td>Quarterly</td>\n      <td>420,000</td>\n      <td>Single</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>816115</td>\n      <td>ULIP</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>30-08-2023</td>\n      <td>26-10-2023</td>\n      <td>12-12-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>33</td>\n      <td>Husband</td>\n      <td>Service</td>\n      <td>350,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>180,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>464001</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>02-03-2022</td>\n      <td>31-03-2022</td>\n      <td>10-02-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>34</td>\n      <td>Wife</td>\n      <td>Self-Employed</td>\n      <td>350,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>249,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>731238</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>08-07-2022</td>\n      <td>11-11-2022</td>\n      <td>26-06-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>34</td>\n      <td>Wife</td>\n      <td>Service</td>\n      <td>325,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>360,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>281504</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>27-12-2022</td>\n      <td>08-04-2023</td>\n      <td>14-08-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>54</td>\n      <td>Husband</td>\n      <td>Housewife</td>\n      <td>100,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>600,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>606001</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>24-03-2023</td>\n      <td>11-04-2023</td>\n      <td>06-12-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n      <td>Misrepresentation</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 24 columns</p>\n</div>"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"y = data['Fraud Category']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:42:56.060876Z","iopub.execute_input":"2024-12-27T16:42:56.061234Z","iopub.status.idle":"2024-12-27T16:42:56.066439Z","shell.execute_reply.started":"2024-12-27T16:42:56.061205Z","shell.execute_reply":"2024-12-27T16:42:56.064961Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"y","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:42:59.461455Z","iopub.execute_input":"2024-12-27T16:42:59.461924Z","iopub.status.idle":"2024-12-27T16:42:59.472746Z","shell.execute_reply.started":"2024-12-27T16:42:59.461884Z","shell.execute_reply":"2024-12-27T16:42:59.470294Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"0        Misrepresentation\n1        Misrepresentation\n2        Misrepresentation\n3        Misrepresentation\n4        Misrepresentation\n               ...        \n1316    Document Tampering\n1317           Misselling \n1318           Misselling \n1319     Signature Forgery\n1320     Signature Forgery\nName: Fraud Category, Length: 1321, dtype: object"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"x = data.drop('Fraud Category', axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:43:38.235930Z","iopub.execute_input":"2024-12-27T16:43:38.236335Z","iopub.status.idle":"2024-12-27T16:43:38.243298Z","shell.execute_reply.started":"2024-12-27T16:43:38.236297Z","shell.execute_reply":"2024-12-27T16:43:38.242068Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"x","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:43:42.475532Z","iopub.execute_input":"2024-12-27T16:43:42.475986Z","iopub.status.idle":"2024-12-27T16:43:42.505447Z","shell.execute_reply.started":"2024-12-27T16:43:42.475952Z","shell.execute_reply":"2024-12-27T16:43:42.504129Z"}},"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"      Dummy Policy No  ASSURED_AGE NOMINEE_RELATION     OCCUPATION  \\\n0                   1           20           Mother        Service   \n1                   2           33          Husband        Service   \n2                   3           34             Wife  Self-Employed   \n3                   4           34             Wife        Service   \n4                   5           54          Husband      Housewife   \n...               ...          ...              ...            ...   \n1316             1317           42           Spouse  Self-Employed   \n1317             1318           20              Son       Business   \n1318             1319           54          Husband        Service   \n1319             1320           36         Daughter      Housewife   \n1320             1321           36              Son      Housewife   \n\n     POLICY SUMASSURED       Premium PREMIUMPAYMENTMODE Annual Income  \\\n0           1,200,000       120,000           Quarterly      420,000    \n1             350,000         5,000              Yearly      180,000    \n2             350,000         5,000              Yearly      249,000    \n3             325,000         5,000              Yearly      360,000    \n4             100,000         5,000              Yearly      600,000    \n...                ...           ...                ...           ...   \n1316        1,750,000       250,000              Yearly      681,600    \n1317          300,000        15,000              Yearly      200,000    \n1318          300,000        15,000              Yearly    1,000,000    \n1319                0     1,000,000              Single    1,200,000    \n1320                0     1,000,000              Single    1,200,000    \n\n     HOLDERMARITALSTATUS INDIV_REQUIREMENTFLAG  ...  CORRESPONDENCESTATE  \\\n0                 Single           Non Medical  ...            Jharkhand   \n1                Married           Non Medical  ...       Madhya Pradesh   \n2                Married           Non Medical  ...          West Bengal   \n3                Married           Non Medical  ...        Uttar Pradesh   \n4                Married           Non Medical  ...           Tamil Nadu   \n...                  ...                   ...  ...                  ...   \n1316             Married           Non Medical  ...        Uttar Pradesh   \n1317              Single           Non Medical  ...       Madhya Pradesh   \n1318             Married           Non Medical  ...       Madhya Pradesh   \n1319             widowed           Non Medical  ...              Haryana   \n1320             widowed           Non Medical  ...              Haryana   \n\n      CORRESPONDENCEPOSTCODE Product Type        CHANNEL Bank code  \\\n0                     816115         ULIP  Retail Agency       NaN   \n1                     464001  Traditional  Retail Agency       NaN   \n2                     731238  Traditional  Retail Agency       NaN   \n3                     281504  Traditional  Retail Agency       NaN   \n4                     606001  Traditional  Retail Agency       NaN   \n...                      ...          ...            ...       ...   \n1316                  231216         ULIP  Bancassurance       1.0   \n1317                  473001     Variable  Bancassurance       7.0   \n1318                  473001     Variable  Bancassurance       7.0   \n1319                  121003      Pension  Bancassurance       1.0   \n1320                  121003      Pension  Bancassurance       1.0   \n\n     POLICYRISKCOMMENCEMENTDATE Date of Death  INTIMATIONDATE        STATUS  \\\n0                    30-08-2023    26-10-2023      12-12-2023         Claim   \n1                    02-03-2022    31-03-2022      10-02-2023         Claim   \n2                    08-07-2022    11-11-2022      26-06-2023         Claim   \n3                    27-12-2022    08-04-2023      14-08-2023         Claim   \n4                    24-03-2023    11-04-2023      06-12-2023         Claim   \n...                         ...           ...             ...           ...   \n1316                          -             -               -  Cancellation   \n1317                 20-03-2013             -               -      Maturity   \n1318                 20-03-2013             -               -      Maturity   \n1319                 19-09-2019             -               -       Inforce   \n1320                 06-01-2020             -               -       Inforce   \n\n                  SUB_STATUS  \n0     Death Claim Repudiated  \n1     Death Claim Repudiated  \n2     Death Claim Repudiated  \n3     Death Claim Repudiated  \n4     Death Claim Repudiated  \n...                      ...  \n1316            Other Reason  \n1317      Disinvested Unpaid  \n1318      Disinvested Unpaid  \n1319                       -  \n1320                       -  \n\n[1321 rows x 23 columns]","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>Dummy Policy No</th>\n      <th>ASSURED_AGE</th>\n      <th>NOMINEE_RELATION</th>\n      <th>OCCUPATION</th>\n      <th>POLICY SUMASSURED</th>\n      <th>Premium</th>\n      <th>PREMIUMPAYMENTMODE</th>\n      <th>Annual Income</th>\n      <th>HOLDERMARITALSTATUS</th>\n      <th>INDIV_REQUIREMENTFLAG</th>\n      <th>...</th>\n      <th>CORRESPONDENCESTATE</th>\n      <th>CORRESPONDENCEPOSTCODE</th>\n      <th>Product Type</th>\n      <th>CHANNEL</th>\n      <th>Bank code</th>\n      <th>POLICYRISKCOMMENCEMENTDATE</th>\n      <th>Date of Death</th>\n      <th>INTIMATIONDATE</th>\n      <th>STATUS</th>\n      <th>SUB_STATUS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20</td>\n      <td>Mother</td>\n      <td>Service</td>\n      <td>1,200,000</td>\n      <td>120,000</td>\n      <td>Quarterly</td>\n      <td>420,000</td>\n      <td>Single</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Jharkhand</td>\n      <td>816115</td>\n      <td>ULIP</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>30-08-2023</td>\n      <td>26-10-2023</td>\n      <td>12-12-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>33</td>\n      <td>Husband</td>\n      <td>Service</td>\n      <td>350,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>180,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Madhya Pradesh</td>\n      <td>464001</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>02-03-2022</td>\n      <td>31-03-2022</td>\n      <td>10-02-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>34</td>\n      <td>Wife</td>\n      <td>Self-Employed</td>\n      <td>350,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>249,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>West Bengal</td>\n      <td>731238</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>08-07-2022</td>\n      <td>11-11-2022</td>\n      <td>26-06-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>34</td>\n      <td>Wife</td>\n      <td>Service</td>\n      <td>325,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>360,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Uttar Pradesh</td>\n      <td>281504</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>27-12-2022</td>\n      <td>08-04-2023</td>\n      <td>14-08-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>54</td>\n      <td>Husband</td>\n      <td>Housewife</td>\n      <td>100,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>600,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Tamil Nadu</td>\n      <td>606001</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>24-03-2023</td>\n      <td>11-04-2023</td>\n      <td>06-12-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1316</th>\n      <td>1317</td>\n      <td>42</td>\n      <td>Spouse</td>\n      <td>Self-Employed</td>\n      <td>1,750,000</td>\n      <td>250,000</td>\n      <td>Yearly</td>\n      <td>681,600</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Uttar Pradesh</td>\n      <td>231216</td>\n      <td>ULIP</td>\n      <td>Bancassurance</td>\n      <td>1.0</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>Cancellation</td>\n      <td>Other Reason</td>\n    </tr>\n    <tr>\n      <th>1317</th>\n      <td>1318</td>\n      <td>20</td>\n      <td>Son</td>\n      <td>Business</td>\n      <td>300,000</td>\n      <td>15,000</td>\n      <td>Yearly</td>\n      <td>200,000</td>\n      <td>Single</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Madhya Pradesh</td>\n      <td>473001</td>\n      <td>Variable</td>\n      <td>Bancassurance</td>\n      <td>7.0</td>\n      <td>20-03-2013</td>\n      <td>-</td>\n      <td>-</td>\n      <td>Maturity</td>\n      <td>Disinvested Unpaid</td>\n    </tr>\n    <tr>\n      <th>1318</th>\n      <td>1319</td>\n      <td>54</td>\n      <td>Husband</td>\n      <td>Service</td>\n      <td>300,000</td>\n      <td>15,000</td>\n      <td>Yearly</td>\n      <td>1,000,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Madhya Pradesh</td>\n      <td>473001</td>\n      <td>Variable</td>\n      <td>Bancassurance</td>\n      <td>7.0</td>\n      <td>20-03-2013</td>\n      <td>-</td>\n      <td>-</td>\n      <td>Maturity</td>\n      <td>Disinvested Unpaid</td>\n    </tr>\n    <tr>\n      <th>1319</th>\n      <td>1320</td>\n      <td>36</td>\n      <td>Daughter</td>\n      <td>Housewife</td>\n      <td>0</td>\n      <td>1,000,000</td>\n      <td>Single</td>\n      <td>1,200,000</td>\n      <td>widowed</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Haryana</td>\n      <td>121003</td>\n      <td>Pension</td>\n      <td>Bancassurance</td>\n      <td>1.0</td>\n      <td>19-09-2019</td>\n      <td>-</td>\n      <td>-</td>\n      <td>Inforce</td>\n      <td>-</td>\n    </tr>\n    <tr>\n      <th>1320</th>\n      <td>1321</td>\n      <td>36</td>\n      <td>Son</td>\n      <td>Housewife</td>\n      <td>0</td>\n      <td>1,000,000</td>\n      <td>Single</td>\n      <td>1,200,000</td>\n      <td>widowed</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Haryana</td>\n      <td>121003</td>\n      <td>Pension</td>\n      <td>Bancassurance</td>\n      <td>1.0</td>\n      <td>06-01-2020</td>\n      <td>-</td>\n      <td>-</td>\n      <td>Inforce</td>\n      <td>-</td>\n    </tr>\n  </tbody>\n</table>\n<p>1321 rows × 23 columns</p>\n</div>"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"x[:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:44:49.501024Z","iopub.execute_input":"2024-12-27T16:44:49.501440Z","iopub.status.idle":"2024-12-27T16:44:49.525953Z","shell.execute_reply.started":"2024-12-27T16:44:49.501393Z","shell.execute_reply":"2024-12-27T16:44:49.524414Z"}},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"   Dummy Policy No  ASSURED_AGE NOMINEE_RELATION     OCCUPATION  \\\n0                1           20           Mother        Service   \n1                2           33          Husband        Service   \n2                3           34             Wife  Self-Employed   \n3                4           34             Wife        Service   \n4                5           54          Husband      Housewife   \n\n  POLICY SUMASSURED     Premium PREMIUMPAYMENTMODE Annual Income  \\\n0        1,200,000     120,000           Quarterly      420,000    \n1          350,000       5,000              Yearly      180,000    \n2          350,000       5,000              Yearly      249,000    \n3          325,000       5,000              Yearly      360,000    \n4          100,000       5,000              Yearly      600,000    \n\n  HOLDERMARITALSTATUS INDIV_REQUIREMENTFLAG  ...  CORRESPONDENCESTATE  \\\n0              Single           Non Medical  ...            Jharkhand   \n1             Married           Non Medical  ...       Madhya Pradesh   \n2             Married           Non Medical  ...          West Bengal   \n3             Married           Non Medical  ...        Uttar Pradesh   \n4             Married           Non Medical  ...           Tamil Nadu   \n\n   CORRESPONDENCEPOSTCODE Product Type        CHANNEL Bank code  \\\n0                  816115         ULIP  Retail Agency       NaN   \n1                  464001  Traditional  Retail Agency       NaN   \n2                  731238  Traditional  Retail Agency       NaN   \n3                  281504  Traditional  Retail Agency       NaN   \n4                  606001  Traditional  Retail Agency       NaN   \n\n  POLICYRISKCOMMENCEMENTDATE Date of Death  INTIMATIONDATE STATUS  \\\n0                 30-08-2023    26-10-2023      12-12-2023  Claim   \n1                 02-03-2022    31-03-2022      10-02-2023  Claim   \n2                 08-07-2022    11-11-2022      26-06-2023  Claim   \n3                 27-12-2022    08-04-2023      14-08-2023  Claim   \n4                 24-03-2023    11-04-2023      06-12-2023  Claim   \n\n               SUB_STATUS  \n0  Death Claim Repudiated  \n1  Death Claim Repudiated  \n2  Death Claim Repudiated  \n3  Death Claim Repudiated  \n4  Death Claim Repudiated  \n\n[5 rows x 23 columns]","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>Dummy Policy No</th>\n      <th>ASSURED_AGE</th>\n      <th>NOMINEE_RELATION</th>\n      <th>OCCUPATION</th>\n      <th>POLICY SUMASSURED</th>\n      <th>Premium</th>\n      <th>PREMIUMPAYMENTMODE</th>\n      <th>Annual Income</th>\n      <th>HOLDERMARITALSTATUS</th>\n      <th>INDIV_REQUIREMENTFLAG</th>\n      <th>...</th>\n      <th>CORRESPONDENCESTATE</th>\n      <th>CORRESPONDENCEPOSTCODE</th>\n      <th>Product Type</th>\n      <th>CHANNEL</th>\n      <th>Bank code</th>\n      <th>POLICYRISKCOMMENCEMENTDATE</th>\n      <th>Date of Death</th>\n      <th>INTIMATIONDATE</th>\n      <th>STATUS</th>\n      <th>SUB_STATUS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20</td>\n      <td>Mother</td>\n      <td>Service</td>\n      <td>1,200,000</td>\n      <td>120,000</td>\n      <td>Quarterly</td>\n      <td>420,000</td>\n      <td>Single</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Jharkhand</td>\n      <td>816115</td>\n      <td>ULIP</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>30-08-2023</td>\n      <td>26-10-2023</td>\n      <td>12-12-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>33</td>\n      <td>Husband</td>\n      <td>Service</td>\n      <td>350,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>180,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Madhya Pradesh</td>\n      <td>464001</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>02-03-2022</td>\n      <td>31-03-2022</td>\n      <td>10-02-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>34</td>\n      <td>Wife</td>\n      <td>Self-Employed</td>\n      <td>350,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>249,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>West Bengal</td>\n      <td>731238</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>08-07-2022</td>\n      <td>11-11-2022</td>\n      <td>26-06-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>34</td>\n      <td>Wife</td>\n      <td>Service</td>\n      <td>325,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>360,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Uttar Pradesh</td>\n      <td>281504</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>27-12-2022</td>\n      <td>08-04-2023</td>\n      <td>14-08-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>54</td>\n      <td>Husband</td>\n      <td>Housewife</td>\n      <td>100,000</td>\n      <td>5,000</td>\n      <td>Yearly</td>\n      <td>600,000</td>\n      <td>Married</td>\n      <td>Non Medical</td>\n      <td>...</td>\n      <td>Tamil Nadu</td>\n      <td>606001</td>\n      <td>Traditional</td>\n      <td>Retail Agency</td>\n      <td>NaN</td>\n      <td>24-03-2023</td>\n      <td>11-04-2023</td>\n      <td>06-12-2023</td>\n      <td>Claim</td>\n      <td>Death Claim Repudiated</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"y[:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:44:56.880709Z","iopub.execute_input":"2024-12-27T16:44:56.881061Z","iopub.status.idle":"2024-12-27T16:44:56.888437Z","shell.execute_reply.started":"2024-12-27T16:44:56.881033Z","shell.execute_reply":"2024-12-27T16:44:56.887321Z"}},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"0    Misrepresentation\n1    Misrepresentation\n2    Misrepresentation\n3    Misrepresentation\n4    Misrepresentation\nName: Fraud Category, dtype: object"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"all_type_fraud = y.unique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:46:01.826886Z","iopub.execute_input":"2024-12-27T16:46:01.827301Z","iopub.status.idle":"2024-12-27T16:46:01.833462Z","shell.execute_reply.started":"2024-12-27T16:46:01.827236Z","shell.execute_reply":"2024-12-27T16:46:01.832133Z"}},"outputs":[],"execution_count":23},{"cell_type":"code","source":"list(all_type_fraud), len(all_type_fraud)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:46:35.541121Z","iopub.execute_input":"2024-12-27T16:46:35.541658Z","iopub.status.idle":"2024-12-27T16:46:35.548598Z","shell.execute_reply.started":"2024-12-27T16:46:35.541511Z","shell.execute_reply":"2024-12-27T16:46:35.547357Z"}},"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"(['Misrepresentation',\n  'Document Tampering',\n  'Signature Forgery',\n  'Misselling ',\n  'Misappropriating Funds',\n  'Misappropriating funds',\n  'Logging in business not sourced by oneself',\n  'Impersonation',\n  'Claims Fraud',\n  'Kickback',\n  'Agent Dual Pan Card',\n  'Unauthorized activity'],\n 12)"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"all_fields = x.columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:47:30.091246Z","iopub.execute_input":"2024-12-27T16:47:30.091657Z","iopub.status.idle":"2024-12-27T16:47:30.096751Z","shell.execute_reply.started":"2024-12-27T16:47:30.091625Z","shell.execute_reply":"2024-12-27T16:47:30.095175Z"}},"outputs":[],"execution_count":29},{"cell_type":"code","source":"list(all_fields), len(all_fields)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:47:44.426775Z","iopub.execute_input":"2024-12-27T16:47:44.427133Z","iopub.status.idle":"2024-12-27T16:47:44.445208Z","shell.execute_reply.started":"2024-12-27T16:47:44.427105Z","shell.execute_reply":"2024-12-27T16:47:44.444048Z"}},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"(['Dummy Policy No',\n  'ASSURED_AGE',\n  'NOMINEE_RELATION',\n  'OCCUPATION',\n  'POLICY SUMASSURED',\n  'Premium',\n  'PREMIUMPAYMENTMODE',\n  'Annual Income',\n  'HOLDERMARITALSTATUS',\n  'INDIV_REQUIREMENTFLAG',\n  'Policy Term',\n  'Policy Payment Term',\n  'CORRESPONDENCECITY',\n  'CORRESPONDENCESTATE',\n  'CORRESPONDENCEPOSTCODE',\n  'Product Type',\n  'CHANNEL',\n  'Bank code',\n  'POLICYRISKCOMMENCEMENTDATE',\n  'Date of Death',\n  'INTIMATIONDATE',\n  'STATUS',\n  'SUB_STATUS'],\n 23)"},"metadata":{}}],"execution_count":30},{"cell_type":"code","source":"data.columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:50:07.010387Z","iopub.execute_input":"2024-12-27T16:50:07.010945Z","iopub.status.idle":"2024-12-27T16:50:07.019418Z","shell.execute_reply.started":"2024-12-27T16:50:07.010906Z","shell.execute_reply":"2024-12-27T16:50:07.018130Z"}},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"Index(['Dummy Policy No', 'ASSURED_AGE', 'NOMINEE_RELATION', 'OCCUPATION',\n       'POLICY SUMASSURED', 'Premium', 'PREMIUMPAYMENTMODE', 'Annual Income',\n       'HOLDERMARITALSTATUS', 'INDIV_REQUIREMENTFLAG', 'Policy Term',\n       'Policy Payment Term', 'CORRESPONDENCECITY', 'CORRESPONDENCESTATE',\n       'CORRESPONDENCEPOSTCODE', 'Product Type', 'CHANNEL', 'Bank code',\n       'POLICYRISKCOMMENCEMENTDATE', 'Date of Death', 'INTIMATIONDATE',\n       'STATUS', 'SUB_STATUS', 'Fraud Category'],\n      dtype='object')"},"metadata":{}}],"execution_count":31},{"cell_type":"markdown","source":"## Converting text data in numbers","metadata":{}},{"cell_type":"code","source":"categorical_cols = data.select_dtypes(include=['object', 'category']).columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:54:09.974812Z","iopub.execute_input":"2024-12-27T16:54:09.975287Z","iopub.status.idle":"2024-12-27T16:54:09.982612Z","shell.execute_reply.started":"2024-12-27T16:54:09.975236Z","shell.execute_reply":"2024-12-27T16:54:09.980868Z"}},"outputs":[],"execution_count":36},{"cell_type":"code","source":"data_encoded = pd.get_dummies(data, columns=categorical_cols)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:54:37.069996Z","iopub.execute_input":"2024-12-27T16:54:37.070374Z","iopub.status.idle":"2024-12-27T16:54:37.113074Z","shell.execute_reply.started":"2024-12-27T16:54:37.070343Z","shell.execute_reply":"2024-12-27T16:54:37.111717Z"}},"outputs":[],"execution_count":38},{"cell_type":"code","source":"from sklearn.preprocessing import OrdinalEncoder\n\nordinal_encoder = OrdinalEncoder()\ndata[categorical_cols] = ordinal_encoder.fit_transform(data[categorical_cols])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:55:04.836443Z","iopub.execute_input":"2024-12-27T16:55:04.836947Z","iopub.status.idle":"2024-12-27T16:55:04.874387Z","shell.execute_reply.started":"2024-12-27T16:55:04.836912Z","shell.execute_reply":"2024-12-27T16:55:04.872841Z"}},"outputs":[],"execution_count":39},{"cell_type":"code","source":"data","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:55:09.474560Z","iopub.execute_input":"2024-12-27T16:55:09.474964Z","iopub.status.idle":"2024-12-27T16:55:09.518895Z","shell.execute_reply.started":"2024-12-27T16:55:09.474929Z","shell.execute_reply":"2024-12-27T16:55:09.517586Z"}},"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"      Dummy Policy No  ASSURED_AGE  NOMINEE_RELATION  OCCUPATION  \\\n0                   1           20               6.0        11.0   \n1                   2           33               5.0        11.0   \n2                   3           34              12.0        10.0   \n3                   4           34              12.0        11.0   \n4                   5           54               5.0         6.0   \n...               ...          ...               ...         ...   \n1316             1317           42              11.0        10.0   \n1317             1318           20              10.0         2.0   \n1318             1319           54               5.0        11.0   \n1319             1320           36               1.0         6.0   \n1320             1321           36              10.0         6.0   \n\n      POLICY SUMASSURED  Premium  PREMIUMPAYMENTMODE  Annual Income  \\\n0                  11.0     39.0                 2.0          110.0   \n1                 105.0    315.0                 4.0           45.0   \n2                 105.0    315.0                 4.0           72.0   \n3                 101.0    315.0                 4.0           97.0   \n4                  40.0    315.0                 4.0          139.0   \n...                 ...      ...                 ...            ...   \n1316               29.0    155.0                 4.0          152.0   \n1317               98.0     51.0                 4.0           59.0   \n1318               98.0     51.0                 4.0            0.0   \n1319              182.0      0.0                 3.0           12.0   \n1320              182.0      0.0                 3.0           12.0   \n\n      HOLDERMARITALSTATUS  INDIV_REQUIREMENTFLAG  ...  CORRESPONDENCEPOSTCODE  \\\n0                     1.0                    1.0  ...                   853.0   \n1                     0.0                    1.0  ...                   382.0   \n2                     0.0                    1.0  ...                   673.0   \n3                     0.0                    1.0  ...                   237.0   \n4                     0.0                    1.0  ...                   598.0   \n...                   ...                    ...  ...                     ...   \n1316                  0.0                    1.0  ...                   183.0   \n1317                  1.0                    1.0  ...                   392.0   \n1318                  0.0                    1.0  ...                   392.0   \n1319                  3.0                    1.0  ...                    21.0   \n1320                  3.0                    1.0  ...                    21.0   \n\n      Product Type  CHANNEL  Bank code  POLICYRISKCOMMENCEMENTDATE  \\\n0              4.0      3.0        NaN                       533.0   \n1              3.0      3.0        NaN                        14.0   \n2              3.0      3.0        NaN                       119.0   \n3              3.0      3.0        NaN                       484.0   \n4              3.0      3.0        NaN                       406.0   \n...            ...      ...        ...                         ...   \n1316           4.0      0.0        1.0                         0.0   \n1317           5.0      0.0        7.0                       319.0   \n1318           5.0      0.0        7.0                       319.0   \n1319           2.0      0.0        1.0                       310.0   \n1320           2.0      0.0        1.0                        74.0   \n\n      Date of Death  INTIMATIONDATE  STATUS  SUB_STATUS  Fraud Category  \n0              47.0            23.0     1.0         3.0             8.0  \n1              53.0            19.0     1.0         3.0             8.0  \n2              20.0            43.0     1.0         3.0             8.0  \n3              12.0            24.0     1.0         3.0             8.0  \n4              17.0            17.0     1.0         3.0             8.0  \n...             ...             ...     ...         ...             ...  \n1316            0.0             0.0     0.0        11.0             2.0  \n1317            0.0             0.0     4.0         7.0             9.0  \n1318            0.0             0.0     4.0         7.0             9.0  \n1319            0.0             0.0     2.0         1.0            10.0  \n1320            0.0             0.0     2.0         1.0            10.0  \n\n[1321 rows x 24 columns]","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>Dummy Policy No</th>\n      <th>ASSURED_AGE</th>\n      <th>NOMINEE_RELATION</th>\n      <th>OCCUPATION</th>\n      <th>POLICY SUMASSURED</th>\n      <th>Premium</th>\n      <th>PREMIUMPAYMENTMODE</th>\n      <th>Annual Income</th>\n      <th>HOLDERMARITALSTATUS</th>\n      <th>INDIV_REQUIREMENTFLAG</th>\n      <th>...</th>\n      <th>CORRESPONDENCEPOSTCODE</th>\n      <th>Product Type</th>\n      <th>CHANNEL</th>\n      <th>Bank code</th>\n      <th>POLICYRISKCOMMENCEMENTDATE</th>\n      <th>Date of Death</th>\n      <th>INTIMATIONDATE</th>\n      <th>STATUS</th>\n      <th>SUB_STATUS</th>\n      <th>Fraud Category</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20</td>\n      <td>6.0</td>\n      <td>11.0</td>\n      <td>11.0</td>\n      <td>39.0</td>\n      <td>2.0</td>\n      <td>110.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>853.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>533.0</td>\n      <td>47.0</td>\n      <td>23.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>33</td>\n      <td>5.0</td>\n      <td>11.0</td>\n      <td>105.0</td>\n      <td>315.0</td>\n      <td>4.0</td>\n      <td>45.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>382.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>14.0</td>\n      <td>53.0</td>\n      <td>19.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>34</td>\n      <td>12.0</td>\n      <td>10.0</td>\n      <td>105.0</td>\n      <td>315.0</td>\n      <td>4.0</td>\n      <td>72.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>673.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>119.0</td>\n      <td>20.0</td>\n      <td>43.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>34</td>\n      <td>12.0</td>\n      <td>11.0</td>\n      <td>101.0</td>\n      <td>315.0</td>\n      <td>4.0</td>\n      <td>97.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>237.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>484.0</td>\n      <td>12.0</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>8.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>54</td>\n      <td>5.0</td>\n      <td>6.0</td>\n      <td>40.0</td>\n      <td>315.0</td>\n      <td>4.0</td>\n      <td>139.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>598.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>406.0</td>\n      <td>17.0</td>\n      <td>17.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>8.0</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      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1316</th>\n      <td>1317</td>\n      <td>42</td>\n      <td>11.0</td>\n      <td>10.0</td>\n      <td>29.0</td>\n      <td>155.0</td>\n      <td>4.0</td>\n      <td>152.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>183.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>1317</th>\n      <td>1318</td>\n      <td>20</td>\n      <td>10.0</td>\n      <td>2.0</td>\n      <td>98.0</td>\n      <td>51.0</td>\n      <td>4.0</td>\n      <td>59.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>392.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>319.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>1318</th>\n      <td>1319</td>\n      <td>54</td>\n      <td>5.0</td>\n      <td>11.0</td>\n      <td>98.0</td>\n      <td>51.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>392.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>319.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>1319</th>\n      <td>1320</td>\n      <td>36</td>\n      <td>1.0</td>\n      <td>6.0</td>\n      <td>182.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>12.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>21.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>310.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>1320</th>\n      <td>1321</td>\n      <td>36</td>\n      <td>10.0</td>\n      <td>6.0</td>\n      <td>182.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>12.0</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>21.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>74.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>10.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1321 rows × 24 columns</p>\n</div>"},"metadata":{}}],"execution_count":40},{"cell_type":"code","source":"x = data.drop('Fraud Category', axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:55:31.892287Z","iopub.execute_input":"2024-12-27T16:55:31.892964Z","iopub.status.idle":"2024-12-27T16:55:31.902677Z","shell.execute_reply.started":"2024-12-27T16:55:31.892908Z","shell.execute_reply":"2024-12-27T16:55:31.900431Z"}},"outputs":[],"execution_count":41},{"cell_type":"code","source":"y = data['Fraud Category']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:55:42.886740Z","iopub.execute_input":"2024-12-27T16:55:42.887165Z","iopub.status.idle":"2024-12-27T16:55:42.895993Z","shell.execute_reply.started":"2024-12-27T16:55:42.887134Z","shell.execute_reply":"2024-12-27T16:55:42.892170Z"}},"outputs":[],"execution_count":42},{"cell_type":"code","source":"x.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:55:48.817457Z","iopub.execute_input":"2024-12-27T16:55:48.818171Z","iopub.status.idle":"2024-12-27T16:55:48.875850Z","shell.execute_reply.started":"2024-12-27T16:55:48.818119Z","shell.execute_reply":"2024-12-27T16:55:48.873138Z"}},"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"   Dummy Policy No  ASSURED_AGE  NOMINEE_RELATION  OCCUPATION  \\\n0                1           20               6.0        11.0   \n1                2           33               5.0        11.0   \n2                3           34              12.0        10.0   \n3                4           34              12.0        11.0   \n4                5           54               5.0         6.0   \n\n   POLICY SUMASSURED  Premium  PREMIUMPAYMENTMODE  Annual Income  \\\n0               11.0     39.0                 2.0          110.0   \n1              105.0    315.0                 4.0           45.0   \n2              105.0    315.0                 4.0           72.0   \n3              101.0    315.0                 4.0           97.0   \n4               40.0    315.0                 4.0          139.0   \n\n   HOLDERMARITALSTATUS  INDIV_REQUIREMENTFLAG  ...  CORRESPONDENCESTATE  \\\n0                  1.0                    1.0  ...                 13.0   \n1                  0.0                    1.0  ...                 16.0   \n2                  0.0                    1.0  ...                 31.0   \n3                  0.0                    1.0  ...                 29.0   \n4                  0.0                    1.0  ...                 26.0   \n\n   CORRESPONDENCEPOSTCODE  Product Type  CHANNEL  Bank code  \\\n0                   853.0           4.0      3.0        NaN   \n1                   382.0           3.0      3.0        NaN   \n2                   673.0           3.0      3.0        NaN   \n3                   237.0           3.0      3.0        NaN   \n4                   598.0           3.0      3.0        NaN   \n\n   POLICYRISKCOMMENCEMENTDATE  Date of Death  INTIMATIONDATE  STATUS  \\\n0                       533.0           47.0            23.0     1.0   \n1                        14.0           53.0            19.0     1.0   \n2                       119.0           20.0            43.0     1.0   \n3                       484.0           12.0            24.0     1.0   \n4                       406.0           17.0            17.0     1.0   \n\n   SUB_STATUS  \n0         3.0  \n1         3.0  \n2         3.0  \n3         3.0  \n4         3.0  \n\n[5 rows x 23 columns]","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>Dummy Policy No</th>\n      <th>ASSURED_AGE</th>\n      <th>NOMINEE_RELATION</th>\n      <th>OCCUPATION</th>\n      <th>POLICY SUMASSURED</th>\n      <th>Premium</th>\n      <th>PREMIUMPAYMENTMODE</th>\n      <th>Annual Income</th>\n      <th>HOLDERMARITALSTATUS</th>\n      <th>INDIV_REQUIREMENTFLAG</th>\n      <th>...</th>\n      <th>CORRESPONDENCESTATE</th>\n      <th>CORRESPONDENCEPOSTCODE</th>\n      <th>Product Type</th>\n      <th>CHANNEL</th>\n      <th>Bank code</th>\n      <th>POLICYRISKCOMMENCEMENTDATE</th>\n      <th>Date of Death</th>\n      <th>INTIMATIONDATE</th>\n      <th>STATUS</th>\n      <th>SUB_STATUS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20</td>\n      <td>6.0</td>\n      <td>11.0</td>\n      <td>11.0</td>\n      <td>39.0</td>\n      <td>2.0</td>\n      <td>110.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>13.0</td>\n      <td>853.0</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>533.0</td>\n      <td>47.0</td>\n      <td>23.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>33</td>\n      <td>5.0</td>\n      <td>11.0</td>\n      <td>105.0</td>\n      <td>315.0</td>\n      <td>4.0</td>\n      <td>45.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>16.0</td>\n      <td>382.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>14.0</td>\n      <td>53.0</td>\n      <td>19.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>34</td>\n      <td>12.0</td>\n      <td>10.0</td>\n      <td>105.0</td>\n      <td>315.0</td>\n      <td>4.0</td>\n      <td>72.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>31.0</td>\n      <td>673.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>119.0</td>\n      <td>20.0</td>\n      <td>43.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>34</td>\n      <td>12.0</td>\n      <td>11.0</td>\n      <td>101.0</td>\n      <td>315.0</td>\n      <td>4.0</td>\n      <td>97.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>29.0</td>\n      <td>237.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>484.0</td>\n      <td>12.0</td>\n      <td>24.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>54</td>\n      <td>5.0</td>\n      <td>6.0</td>\n      <td>40.0</td>\n      <td>315.0</td>\n      <td>4.0</td>\n      <td>139.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>26.0</td>\n      <td>598.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>406.0</td>\n      <td>17.0</td>\n      <td>17.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"},"metadata":{}}],"execution_count":43},{"cell_type":"code","source":"y.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:55:55.486791Z","iopub.execute_input":"2024-12-27T16:55:55.487133Z","iopub.status.idle":"2024-12-27T16:55:55.494874Z","shell.execute_reply.started":"2024-12-27T16:55:55.487106Z","shell.execute_reply":"2024-12-27T16:55:55.493700Z"}},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"0    8.0\n1    8.0\n2    8.0\n3    8.0\n4    8.0\nName: Fraud Category, dtype: float64"},"metadata":{}}],"execution_count":44},{"cell_type":"markdown","source":"## Let's use XGBoost model for the prediction of the given data","metadata":{}},{"cell_type":"code","source":"!pip install xgboost","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:51:35.626099Z","iopub.execute_input":"2024-12-27T16:51:35.627113Z","iopub.status.idle":"2024-12-27T16:51:41.915022Z","shell.execute_reply.started":"2024-12-27T16:51:35.627050Z","shell.execute_reply":"2024-12-27T16:51:41.913376Z"}},"outputs":[{"name":"stdout","text":"Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (2.0.3)\nRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.26.4)\nRequirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.13.1)\n","output_type":"stream"}],"execution_count":32},{"cell_type":"code","source":"from xgboost import XGBClassifier, XGBRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:56:00.120481Z","iopub.execute_input":"2024-12-27T16:56:00.120948Z","iopub.status.idle":"2024-12-27T16:56:00.128997Z","shell.execute_reply.started":"2024-12-27T16:56:00.120910Z","shell.execute_reply":"2024-12-27T16:56:00.126845Z"}},"outputs":[],"execution_count":45},{"cell_type":"code","source":"# Train-test split\nx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=42)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:56:04.499920Z","iopub.execute_input":"2024-12-27T16:56:04.500282Z","iopub.status.idle":"2024-12-27T16:56:04.510874Z","shell.execute_reply.started":"2024-12-27T16:56:04.500235Z","shell.execute_reply":"2024-12-27T16:56:04.509010Z"}},"outputs":[],"execution_count":46},{"cell_type":"code","source":"\n# Create an XGBoost classifier\nmodel = XGBClassifier(use_label_encoder=False, eval_metric='logloss')\n\n# Train the model\nmodel.fit(x_train, y_train)\n\n# Predict\ny_pred = model.predict(x_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:56:07.714616Z","iopub.execute_input":"2024-12-27T16:56:07.714982Z","iopub.status.idle":"2024-12-27T16:56:08.230196Z","shell.execute_reply.started":"2024-12-27T16:56:07.714955Z","shell.execute_reply":"2024-12-27T16:56:08.229352Z"}},"outputs":[],"execution_count":47},{"cell_type":"code","source":"# Evaluate\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"Accuracy: {accuracy:.2f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:56:28.010457Z","iopub.execute_input":"2024-12-27T16:56:28.010994Z","iopub.status.idle":"2024-12-27T16:56:28.020170Z","shell.execute_reply.started":"2024-12-27T16:56:28.010960Z","shell.execute_reply":"2024-12-27T16:56:28.018741Z"}},"outputs":[{"name":"stdout","text":"Accuracy: 0.92\n","output_type":"stream"}],"execution_count":48},{"cell_type":"markdown","source":"### Grid Search for Hyperparameter Tuning","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import GridSearchCV\n\nparam_grid = {\n    'n_estimators': [100, 200],\n    'max_depth': [3, 5],\n    'learning_rate': [0.1, 0.01]\n}\n\ngrid = GridSearchCV(estimator=XGBClassifier(eval_metric='logloss'), param_grid=param_grid, scoring='accuracy', cv=3)\ngrid.fit(x_train, y_train)\n\nprint(f\"Best Parameters: {grid.best_params_}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:57:03.164432Z","iopub.execute_input":"2024-12-27T16:57:03.164776Z","iopub.status.idle":"2024-12-27T16:57:09.067159Z","shell.execute_reply.started":"2024-12-27T16:57:03.164749Z","shell.execute_reply":"2024-12-27T16:57:09.066068Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_split.py:700: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=3.\n  warnings.warn(\n/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n16 fits failed out of a total of 24.\nThe score on these train-test partitions for these parameters will be set to nan.\nIf these failures are not expected, you can try to debug them by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n8 fits failed with the following error:\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/usr/local/lib/python3.10/dist-packages/xgboost/core.py\", line 730, in inner_f\n    return func(**kwargs)\n  File \"/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py\", line 1471, in fit\n    raise ValueError(\nValueError: Invalid classes inferred from unique values of `y`.  Expected: [ 0  1  2  3  4  5  6  7  8  9 10], got [ 0.  1.  2.  3.  5.  6.  7.  8.  9. 10. 11.]\n\n--------------------------------------------------------------------------------\n8 fits failed with the following error:\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/usr/local/lib/python3.10/dist-packages/xgboost/core.py\", line 730, in inner_f\n    return func(**kwargs)\n  File \"/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py\", line 1471, in fit\n    raise ValueError(\nValueError: Invalid classes inferred from unique values of `y`.  Expected: [0 1 2 3 4 5 6 7 8 9], got [ 1.  2.  3.  4.  6.  7.  8.  9. 10. 11.]\n\n  warnings.warn(some_fits_failed_message, FitFailedWarning)\n/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [nan nan nan nan nan nan nan nan]\n  warnings.warn(\n","output_type":"stream"},{"name":"stdout","text":"Best Parameters: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100}\n","output_type":"stream"}],"execution_count":49},{"cell_type":"code","source":"\n# Create an XGBoost classifier\nmodel = XGBClassifier(use_label_encoder=False, eval_metric='logloss', n_estimators =100, max_depth=3, learning_rate=0.1)\n\n# Train the model\nmodel.fit(x_train, y_train)\n\n# Predict\ny_pred = model.predict(x_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T16:59:53.990340Z","iopub.execute_input":"2024-12-27T16:59:53.990853Z","iopub.status.idle":"2024-12-27T16:59:54.430871Z","shell.execute_reply.started":"2024-12-27T16:59:53.990787Z","shell.execute_reply":"2024-12-27T16:59:54.430046Z"}},"outputs":[],"execution_count":52},{"cell_type":"code","source":"accuracy = accuracy_score(y_test, y_pred)\nprint(f\"Test Accuracy: {accuracy:.2f}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T17:00:08.185292Z","iopub.execute_input":"2024-12-27T17:00:08.185664Z","iopub.status.idle":"2024-12-27T17:00:08.193143Z","shell.execute_reply.started":"2024-12-27T17:00:08.185634Z","shell.execute_reply":"2024-12-27T17:00:08.192160Z"}},"outputs":[{"name":"stdout","text":"Test Accuracy: 0.94\n","output_type":"stream"}],"execution_count":53},{"cell_type":"code","source":"from sklearn.metrics import precision_score\nprecision = precision_score(y_test, y_pred, average='weighted')\nprint(f\"Precision: {precision:.2f}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T17:00:42.245054Z","iopub.execute_input":"2024-12-27T17:00:42.245451Z","iopub.status.idle":"2024-12-27T17:00:42.261239Z","shell.execute_reply.started":"2024-12-27T17:00:42.245416Z","shell.execute_reply":"2024-12-27T17:00:42.259882Z"}},"outputs":[{"name":"stdout","text":"Precision: 0.90\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n  _warn_prf(average, modifier, msg_start, len(result))\n","output_type":"stream"}],"execution_count":54},{"cell_type":"code","source":"from sklearn.metrics import recall_score\nrecall = recall_score(y_test, y_pred, average='weighted')\nprint(f\"Recall: {recall:.2f}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T17:00:51.500687Z","iopub.execute_input":"2024-12-27T17:00:51.501033Z","iopub.status.idle":"2024-12-27T17:00:51.512216Z","shell.execute_reply.started":"2024-12-27T17:00:51.501006Z","shell.execute_reply":"2024-12-27T17:00:51.510755Z"}},"outputs":[{"name":"stdout","text":"Recall: 0.94\n","output_type":"stream"},{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n  _warn_prf(average, modifier, msg_start, len(result))\n","output_type":"stream"}],"execution_count":55},{"cell_type":"code","source":"from sklearn.metrics import f1_score\nf1 = f1_score(y_test, y_pred, average='weighted')\nprint(f\"F1 Score: {f1:.2f}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T17:00:59.005745Z","iopub.execute_input":"2024-12-27T17:00:59.006137Z","iopub.status.idle":"2024-12-27T17:00:59.018072Z","shell.execute_reply.started":"2024-12-27T17:00:59.006105Z","shell.execute_reply":"2024-12-27T17:00:59.016752Z"}},"outputs":[{"name":"stdout","text":"F1 Score: 0.92\n","output_type":"stream"}],"execution_count":56},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ncm = confusion_matrix(y_test, y_pred)\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\nplt.xlabel('Predicted')\nplt.ylabel('Actual')\nplt.title('Confusion Matrix')\nplt.show()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-27T17:02:15.311461Z","iopub.execute_input":"2024-12-27T17:02:15.311835Z","iopub.status.idle":"2024-12-27T17:02:16.213198Z","shell.execute_reply.started":"2024-12-27T17:02:15.311807Z","shell.execute_reply":"2024-12-27T17:02:16.211989Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":61},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}