Initial commit

smote_logistic.py

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+import pandas as pd
+import numpy as np
+import statsmodels.api as sm
+from imblearn.over_sampling import SMOTE
+
+df = pd.read_excel('Data_VN_filter_v5.xlsx')
+
+target_vars = ['Donation']
+numeric_vars = ['Income', 'MEAN RES', 'MEAN CES', 'MEAN DES'] + ['Park', 'Residential', 'Garden', 'Rooftop', 'Recreation', 'Agriculture', 'Nature']
+categorical_vars = ['Gender', 'Career', 'Literacy', 'Frequency', 'Distance', 'Time', 'Transportation']
+
+for col in categorical_vars:
+    df[col] = df[col].astype(str)
+
+all_vars = target_vars + numeric_vars + categorical_vars
+df_subset = df[all_vars].dropna()
+
+X = pd.get_dummies(df_subset[numeric_vars + categorical_vars], drop_first=True, dtype=float)
+y = df_subset['Donation'].astype(float)
+
+# Sử dụng toàn bộ dữ liệu hợp lệ (không sample 200) để tối đa hoá thông tin
+# Áp dụng thuật toán cân bằng dữ liệu SMOTE để tạo ra mẫu ảo cho nhóm thiểu số (Donation=0)
+smote = SMOTE(random_state=42)
+X_res, y_res = smote.fit_resample(X, y)
+
+print(f"Data shape after SMOTE: {X_res.shape}")
+print(f"Donation=1: {sum(y_res==1)}, Donation=0: {sum(y_res==0)}")
+
+X_res = sm.add_constant(X_res)
+
+model = sm.Logit(y_res, X_res)
+try:
+    result = model.fit(method='bfgs', maxiter=1000, disp=False)
+    
+    summary_df = pd.DataFrame({
+        'Beta (B)': result.params,
+        'P-value': result.pvalues,
+        'Odds Ratio EXP(B)': np.exp(result.params)
+    })
+    
+    summary_df = summary_df.round(4)
+    summary_df['Significance'] = summary_df['P-value'].apply(lambda p: '***' if p < 0.001 else ('**' if p < 0.01 else ('*' if p < 0.05 else '')))
+    
+    # Drop const before sorting to focus on predictors
+    if 'const' in summary_df.index:
+        summary_df_no_const = summary_df.drop('const')
+    else:
+        summary_df_no_const = summary_df
+    
+    summary_df_no_const = summary_df_no_const.sort_values('P-value')
+    summary_df_no_const.to_csv('Logistic_Results_Donation_SMOTE.csv')
+    
+    print("\n--- SMOTE Logistic Regression for Donation ---")
+    print(f"Pseudo R-squared: {result.prsquared:.4f}")
+    print(summary_df_no_const.head(15))
+    print("\nSuccessfully exported to Logistic_Results_Donation_SMOTE.csv")
+    
+except Exception as e:
+    print(f"Model failed to converge: {e}")