Home Credit Modeling 2 - Rachel (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "352bc1e2",
   "metadata": {},
   "source": [
    "# Home Credit Gradient Boosted Trees Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f4dc83",
   "metadata": {},
   "source": [
    "Explaination of the business problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c418ce",
   "metadata": {},
   "source": [
    "## Importing packages and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ba9bd482",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint, uniform\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4ab5363d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Load the train and test data\n",
    "train_data = pd.read_csv('application_train.csv')\n",
    "test_data = pd.read_csv('application_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b50a72d",
   "metadata": {},
   "source": [
    "## EDA Continued"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d607f751",
   "metadata": {},
   "source": [
    "### Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ecf3f2ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove columns with more than 30% null values\n",
    "train_data = train_data.dropna(thresh=len(train_data) * 0.7, axis=1)\n",
    "test_data = test_data.dropna(thresh=len(test_data) * 0.7, axis=1)\n",
    "\n",
    "# Select valid numeric columns\n",
    "numeric_columns = train_data.select_dtypes(include=np.number).columns\n",
    "\n",
    "# Impute mean for numeric columns with less than 30% null values in train_data\n",
    "train_data = train_data.loc[:, numeric_columns].fillna(train_data.loc[:, numeric_columns].mean())\n",
    "\n",
    "# Select valid numeric columns in test\n",
    "test_numeric_columns = test_data.select_dtypes(include=np.number).columns\n",
    "\n",
    "# Impute mean for numeric columns with less than 30% null values in test_data\n",
    "test_data = test_data.loc[:, test_numeric_columns].fillna(test_data.loc[:, test_numeric_columns].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "262e704b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR                    0\n",
      "TARGET                        0\n",
      "CNT_CHILDREN                  0\n",
      "AMT_INCOME_TOTAL              0\n",
      "AMT_CREDIT                    0\n",
      "                             ..\n",
      "AMT_REQ_CREDIT_BUREAU_DAY     0\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK    0\n",
      "AMT_REQ_CREDIT_BUREAU_MON     0\n",
      "AMT_REQ_CREDIT_BUREAU_QRT     0\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR    0\n",
      "Length: 61, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for null values in train_data\n",
    "null_counts = train_data.isnull().sum()\n",
    "\n",
    "print(null_counts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a94aae6",
   "metadata": {},
   "source": [
    "### Encoding Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d3a68420",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical variables in train data\n",
    "categorical_cols = train_data.select_dtypes(include=['object']).columns\n",
    "encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')\n",
    "\n",
    "encoded_cols = pd.DataFrame(encoder.fit_transform(train_data[categorical_cols]))\n",
    "train_data = pd.concat([train_data, encoded_cols], axis=1).drop(categorical_cols, axis=1)\n",
    "\n",
    "# Encode categorical variables in test data\n",
    "encoded_cols = pd.DataFrame(encoder.transform(test_data[categorical_cols]))\n",
    "test_data = pd.concat([test_data, encoded_cols], axis=1).drop(categorical_cols, axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "677a4013",
   "metadata": {},
   "source": [
    "### Scale and Normalized Numeric Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aa6ef12f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exclude 'SK_ID_CURR' and 'TARGET' from the numerical columns\n",
    "#numerical_cols = train_data.select_dtypes(include=['float', 'int']).columns\n",
    "#numerical_cols = numerical_cols.drop(['SK_ID_CURR', 'TARGET'])\n",
    "\n",
    "# Scale/Normalize numerical features in train data\n",
    "#scaler = StandardScaler()\n",
    "#train_data[numerical_cols] = scaler.fit_transform(train_data[numerical_cols].values)\n",
    "\n",
    "# Exclude 'SK_ID_CURR' from the numerical columns\n",
    "#test_numerical_cols = test_data.select_dtypes(include=['float', 'int']).columns\n",
    "#test_numerical_cols = test_numerical_cols.drop('SK_ID_CURR')\n",
    "\n",
    "# Scale/Normalize numerical features in test data\n",
    "#scaler = StandardScaler()\n",
    "#test_data[test_numerical_cols] = scaler.fit_transform(test_data[test_numerical_cols].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ac8c6a",
   "metadata": {},
   "source": [
    "## Modeling Process"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3796f02",
   "metadata": {},
   "source": [
    "### Oversampling on the Training Set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dc747889",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and validation sets\n",
    "y = train_data['TARGET']\n",
    "X = train_data.drop('TARGET', axis=1)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Perform oversampling on the training set\n",
    "oversampler = RandomOverSampler(random_state=42)\n",
    "X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5be4313f",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "21fffea6",
   "metadata": {},
   "outputs": [
    {
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       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, gpu_id=None,\n",
       "                                           grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate...\n",
       "                                           predictor=None, random_state=None, ...),\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x000001EDDB4CA650&gt;,\n",
       "                                        &#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001EDDB4CBF40&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001EDDB6488E0&gt;},\n",
       "                   scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, gpu_id=None,\n",
       "                                           grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate...\n",
       "                                           predictor=None, random_state=None, ...),\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x000001EDDB4CA650&gt;,\n",
       "                                        &#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001EDDB4CBF40&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001EDDB6488E0&gt;},\n",
       "                   scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, gpu_id=None,\n",
       "                                           grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate...\n",
       "                                           predictor=None, random_state=None, ...),\n",
       "                   param_distributions={'learning_rate': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x000001EDDB4CA650>,\n",
       "                                        'max_depth': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001EDDB4CBF40>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x000001EDDB6488E0>},\n",
       "                   scoring='accuracy')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an XGBoost classifier\n",
    "xgb_model = xgb.XGBClassifier()\n",
    "\n",
    "# Define the parameter distributions for randomized search\n",
    "param_dist = {\n",
    "    'n_estimators': randint(100, 500),       \n",
    "    'max_depth': randint(3, 10),             \n",
    "    'learning_rate': uniform(0.01, 0.1)    \n",
    "}\n",
    "\n",
    "# Perform randomized search for hyperparameter tuning with increased iterations and cv folds\n",
    "random_search = RandomizedSearchCV(estimator=xgb_model, param_distributions=param_dist, n_iter=5, cv=2, scoring='accuracy')\n",
    "random_search.fit(X_train_resampled, y_train_resampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d468431",
   "metadata": {},
   "source": [
    "### Training the Model and Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7b852bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the best estimator from randomized search\n",
    "best_xgb_model = random_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0cf56c56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the model using cross-validation\n",
    "cv_scores = cross_val_score(best_xgb_model, X_train_resampled, y_train_resampled, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0526bf45",
   "metadata": {},
   "source": [
    "### Accuracy of the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dc3404f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [0.87453152 0.87457575 0.87896477 0.87794766 0.87981471]\n",
      "Mean Accuracy: 0.8771668818385423\n"
     ]
    }
   ],
   "source": [
    "# Print the cross-validation scores\n",
    "print(\"Cross-validation scores:\", cv_scores)\n",
    "print(\"Mean Accuracy:\", np.mean(cv_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772b10cd",
   "metadata": {},
   "source": [
    "### Make Prediction on the Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4676d6e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "test_predictions = best_xgb_model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7412f317",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a submission DataFrame\n",
    "submission = pd.DataFrame({\n",
    "    \"SK_ID_CURR\": test_data[\"SK_ID_CURR\"], \n",
    "    \"TARGET\": test_predictions\n",
    "})\n",
    "\n",
    "# Save the submission DataFrame to a CSV file\n",
    "submission.to_csv(\"submission.csv\", index=False)"
   ]
  }
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