{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"competition","sourceId":50160,"databundleVersionId":7921029,"isSourceIdPinned":false}],"dockerImageVersionId":31328,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# =========================================================\n# IMPORT LIBRARIES\n# =========================================================\n\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.impute import SimpleImputer\n\nfrom sklearn.metrics import (\n    roc_auc_score,\n    classification_report,\n    confusion_matrix\n)\n\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout\nfrom tensorflow.keras.callbacks import EarlyStopping\nfrom tensorflow.keras.regularizers import l2\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:21:18.06652Z","iopub.execute_input":"2026-05-13T19:21:18.066767Z","iopub.status.idle":"2026-05-13T19:21:18.07199Z","shell.execute_reply.started":"2026-05-13T19:21:18.066747Z","shell.execute_reply":"2026-05-13T19:21:18.071296Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"# =========================================================\n# LOAD BASE DATA\n# =========================================================\n\nbase = pd.read_csv(\n    \"/kaggle/input/competitions/home-credit-credit-risk-model-stability/csv_files/train/train_base.csv\"\n)\n\nprint(\"Shape of base table:\", base.shape)\n\nbase.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:23:35.89004Z","iopub.execute_input":"2026-05-13T19:23:35.89028Z","iopub.status.idle":"2026-05-13T19:23:36.722219Z","shell.execute_reply.started":"2026-05-13T19:23:35.890261Z","shell.execute_reply":"2026-05-13T19:23:36.721587Z"}},"outputs":[{"name":"stdout","text":"Shape of base table: (1526659, 5)\n","output_type":"stream"},{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"   case_id date_decision   MONTH  WEEK_NUM  target\n0        0    2019-01-03  201901         0       0\n1        1    2019-01-03  201901         0       0\n2        2    2019-01-04  201901         0       0\n3        3    2019-01-03  201901         0       0\n4        4    2019-01-04  201901         0       1","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>case_id</th>\n      <th>date_decision</th>\n      <th>MONTH</th>\n      <th>WEEK_NUM</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2019-01-04</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>2019-01-04</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"# =========================================================\n# TARGET DISTRIBUTION\n# =========================================================\n\nprint(base[\"target\"].value_counts())\n\nprint(\"\\nPercentage:\\n\")\nprint(base[\"target\"].value_counts(normalize=True) * 100)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:24:10.546151Z","iopub.execute_input":"2026-05-13T19:24:10.546404Z","iopub.status.idle":"2026-05-13T19:24:10.570092Z","shell.execute_reply.started":"2026-05-13T19:24:10.546383Z","shell.execute_reply":"2026-05-13T19:24:10.569216Z"}},"outputs":[{"name":"stdout","text":"target\n0    1478665\n1      47994\nName: count, dtype: int64\n\nPercentage:\n\ntarget\n0    96.856272\n1     3.143728\nName: proportion, dtype: float64\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"# =========================================================\n# LOAD STATIC FEATURES\n# =========================================================\n\nstatic_0 = pd.read_csv(\n    \"/kaggle/input/competitions/home-credit-credit-risk-model-stability/csv_files/train/train_static_0_0.csv\"\n)\n\nprint(\"Shape:\", static_0.shape)\n\nstatic_0.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:24:48.669362Z","iopub.execute_input":"2026-05-13T19:24:48.669619Z","iopub.status.idle":"2026-05-13T19:25:05.69716Z","shell.execute_reply.started":"2026-05-13T19:24:48.669598Z","shell.execute_reply":"2026-05-13T19:25:05.696521Z"}},"outputs":[{"name":"stdout","text":"Shape: (1003757, 168)\n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"   case_id  actualdpdtolerance_344P  amtinstpaidbefduel24m_4187115A  \\\n0        0                      NaN                             NaN   \n1        1                      NaN                             NaN   \n2        2                      NaN                             NaN   \n3        3                      NaN                             NaN   \n4        4                      NaN                             NaN   \n\n   annuity_780A  annuitynextmonth_57A  applicationcnt_361L  \\\n0        1917.6                   0.0                  0.0   \n1        3134.0                   0.0                  0.0   \n2        4937.0                   0.0                  0.0   \n3        4643.6                   0.0                  0.0   \n4        3390.2                   0.0                  0.0   \n\n   applications30d_658L  applicationscnt_1086L  applicationscnt_464L  \\\n0                   0.0                    0.0                   0.0   \n1                   0.0                    0.0                   0.0   \n2                   0.0                    0.0                   0.0   \n3                   1.0                    0.0                   2.0   \n4                   1.0                    0.0                   0.0   \n\n   applicationscnt_629L  ...  sellerplacecnt_915L  sellerplacescnt_216L  \\\n0                   0.0  ...                  0.0                   0.0   \n1                   0.0  ...                  0.0                   0.0   \n2                   0.0  ...                  0.0                   0.0   \n3                   0.0  ...                  1.0                   1.0   \n4                   0.0  ...                  0.0                   0.0   \n\n   sumoutstandtotal_3546847A  sumoutstandtotalest_4493215A  totaldebt_9A  \\\n0                        NaN                           NaN           0.0   \n1                        NaN                           NaN           0.0   \n2                        NaN                           NaN           0.0   \n3                        NaN                           NaN           0.0   \n4                        NaN                           NaN           0.0   \n\n   totalsettled_863A  totinstallast1m_4525188A  twobodfilling_608L  \\\n0                0.0                       NaN                  BO   \n1                0.0                       NaN                  BO   \n2                0.0                       NaN                  BO   \n3                0.0                       NaN                  BO   \n4                0.0                       NaN                  BO   \n\n   typesuite_864L  validfrom_1069D  \n0             NaN              NaN  \n1             NaN              NaN  \n2              AL              NaN  \n3              AL              NaN  \n4              AL              NaN  \n\n[5 rows x 168 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>case_id</th>\n      <th>actualdpdtolerance_344P</th>\n      <th>amtinstpaidbefduel24m_4187115A</th>\n      <th>annuity_780A</th>\n      <th>annuitynextmonth_57A</th>\n      <th>applicationcnt_361L</th>\n      <th>applications30d_658L</th>\n      <th>applicationscnt_1086L</th>\n      <th>applicationscnt_464L</th>\n      <th>applicationscnt_629L</th>\n      <th>...</th>\n      <th>sellerplacecnt_915L</th>\n      <th>sellerplacescnt_216L</th>\n      <th>sumoutstandtotal_3546847A</th>\n      <th>sumoutstandtotalest_4493215A</th>\n      <th>totaldebt_9A</th>\n      <th>totalsettled_863A</th>\n      <th>totinstallast1m_4525188A</th>\n      <th>twobodfilling_608L</th>\n      <th>typesuite_864L</th>\n      <th>validfrom_1069D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1917.6</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3134.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4937.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>AL</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4643.6</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>AL</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3390.2</td>\n      <td>0.0</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>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>AL</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 168 columns</p>\n</div>"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"# =========================================================\n# MERGE BASE + STATIC FEATURES\n# =========================================================\n\ndf = base.merge(static_0, on=\"case_id\", how=\"left\")\n\nprint(\"Merged Shape:\", df.shape)\n\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:25:34.506906Z","iopub.execute_input":"2026-05-13T19:25:34.507154Z","iopub.status.idle":"2026-05-13T19:25:37.729475Z","shell.execute_reply.started":"2026-05-13T19:25:34.507133Z","shell.execute_reply":"2026-05-13T19:25:37.728837Z"}},"outputs":[{"name":"stdout","text":"Merged Shape: (1526659, 172)\n","output_type":"stream"},{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"   case_id date_decision   MONTH  WEEK_NUM  target  actualdpdtolerance_344P  \\\n0        0    2019-01-03  201901         0       0                      NaN   \n1        1    2019-01-03  201901         0       0                      NaN   \n2        2    2019-01-04  201901         0       0                      NaN   \n3        3    2019-01-03  201901         0       0                      NaN   \n4        4    2019-01-04  201901         0       1                      NaN   \n\n   amtinstpaidbefduel24m_4187115A  annuity_780A  annuitynextmonth_57A  \\\n0                             NaN        1917.6                   0.0   \n1                             NaN        3134.0                   0.0   \n2                             NaN        4937.0                   0.0   \n3                             NaN        4643.6                   0.0   \n4                             NaN        3390.2                   0.0   \n\n   applicationcnt_361L  ...  sellerplacecnt_915L  sellerplacescnt_216L  \\\n0                  0.0  ...                  0.0                   0.0   \n1                  0.0  ...                  0.0                   0.0   \n2                  0.0  ...                  0.0                   0.0   \n3                  0.0  ...                  1.0                   1.0   \n4                  0.0  ...                  0.0                   0.0   \n\n   sumoutstandtotal_3546847A  sumoutstandtotalest_4493215A  totaldebt_9A  \\\n0                        NaN                           NaN           0.0   \n1                        NaN                           NaN           0.0   \n2                        NaN                           NaN           0.0   \n3                        NaN                           NaN           0.0   \n4                        NaN                           NaN           0.0   \n\n   totalsettled_863A  totinstallast1m_4525188A  twobodfilling_608L  \\\n0                0.0                       NaN                  BO   \n1                0.0                       NaN                  BO   \n2                0.0                       NaN                  BO   \n3                0.0                       NaN                  BO   \n4                0.0                       NaN                  BO   \n\n   typesuite_864L  validfrom_1069D  \n0             NaN              NaN  \n1             NaN              NaN  \n2              AL              NaN  \n3              AL              NaN  \n4              AL              NaN  \n\n[5 rows x 172 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>case_id</th>\n      <th>date_decision</th>\n      <th>MONTH</th>\n      <th>WEEK_NUM</th>\n      <th>target</th>\n      <th>actualdpdtolerance_344P</th>\n      <th>amtinstpaidbefduel24m_4187115A</th>\n      <th>annuity_780A</th>\n      <th>annuitynextmonth_57A</th>\n      <th>applicationcnt_361L</th>\n      <th>...</th>\n      <th>sellerplacecnt_915L</th>\n      <th>sellerplacescnt_216L</th>\n      <th>sumoutstandtotal_3546847A</th>\n      <th>sumoutstandtotalest_4493215A</th>\n      <th>totaldebt_9A</th>\n      <th>totalsettled_863A</th>\n      <th>totinstallast1m_4525188A</th>\n      <th>twobodfilling_608L</th>\n      <th>typesuite_864L</th>\n      <th>validfrom_1069D</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1917.6</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3134.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>2019-01-04</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4937.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>AL</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>2019-01-03</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4643.6</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>AL</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>2019-01-04</td>\n      <td>201901</td>\n      <td>0</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3390.2</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>BO</td>\n      <td>AL</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 172 columns</p>\n</div>"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"# =========================================================\n# CHECK MISSING VALUES\n# =========================================================\n\nmissing_percent = (df.isnull().sum() / len(df)) * 100\n\nmissing_df = pd.DataFrame({\n    \"Column\": missing_percent.index,\n    \"Missing %\": missing_percent.values\n})\n\nmissing_df = missing_df.sort_values(\n    by=\"Missing %\",\n    ascending=False\n)\n\nmissing_df.head(20)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:26:08.07517Z","iopub.execute_input":"2026-05-13T19:26:08.075417Z","iopub.status.idle":"2026-05-13T19:26:09.74902Z","shell.execute_reply.started":"2026-05-13T19:26:08.075397Z","shell.execute_reply":"2026-05-13T19:26:09.748281Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"                           Column  Missing %\n33                clientscnt_136L  99.973996\n150  payvacationpostpone_4187118D  99.903908\n88          lastrepayingdate_696D  99.894868\n81       lastotherlnsexpense_631A  99.857794\n80              lastotherinc_902A  99.854060\n69       isbidproductrequest_292L  99.444801\n67          interestrategrace_34L  98.913772\n79         lastdependentsnum_448L  98.358638\n60            equalityempfrom_62L  98.139401\n94            maxannuity_4075009A  96.716883\n59     equalitydataagreement_891L  96.573433\n20      avglnamtstart24m_4525187A  95.427466\n50       datelastinstal40dpd_247D  95.317029\n171               validfrom_1069D  92.178673\n25                   cardtype_51L  91.760832\n70               isdebitcard_729L  91.755264\n64     inittransactionamount_650A  91.755264\n168      totinstallast1m_4525188A  90.915326\n111         maxpmtlast3m_4525190A  89.665931\n109      maxlnamtstart6m_4525199A  87.142119","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>Column</th>\n      <th>Missing %</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>33</th>\n      <td>clientscnt_136L</td>\n      <td>99.973996</td>\n    </tr>\n    <tr>\n      <th>150</th>\n      <td>payvacationpostpone_4187118D</td>\n      <td>99.903908</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>lastrepayingdate_696D</td>\n      <td>99.894868</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>lastotherlnsexpense_631A</td>\n      <td>99.857794</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>lastotherinc_902A</td>\n      <td>99.854060</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>isbidproductrequest_292L</td>\n      <td>99.444801</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>interestrategrace_34L</td>\n      <td>98.913772</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>lastdependentsnum_448L</td>\n      <td>98.358638</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>equalityempfrom_62L</td>\n      <td>98.139401</td>\n    </tr>\n    <tr>\n      <th>94</th>\n      <td>maxannuity_4075009A</td>\n      <td>96.716883</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>equalitydataagreement_891L</td>\n      <td>96.573433</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>avglnamtstart24m_4525187A</td>\n      <td>95.427466</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>datelastinstal40dpd_247D</td>\n      <td>95.317029</td>\n    </tr>\n    <tr>\n      <th>171</th>\n      <td>validfrom_1069D</td>\n      <td>92.178673</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>cardtype_51L</td>\n      <td>91.760832</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>isdebitcard_729L</td>\n      <td>91.755264</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>inittransactionamount_650A</td>\n      <td>91.755264</td>\n    </tr>\n    <tr>\n      <th>168</th>\n      <td>totinstallast1m_4525188A</td>\n      <td>90.915326</td>\n    </tr>\n    <tr>\n      <th>111</th>\n      <td>maxpmtlast3m_4525190A</td>\n      <td>89.665931</td>\n    </tr>\n    <tr>\n      <th>109</th>\n      <td>maxlnamtstart6m_4525199A</td>\n      <td>87.142119</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"# =========================================================\n# DROP COLUMNS WITH >95% MISSING VALUES\n# =========================================================\n\nmissing_threshold = 95\n\ncols_to_drop = missing_df[\n    missing_df[\"Missing %\"] > missing_threshold\n][\"Column\"].tolist()\n\nprint(\"Number of columns to drop:\", len(cols_to_drop))\n\ndf = df.drop(columns=cols_to_drop)\n\nprint(\"New Shape:\", df.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:26:45.434069Z","iopub.execute_input":"2026-05-13T19:26:45.434302Z","iopub.status.idle":"2026-05-13T19:26:46.167415Z","shell.execute_reply.started":"2026-05-13T19:26:45.434284Z","shell.execute_reply":"2026-05-13T19:26:46.166512Z"}},"outputs":[{"name":"stdout","text":"Number of columns to drop: 13\nNew Shape: (1526659, 159)\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"# =========================================================\n# CHECK COLUMN TYPES\n# =========================================================\n\ncategorical_cols = df.select_dtypes(\n    include=[\"object\"]\n).columns.tolist()\n\nnumerical_cols = df.select_dtypes(\n    exclude=[\"object\"]\n).columns.tolist()\n\nprint(\"Categorical Columns:\", len(categorical_cols))\nprint(\"Numerical Columns:\", len(numerical_cols))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:27:07.745064Z","iopub.execute_input":"2026-05-13T19:27:07.745327Z","iopub.status.idle":"2026-05-13T19:27:09.148159Z","shell.execute_reply.started":"2026-05-13T19:27:07.745303Z","shell.execute_reply":"2026-05-13T19:27:09.147324Z"}},"outputs":[{"name":"stdout","text":"Categorical Columns: 34\nNumerical Columns: 125\n","output_type":"stream"}],"execution_count":12},{"cell_type":"code","source":"# =========================================================\n# HANDLE MISSING VALUES\n# =========================================================\n\n# Fill numerical columns with median\nfor col in numerical_cols:\n    df[col] = df[col].fillna(df[col].median())\n\n# Fill categorical columns with mode\nfor col in categorical_cols:\n    df[col] = df[col].fillna(df[col].mode()[0])\n\nprint(\"Missing values after handling:\")\nprint(df.isnull().sum().sum())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:27:30.148616Z","iopub.execute_input":"2026-05-13T19:27:30.148876Z","iopub.status.idle":"2026-05-13T19:27:38.95828Z","shell.execute_reply.started":"2026-05-13T19:27:30.148857Z","shell.execute_reply":"2026-05-13T19:27:38.957535Z"}},"outputs":[{"name":"stdout","text":"Missing values after handling:\n0\n","output_type":"stream"}],"execution_count":13},{"cell_type":"code","source":"# =========================================================\n# ENCODE CATEGORICAL FEATURES\n# =========================================================\n\nfrom sklearn.preprocessing import LabelEncoder\n\nle = LabelEncoder()\n\nfor col in categorical_cols:\n    df[col] = le.fit_transform(df[col].astype(str))\n\nprint(\"Categorical encoding completed.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:28:06.547129Z","iopub.execute_input":"2026-05-13T19:28:06.5474Z","iopub.status.idle":"2026-05-13T19:28:13.863384Z","shell.execute_reply.started":"2026-05-13T19:28:06.547377Z","shell.execute_reply":"2026-05-13T19:28:13.862665Z"}},"outputs":[{"name":"stdout","text":"Categorical encoding completed.\n","output_type":"stream"}],"execution_count":14},{"cell_type":"code","source":"# =========================================================\n# CORRELATION MATRIX\n# =========================================================\n\nplt.figure(figsize=(12,8))\n\ncorr_matrix = df.select_dtypes(include=np.number).corr()\n\nsns.heatmap(\n    corr_matrix.iloc[:20, :20],\n    cmap='coolwarm'\n)\n\nplt.title(\"Correlation Heatmap\")\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T20:08:35.105814Z","iopub.execute_input":"2026-05-13T20:08:35.106123Z","iopub.status.idle":"2026-05-13T20:09:43.935212Z","shell.execute_reply.started":"2026-05-13T20:08:35.106096Z","shell.execute_reply":"2026-05-13T20:09:43.934461Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":48},{"cell_type":"code","source":"# =========================================================\n# DEFINE FEATURES AND TARGET\n# =========================================================\n\nX = df.drop(\n    columns=[\"target\", \"case_id\", \"date_decision\"]\n)\n\ny = df[\"target\"]\n\nprint(\"X Shape:\", X.shape)\nprint(\"y Shape:\", y.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:28:34.977235Z","iopub.execute_input":"2026-05-13T19:28:34.977799Z","iopub.status.idle":"2026-05-13T19:28:35.550598Z","shell.execute_reply.started":"2026-05-13T19:28:34.977748Z","shell.execute_reply":"2026-05-13T19:28:35.549979Z"}},"outputs":[{"name":"stdout","text":"X Shape: (1526659, 156)\ny Shape: (1526659,)\n","output_type":"stream"}],"execution_count":15},{"cell_type":"code","source":"# =========================================================\n# FEATURE SCALING\n# =========================================================\n\nscaler = StandardScaler()\n\nX_scaled = scaler.fit_transform(X)\n\nprint(\"Scaling completed.\")\nprint(\"Scaled Shape:\", X_scaled.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:28:56.507962Z","iopub.execute_input":"2026-05-13T19:28:56.508212Z","iopub.status.idle":"2026-05-13T19:28:58.621916Z","shell.execute_reply.started":"2026-05-13T19:28:56.508189Z","shell.execute_reply":"2026-05-13T19:28:58.62118Z"}},"outputs":[{"name":"stdout","text":"Scaling completed.\nScaled Shape: (1526659, 156)\n","output_type":"stream"}],"execution_count":16},{"cell_type":"code","source":"# =========================================================\n# TRAIN TEST SPLIT\n# =========================================================\n\nX_train, X_val, y_train, y_val = train_test_split(\n    X_scaled,\n    y,\n    test_size=0.2,\n    random_state=42,\n    stratify=y\n)\n\nprint(\"X_train shape:\", X_train.shape)\nprint(\"X_val shape:\", X_val.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:29:31.305056Z","iopub.execute_input":"2026-05-13T19:29:31.305309Z","iopub.status.idle":"2026-05-13T19:29:33.454401Z","shell.execute_reply.started":"2026-05-13T19:29:31.30529Z","shell.execute_reply":"2026-05-13T19:29:33.45382Z"}},"outputs":[{"name":"stdout","text":"X_train shape: (1221327, 156)\nX_val shape: (305332, 156)\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"# =========================================================\n# CLASS WEIGHTS\n# =========================================================\n\nfrom sklearn.utils.class_weight import compute_class_weight\n\nclasses = np.unique(y_train)\n\nclass_weights = compute_class_weight(\n    class_weight='balanced',\n    classes=classes,\n    y=y_train\n)\n\nclass_weights = dict(enumerate(class_weights))\n\nprint(class_weights)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:37:25.174406Z","iopub.execute_input":"2026-05-13T19:37:25.174673Z","iopub.status.idle":"2026-05-13T19:37:25.32146Z","shell.execute_reply.started":"2026-05-13T19:37:25.174654Z","shell.execute_reply":"2026-05-13T19:37:25.320802Z"}},"outputs":[{"name":"stdout","text":"{0: np.float64(0.5162287434949769), 1: np.float64(15.904766245604897)}\n","output_type":"stream"}],"execution_count":23},{"cell_type":"code","source":"# =========================================================\n# BUILD NEURAL NETWORK MODEL\n# =========================================================\n\n\nimproved_model = Sequential([\n\n    Dense(\n        256,\n        activation='relu',\n        kernel_regularizer=l2(0.0005),\n        input_shape=(X_train.shape[1],)\n    ),\n\n    Dropout(0.4),\n\n    Dense(\n        128,\n        activation='relu',\n        kernel_regularizer=l2(0.0005)\n    ),\n\n    Dropout(0.3),\n\n    Dense(\n        64,\n        activation='relu'\n    ),\n\n    Dense(\n        1,\n        activation='sigmoid'\n    )\n])\n\nimproved_model.compile(\n    optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),\n    loss='binary_crossentropy',\n    metrics=['AUC']\n)\n\nimproved_model.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:38:19.38733Z","iopub.execute_input":"2026-05-13T19:38:19.387604Z","iopub.status.idle":"2026-05-13T19:38:19.430759Z","shell.execute_reply.started":"2026-05-13T19:38:19.387567Z","shell.execute_reply":"2026-05-13T19:38:19.430194Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"\u001b[1mModel: \"sequential_2\"\u001b[0m\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │        \u001b[38;5;34m40,192\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)             │         \u001b[38;5;34m8,256\u001b[0m │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m65\u001b[0m │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">40,192</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n├─────────────────────────────────┼────────────────────────┼───────────────┤\n│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n└─────────────────────────────────┴────────────────────────┴───────────────┘\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m81,409\u001b[0m (318.00 KB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">81,409</span> (318.00 KB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m81,409\u001b[0m (318.00 KB)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">81,409</span> (318.00 KB)\n</pre>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n","text/html":"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n</pre>\n"},"metadata":{}}],"execution_count":25},{"cell_type":"code","source":"# =========================================================\n# COMPILE MODEL\n# =========================================================\n\nmodel.compile(\n    optimizer='adam',\n    loss='binary_crossentropy',\n    metrics=['AUC']\n)\n\nprint(\"Model compiled successfully.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:38:29.040184Z","iopub.execute_input":"2026-05-13T19:38:29.040424Z","iopub.status.idle":"2026-05-13T19:38:29.05317Z","shell.execute_reply.started":"2026-05-13T19:38:29.040404Z","shell.execute_reply":"2026-05-13T19:38:29.052508Z"}},"outputs":[{"name":"stdout","text":"Model compiled successfully.\n","output_type":"stream"}],"execution_count":26},{"cell_type":"code","source":"# =========================================================\n# EARLY STOPPING\n# =========================================================\n\nearly_stop = EarlyStopping(\n    monitor='val_auc',\n    patience=3,\n    mode='max',\n    restore_best_weights=True\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:38:31.61963Z","iopub.execute_input":"2026-05-13T19:38:31.619915Z","iopub.status.idle":"2026-05-13T19:38:31.623456Z","shell.execute_reply.started":"2026-05-13T19:38:31.619895Z","shell.execute_reply":"2026-05-13T19:38:31.622941Z"}},"outputs":[],"execution_count":27},{"cell_type":"code","source":"# =========================================================\n# TRAIN MODEL\n# =========================================================\n\nhistory2 = improved_model.fit(\n    X_train,\n    y_train,\n\n    validation_data=(X_val, y_val),\n\n    epochs=20,\n    batch_size=1024,\n\n    class_weight=class_weights,\n\n    callbacks=[early_stop],\n\n    verbose=1\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:38:35.762076Z","iopub.execute_input":"2026-05-13T19:38:35.762329Z","iopub.status.idle":"2026-05-13T19:42:22.30743Z","shell.execute_reply.started":"2026-05-13T19:38:35.762307Z","shell.execute_reply":"2026-05-13T19:42:22.306756Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 10ms/step - AUC: 0.6437 - loss: 0.8112 - val_AUC: 0.6978 - val_loss: 0.6920\nEpoch 2/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6962 - loss: 0.7015 - val_AUC: 0.7017 - val_loss: 0.6825\nEpoch 3/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7041 - loss: 0.6567 - val_AUC: 0.7026 - val_loss: 0.6683\nEpoch 4/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7083 - loss: 0.6428 - val_AUC: 0.7052 - val_loss: 0.6177\nEpoch 5/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7072 - loss: 0.6348 - val_AUC: 0.7075 - val_loss: 0.6346\nEpoch 6/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7083 - loss: 0.6329 - val_AUC: 0.7073 - val_loss: 0.6395\nEpoch 7/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7113 - loss: 0.6269 - val_AUC: 0.7090 - val_loss: 0.5917\nEpoch 8/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7105 - loss: 0.6273 - val_AUC: 0.7094 - val_loss: 0.6274\nEpoch 9/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7137 - loss: 0.6263 - val_AUC: 0.7072 - val_loss: 0.6348\nEpoch 10/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7146 - loss: 0.6262 - val_AUC: 0.7096 - val_loss: 0.5906\nEpoch 11/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 10ms/step - AUC: 0.7141 - loss: 0.6235 - val_AUC: 0.7095 - val_loss: 0.6580\nEpoch 12/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7129 - loss: 0.6209 - val_AUC: 0.7110 - val_loss: 0.6297\nEpoch 13/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 10ms/step - AUC: 0.7169 - loss: 0.6255 - val_AUC: 0.7115 - val_loss: 0.6286\nEpoch 14/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7183 - loss: 0.6216 - val_AUC: 0.7107 - val_loss: 0.6224\nEpoch 15/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7149 - loss: 0.6226 - val_AUC: 0.7119 - val_loss: 0.6208\nEpoch 16/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7166 - loss: 0.6221 - val_AUC: 0.7108 - val_loss: 0.6318\nEpoch 17/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7190 - loss: 0.6208 - val_AUC: 0.7122 - val_loss: 0.6494\nEpoch 18/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7191 - loss: 0.6195 - val_AUC: 0.7110 - val_loss: 0.6359\nEpoch 19/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7167 - loss: 0.6238 - val_AUC: 0.7125 - val_loss: 0.6310\nEpoch 20/20\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.7190 - loss: 0.6207 - val_AUC: 0.7116 - val_loss: 0.6201\n","output_type":"stream"}],"execution_count":28},{"cell_type":"code","source":"# =========================================================\n# MODEL EVALUATION\n# =========================================================\n\ny_pred_prob2 = improved_model.predict(X_val)\n\nauc_score2 = roc_auc_score(y_val, y_pred_prob2)\n\ngini_score2 = 2 * auc_score2 - 1\n\nprint(\"Improved AUC Score:\", auc_score2)\nprint(\"Improved Gini Score:\", gini_score2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:44:37.359382Z","iopub.execute_input":"2026-05-13T19:44:37.359654Z","iopub.status.idle":"2026-05-13T19:44:47.020171Z","shell.execute_reply.started":"2026-05-13T19:44:37.359633Z","shell.execute_reply":"2026-05-13T19:44:47.019569Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m9542/9542\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 745us/step\nImproved AUC Score: 0.7116951260493389\nImproved Gini Score: 0.42339025209867787\n","output_type":"stream"}],"execution_count":30},{"cell_type":"code","source":"# =========================================================\n# PLOT TRAINING CURVES\n# =========================================================\n\nplt.figure(figsize=(10,6))\n\nplt.plot(history2.history['AUC'], label='Train AUC')\n\nplt.plot(history2.history['val_AUC'], label='Validation AUC')\n\nplt.title('Model AUC Over Epochs')\n\nplt.xlabel('Epoch')\n\nplt.ylabel('AUC')\n\nplt.legend()\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T20:04:03.419343Z","iopub.execute_input":"2026-05-13T20:04:03.419622Z","iopub.status.idle":"2026-05-13T20:04:03.538423Z","shell.execute_reply.started":"2026-05-13T20:04:03.419602Z","shell.execute_reply":"2026-05-13T20:04:03.537213Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":44},{"cell_type":"code","source":"# =========================================================\n# SGD MODEL\n# =========================================================\n\nsgd_model = Sequential([\n\n    Dense(\n        256,\n        activation='relu',\n        kernel_regularizer=l2(0.0005),\n        input_shape=(X_train.shape[1],)\n    ),\n\n    Dropout(0.4),\n\n    Dense(\n        128,\n        activation='relu',\n        kernel_regularizer=l2(0.0005)\n    ),\n\n    Dropout(0.3),\n\n    Dense(64, activation='relu'),\n\n    Dense(1, activation='sigmoid')\n])\n\nsgd_model.compile(\n    optimizer=tf.keras.optimizers.SGD(\n        learning_rate=0.01,\n        momentum=0.9\n    ),\n\n    loss='binary_crossentropy',\n\n    metrics=['AUC']\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:49:44.750494Z","iopub.execute_input":"2026-05-13T19:49:44.750809Z","iopub.status.idle":"2026-05-13T19:49:44.787161Z","shell.execute_reply.started":"2026-05-13T19:49:44.750756Z","shell.execute_reply":"2026-05-13T19:49:44.786492Z"}},"outputs":[],"execution_count":37},{"cell_type":"code","source":"# =========================================================\n# TRAIN SGD MODEL\n# =========================================================\n\nhistory_sgd = sgd_model.fit(\n    X_train,\n    y_train,\n\n    validation_data=(X_val, y_val),\n\n    epochs=10,\n\n    batch_size=1024,\n\n    class_weight=class_weights,\n\n    callbacks=[early_stop],\n\n    verbose=1\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:49:57.154009Z","iopub.execute_input":"2026-05-13T19:49:57.154297Z","iopub.status.idle":"2026-05-13T19:51:41.780128Z","shell.execute_reply.started":"2026-05-13T19:49:57.154275Z","shell.execute_reply":"2026-05-13T19:51:41.779538Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 9ms/step - AUC: 0.6312 - loss: 0.8385 - val_AUC: 0.6915 - val_loss: 0.7653\nEpoch 2/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6876 - loss: 0.7708 - val_AUC: 0.6980 - val_loss: 0.7269\nEpoch 3/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 8ms/step - AUC: 0.6984 - loss: 0.7329 - val_AUC: 0.7008 - val_loss: 0.7242\nEpoch 4/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 8ms/step - AUC: 0.7023 - loss: 0.7062 - val_AUC: 0.7028 - val_loss: 0.6768\nEpoch 5/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 8ms/step - AUC: 0.7068 - loss: 0.6863 - val_AUC: 0.7047 - val_loss: 0.6880\nEpoch 6/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 8ms/step - AUC: 0.7073 - loss: 0.6721 - val_AUC: 0.7059 - val_loss: 0.7016\nEpoch 7/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 8ms/step - AUC: 0.7087 - loss: 0.6625 - val_AUC: 0.7066 - val_loss: 0.6694\nEpoch 8/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - AUC: 0.7125 - loss: 0.6516 - val_AUC: 0.7073 - val_loss: 0.6330\nEpoch 9/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - AUC: 0.7144 - loss: 0.6472 - val_AUC: 0.7099 - val_loss: 0.6607\nEpoch 10/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 9ms/step - AUC: 0.7135 - loss: 0.6362 - val_AUC: 0.7099 - val_loss: 0.6145\n","output_type":"stream"}],"execution_count":38},{"cell_type":"code","source":"# =========================================================\n# SGD EVALUATION\n# =========================================================\n\ny_pred_sgd = sgd_model.predict(X_val)\n\nauc_sgd = roc_auc_score(y_val, y_pred_sgd)\n\ngini_sgd = 2 * auc_sgd - 1\n\nprint(\"SGD AUC:\", auc_sgd)\n\nprint(\"SGD Gini:\", gini_sgd)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T20:05:19.900291Z","iopub.execute_input":"2026-05-13T20:05:19.900541Z","iopub.status.idle":"2026-05-13T20:05:29.856759Z","shell.execute_reply.started":"2026-05-13T20:05:19.900522Z","shell.execute_reply":"2026-05-13T20:05:29.856202Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m9542/9542\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 777us/step\nSGD AUC: 0.7100030393508237\nSGD Gini: 0.4200060787016473\n","output_type":"stream"}],"execution_count":45},{"cell_type":"code","source":"# =========================================================\n# RMSPROP MODEL\n# =========================================================\n\nrms_model = Sequential([\n\n    Dense(\n        256,\n        activation='relu',\n        kernel_regularizer=l2(0.0005),\n        input_shape=(X_train.shape[1],)\n    ),\n\n    Dropout(0.4),\n\n    Dense(\n        128,\n        activation='relu',\n        kernel_regularizer=l2(0.0005)\n    ),\n\n    Dropout(0.3),\n\n    Dense(64, activation='relu'),\n\n    Dense(1, activation='sigmoid')\n])\n\nrms_model.compile(\n    optimizer=tf.keras.optimizers.RMSprop(\n        learning_rate=0.0005\n    ),\n\n    loss='binary_crossentropy',\n\n    metrics=['AUC']\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:55:28.517214Z","iopub.execute_input":"2026-05-13T19:55:28.517453Z","iopub.status.idle":"2026-05-13T19:55:28.554662Z","shell.execute_reply.started":"2026-05-13T19:55:28.517434Z","shell.execute_reply":"2026-05-13T19:55:28.553918Z"}},"outputs":[],"execution_count":39},{"cell_type":"code","source":"# =========================================================\n# TRAIN RMSPROP MODEL\n# =========================================================\n\nhistory_rms = rms_model.fit(\n    X_train,\n    y_train,\n\n    validation_data=(X_val, y_val),\n\n    epochs=10,\n\n    batch_size=1024,\n\n    class_weight=class_weights,\n\n    callbacks=[early_stop],\n\n    verbose=1\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T19:55:40.778423Z","iopub.execute_input":"2026-05-13T19:55:40.778665Z","iopub.status.idle":"2026-05-13T19:57:32.480813Z","shell.execute_reply.started":"2026-05-13T19:55:40.778646Z","shell.execute_reply":"2026-05-13T19:57:32.480162Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 9ms/step - AUC: 0.6408 - loss: 0.8129 - val_AUC: 0.6862 - val_loss: 0.6532\nEpoch 2/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6886 - loss: 0.6901 - val_AUC: 0.6950 - val_loss: 0.6198\nEpoch 3/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6945 - loss: 0.6550 - val_AUC: 0.6942 - val_loss: 0.6518\nEpoch 4/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6931 - loss: 0.6445 - val_AUC: 0.6926 - val_loss: 0.5994\nEpoch 5/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6950 - loss: 0.6404 - val_AUC: 0.6944 - val_loss: 0.6326\nEpoch 6/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6946 - loss: 0.6410 - val_AUC: 0.6957 - val_loss: 0.6152\nEpoch 7/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6960 - loss: 0.6377 - val_AUC: 0.6942 - val_loss: 0.6747\nEpoch 8/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6947 - loss: 0.6401 - val_AUC: 0.6950 - val_loss: 0.6045\nEpoch 9/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6958 - loss: 0.6375 - val_AUC: 0.6954 - val_loss: 0.6419\nEpoch 10/10\n\u001b[1m1193/1193\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 9ms/step - AUC: 0.6966 - loss: 0.6383 - val_AUC: 0.6939 - val_loss: 0.6086\n","output_type":"stream"}],"execution_count":40},{"cell_type":"code","source":"# =========================================================\n# RMSPROP EVALUATION\n# =========================================================\n\ny_pred_rms = rms_model.predict(X_val)\n\nauc_rms = roc_auc_score(y_val, y_pred_rms)\n\ngini_rms = 2 * auc_rms - 1\n\nprint(\"RMSprop AUC:\", auc_rms)\n\nprint(\"RMSprop Gini:\", gini_rms)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T20:05:49.192304Z","iopub.execute_input":"2026-05-13T20:05:49.192536Z","iopub.status.idle":"2026-05-13T20:05:59.339003Z","shell.execute_reply.started":"2026-05-13T20:05:49.192518Z","shell.execute_reply":"2026-05-13T20:05:59.338251Z"}},"outputs":[{"name":"stdout","text":"\u001b[1m9542/9542\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 764us/step\nRMSprop AUC: 0.6938781657115471\nRMSprop Gini: 0.3877563314230943\n","output_type":"stream"}],"execution_count":46},{"cell_type":"code","source":"# =========================================================\n# FINAL MODEL COMPARISON\n# =========================================================\n\ncomparison = pd.DataFrame({\n\n    \"Optimizer\": [\n        \"Adam\",\n        \"SGD\",\n        \"RMSprop\"\n    ],\n\n    \"AUC\": [\n        auc_score2,\n        auc_sgd,\n        auc_rms\n    ],\n\n    \"Gini\": [\n        gini_score2,\n        gini_sgd,\n        gini_rms\n    ]\n})\n\ncomparison","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T20:06:07.002429Z","iopub.execute_input":"2026-05-13T20:06:07.002706Z","iopub.status.idle":"2026-05-13T20:06:07.011093Z","shell.execute_reply.started":"2026-05-13T20:06:07.002685Z","shell.execute_reply":"2026-05-13T20:06:07.010244Z"}},"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"  Optimizer       AUC      Gini\n0      Adam  0.711695  0.423390\n1       SGD  0.710003  0.420006\n2   RMSprop  0.693878  0.387756","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>Optimizer</th>\n      <th>AUC</th>\n      <th>Gini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adam</td>\n      <td>0.711695</td>\n      <td>0.423390</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SGD</td>\n      <td>0.710003</td>\n      <td>0.420006</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>RMSprop</td>\n      <td>0.693878</td>\n      <td>0.387756</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":47},{"cell_type":"code","source":"# =========================================================\n# PLOT OPTIMIZER COMPARISON\n# =========================================================\n\nplt.figure(figsize=(8,5))\n\nplt.bar(\n    results[\"Optimizer\"],\n    results[\"Best Validation AUC\"]\n)\n\nplt.title(\"Optimizer Comparison\")\n\nplt.ylabel(\"Validation AUC\")\n\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-05-13T20:01:42.991165Z","iopub.execute_input":"2026-05-13T20:01:42.991431Z","iopub.status.idle":"2026-05-13T20:01:43.088608Z","shell.execute_reply.started":"2026-05-13T20:01:42.991412Z","shell.execute_reply":"2026-05-13T20:01:43.087449Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 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\n"},"metadata":{}}],"execution_count":43},{"cell_type":"markdown","source":"# Conclusion\n\n- Neural Networks were successfully applied for credit risk prediction.\n- Data preprocessing included handling missing values, encoding categorical variables, and feature scaling.\n- Class imbalance was addressed using class weights.\n- Adam optimizer achieved the best performance among tested optimizers.\n- Regularization techniques such as Dropout, L2 Regularization, and EarlyStopping reduced overfitting.\n- The final model achieved strong predictive performance with good stability.","metadata":{}}]}