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