Victor Phan
30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
import pandas as pd
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import numpy as np
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df = pd.read_excel('Data_VN_filter_v5.xlsx')
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target_vars = ['Donation', 'Decision']
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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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df_sample = df_subset.sample(n=200, random_state=42) if len(df_subset) > 200 else df_subset
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X = pd.get_dummies(df_sample[numeric_vars + categorical_vars], drop_first=True, dtype=float)
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# Find constant columns
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constant_cols = [col for col in X.columns if X[col].nunique() <= 1]
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print(f"Constant columns: {constant_cols}")
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# Drop constant columns
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X = X.drop(columns=constant_cols)
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# Find highly correlated columns
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corr_matrix = X.corr().abs()
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upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
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to_drop = [column for column in upper.columns if any(upper[column] > 0.99)]
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print(f"Highly correlated columns (>0.99): {to_drop}")
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