FinTechCaseStudies/notebooks/02_model_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n",
    "from data_preprocessing import DataPreprocessing\n",
    "from model_training import ModelTraining\n",
    "import config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROCESSED_DATA_PATH = \"../\" + config.PROCESSED_DATA_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-02-09 01:00:13,827 - INFO - Loading data from ../new_appdata10.csv\n"
     ]
    },
    {
     "data": {
      "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>user</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>age</th>\n",
       "      <th>numscreens</th>\n",
       "      <th>minigame</th>\n",
       "      <th>used_premium_feature</th>\n",
       "      <th>enrolled</th>\n",
       "      <th>liked</th>\n",
       "      <th>location</th>\n",
       "      <th>...</th>\n",
       "      <th>SecurityModal</th>\n",
       "      <th>ResendToken</th>\n",
       "      <th>TransactionList</th>\n",
       "      <th>NetworkFailure</th>\n",
       "      <th>ListPicker</th>\n",
       "      <th>Other</th>\n",
       "      <th>SavingCount</th>\n",
       "      <th>CMCount</th>\n",
       "      <th>CCCount</th>\n",
       "      <th>LoansCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>235136</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>333588</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>254414</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>234192</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51549</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user  dayofweek  hour  age  numscreens  minigame  used_premium_feature  \\\n",
       "0  235136          3     2   23          15         0                     0   \n",
       "1  333588          6     1   24          13         0                     0   \n",
       "2  254414          1    19   23           3         0                     1   \n",
       "3  234192          4    16   28          40         0                     0   \n",
       "4   51549          1    18   31          32         0                     0   \n",
       "\n",
       "   enrolled  liked  location  ...  SecurityModal  ResendToken  \\\n",
       "0         0      0         0  ...              0            0   \n",
       "1         0      0         1  ...              0            0   \n",
       "2         0      1         0  ...              0            0   \n",
       "3         1      0         1  ...              0            0   \n",
       "4         1      1         0  ...              0            0   \n",
       "\n",
       "   TransactionList  NetworkFailure  ListPicker  Other  SavingCount  CMCount  \\\n",
       "0                0               0           0      7            0        0   \n",
       "1                0               0           0      5            0        0   \n",
       "2                0               0           0      0            0        0   \n",
       "3                0               0           0      6            0        3   \n",
       "4                0               0           0     10            0        2   \n",
       "\n",
       "   CCCount  LoansCount  \n",
       "0        0           1  \n",
       "1        0           1  \n",
       "2        0           1  \n",
       "3        0           1  \n",
       "4        0           1  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_df = DataPreprocessing.load_data(PROCESSED_DATA_PATH)\n",
    "processed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt = ModelTraining(PROCESSED_DATA_PATH,\n",
    "                   config.LOGISTIC_REGRESSION_PARAMS,\n",
    "                   config.GRID_SEARCH_PARAMS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt.preprocess_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt.build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data Accuracy: 0.7690\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy, precision, recall, f1 = mt.evaluate_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Accuracy: 0.768 (+/- 0.009)\n"
     ]
    }
   ],
   "source": [
    "mean_accuracy, std_accuracy = mt.cross_validate_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Took 23.17 seconds\n"
     ]
    }
   ],
   "source": [
    "best_accuracy, best_parameters, best_score = mt.tune_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Accuracy: 0.7676749999999999,\n",
      "Best Parameters: {'C': 10, 'penalty': 'l1'},\n",
      "Best Score: 0.7676749999999999\n"
     ]
    }
   ],
   "source": [
    "print(f\"Best Accuracy: {best_accuracy},\\nBest Parameters: {best_parameters},\\nBest Score: {best_score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt2 = ModelTraining(PROCESSED_DATA_PATH,\n",
    "                   config.LOGISTIC_REGRESSION_PARAMS,\n",
    "                   config.GRID_SEARCH_PARAMS2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt2.preprocess_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mt2.build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data Accuracy: 0.7690\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy2, precision2, recall2, f12 = mt2.evaluate_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM Accuracy: 0.768 (+/- 0.009)\n"
     ]
    }
   ],
   "source": [
    "mean_accuracy2, std_accuracy2 = mt2.cross_validate_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Took 22.09 seconds\n"
     ]
    }
   ],
   "source": [
    "best_accuracy2, best_parameters2, best_score2 = mt2.tune_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Accuracy: 0.7676499999999999,\n",
      "Best Parameters: {'C': 1, 'penalty': 'l1'},\n",
      "Best Score: 0.7676499999999999\n"
     ]
    }
   ],
   "source": [
    "print(f\"Best Accuracy: {best_accuracy2},\\nBest Parameters: {best_parameters2},\\nBest Score: {best_score2}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}