Upload model.py

model.py

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+# Import Libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import os
+import joblib
+
+# Load Dataset
+df = pd.read_csv("hospital_readmissions.csv")
+
+# Basic Info
+df.info()
+df.describe()
+
+# Missing Values
+print(df.isnull().sum())
+
+# Readmission Distribution
+sns.countplot(x='readmitted', data=df)
+plt.title('Readmitted Class Distribution')
+plt.xlabel('Readmitted (0=No, 1=Yes)')
+plt.ylabel('Count')
+plt.show()
+
+# Histograms for Numeric Features
+numeric_features = ['time_in_hospital', 'n_lab_procedures', 'n_procedures', 'n_medications']
+df[numeric_features].hist(figsize=(10,8), bins=15)
+plt.suptitle('Distribution of Numeric Features')
+plt.show()
+
+# Encoding Categorical Variables
+from sklearn.preprocessing import LabelEncoder
+
+label_encoders_file = "label_encoders.pkl"
+label_encoders_2_file = "label_encoders_2.pkl"
+
+# Load or Fit Label Encoders
+if os.path.exists(label_encoders_file) and os.path.exists(label_encoders_2_file):
+    print("Loading existing label encoders...")
+    label_encoders = joblib.load(label_encoders_file)
+    label_encoders_2 = joblib.load(label_encoders_2_file)
+else:
+    print("Fitting new label encoders...")
+    categorical_cols = ['age', 'glucose_test', 'A1Ctest', 'change', 'diabetes_med', 'readmitted']
+    label_encoders = {col: LabelEncoder() for col in categorical_cols}
+
+    for col, le in label_encoders.items():
+        df[col] = le.fit_transform(df[col].astype(str))
+
+    categorical_cols_2 = ['medical_specialty', 'diag_1', 'diag_2', 'diag_3']
+    label_encoders_2 = {col: LabelEncoder() for col in categorical_cols_2}
+
+    for col2, le in label_encoders_2.items():
+        df[col2] = le.fit_transform(df[col2].astype(str))
+
+    joblib.dump(label_encoders, label_encoders_file)
+    joblib.dump(label_encoders_2, label_encoders_2_file)
+    print("Label encoders saved.")
+
+# Feature Engineering (Interaction Terms)
+from sklearn.preprocessing import PolynomialFeatures
+
+poly = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)
+interaction_terms = poly.fit_transform(df[['time_in_hospital', 'n_lab_procedures', 'n_procedures', 'n_medications']])
+interaction_df = pd.DataFrame(interaction_terms, columns=poly.get_feature_names_out(
+    ['time_in_hospital', 'n_lab_procedures', 'n_procedures', 'n_medications']))
+df = pd.concat([df, interaction_df], axis=1)
+
+# Splitting the Data
+from sklearn.model_selection import train_test_split
+
+X = df.drop('readmitted', axis=1)
+y = df['readmitted']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Remove duplicate columns
+X_train = X_train.loc[:, ~X_train.columns.duplicated()]
+X_test = X_test.loc[:, ~X_test.columns.duplicated()]
+
+
+feature_columns_file = "feature_columns.pkl"
+joblib.dump(X_train.columns.tolist(), feature_columns_file)
+print(f"Feature columns saved as {feature_columns_file}")
+
+# Define Model Filenames
+rf_model_file = "rf_tuned_model.pkl"
+xgb_model_file = "xgb_model.pkl"
+lgbm_model_file = "lgbm_model.pkl"
+
+# Random Forest Classifier
+from sklearn.ensemble import RandomForestClassifier
+
+if os.path.exists(rf_model_file):
+    print(f"Loading existing Random Forest model from {rf_model_file}...")
+    rf_model = joblib.load(rf_model_file)
+else:
+    print("Training Random Forest model...")
+    rf_model = RandomForestClassifier(
+        bootstrap=True,
+        max_depth=10,
+        min_samples_leaf=4,
+        min_samples_split=5,
+        n_estimators=200,
+        random_state=42
+    )
+    rf_model.fit(X_train, y_train)
+    joblib.dump(rf_model, rf_model_file)
+    print(f"Random Forest model saved as {rf_model_file}")
+
+# XGBoost Classifier
+from xgboost import XGBClassifier
+
+if os.path.exists(xgb_model_file):
+    print(f"Loading existing XGBoost model from {xgb_model_file}...")
+    xgb_model = joblib.load(xgb_model_file)
+else:
+    print("Training XGBoost model...")
+    xgb_model = XGBClassifier(
+        n_estimators=200,
+        max_depth=10,
+        learning_rate=0.1,
+        subsample=0.8,
+        colsample_bytree=0.8,
+        random_state=42
+    )
+    xgb_model.fit(X_train, y_train)
+    joblib.dump(xgb_model, xgb_model_file)
+    print(f"XGBoost model saved as {xgb_model_file}")
+
+# LightGBM Classifier
+from lightgbm import LGBMClassifier
+
+if os.path.exists(lgbm_model_file):
+    print(f"Loading existing LightGBM model from {lgbm_model_file}...")
+    lgbm_model = joblib.load(lgbm_model_file)
+else:
+    print("Training LightGBM model...")
+    lgbm_model = LGBMClassifier(
+        n_estimators=200,
+        max_depth=10,
+        learning_rate=0.1,
+        subsample=0.8,
+        colsample_bytree=0.8,
+        random_state=42
+    )
+    lgbm_model.fit(X_train, y_train)
+    joblib.dump(lgbm_model, lgbm_model_file)
+    print(f"LightGBM model saved as {lgbm_model_file}")
+
+# Predictions
+y_pred_rf = rf_model.predict(X_test)
+y_proba_rf = rf_model.predict_proba(X_test)[:, 1]
+
+y_pred_xgb = xgb_model.predict(X_test)
+y_proba_xgb = xgb_model.predict_proba(X_test)[:, 1]
+
+y_pred_lgbm = lgbm_model.predict(X_test)
+y_proba_lgbm = lgbm_model.predict_proba(X_test)[:, 1]
+
+# Evaluation
+from sklearn.metrics import classification_report, roc_auc_score
+
+print("Random Forest Classification Report:\n", classification_report(y_test, y_pred_rf))
+print("Random Forest ROC-AUC Score:", roc_auc_score(y_test, y_proba_rf))
+
+print("XGBoost Classification Report:\n", classification_report(y_test, y_pred_xgb))
+print("XGBoost ROC-AUC Score:", roc_auc_score(y_test, y_proba_xgb))
+
+print("LightGBM Classification Report:\n", classification_report(y_test, y_pred_lgbm))
+print("LightGBM ROC-AUC Score:", roc_auc_score(y_test, y_proba_lgbm))
+
+# Compare Models
+results = {
+    "Model": ["Random Forest", "XGBoost", "LightGBM"],
+    "ROC-AUC Score": [roc_auc_score(y_test, y_proba_rf),
+                      roc_auc_score(y_test, y_proba_xgb),
+                      roc_auc_score(y_test, y_proba_lgbm)]
+}
+
+results_df = pd.DataFrame(results)
+print("\nModel Performance Summary:")
+print(results_df)
+
+# Plot Model Comparison
+sns.barplot(data=results_df, x="Model", y="ROC-AUC Score", palette="viridis")
+plt.title("Model ROC-AUC Comparison")
+plt.ylabel("ROC-AUC Score")
+plt.show()