Initial commit

fix_logistic.py

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+import pandas as pd
+import numpy as np
+import statsmodels.api as sm
+
+df = pd.read_excel('Data_VN_filter_v5.xlsx')
+
+target_vars = ['Donation']
+# Removed some categorical variables that have zero variance or cause perfect separation easily
+numeric_vars = ['Income', 'MEAN RES', 'MEAN CES', 'MEAN DES'] + ['Park', 'Residential', 'Garden', 'Rooftop', 'Recreation']
+categorical_vars = ['Gender', 'Frequency', 'Distance', 'Time']
+
+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()
+df_sample = df_subset.sample(n=200, random_state=42)
+
+X = pd.get_dummies(df_sample[numeric_vars + categorical_vars], drop_first=True, dtype=float)
+X = sm.add_constant(X)
+y = df_sample['Donation'].astype(float)
+
+model = sm.Logit(y, X)
+try:
+    result = model.fit(disp=False)
+    summary_df = pd.DataFrame({
+        'Beta (B)': result.params,
+        'P-value': result.pvalues,
+        'Odds Ratio EXP(B)': np.exp(result.params)
+    }).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 '')))
+    summary_df = summary_df.sort_values('P-value')
+    summary_df.to_csv('Logistic_Results_Donation_Optimized.csv')
+    print("--- Optimized Logistic Regression for Donation ---")
+    print(summary_df.head(10))
+except Exception as e:
+    print("Standard fit failed:", e)
+    result = model.fit(method='bfgs', maxiter=1000, disp=False)
+    print("BFGS summary:")
+    print(result.summary())