Campaign Response Predictor

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A supervised learning pipeline predicting customer responsiveness to marketing campaigns

Key Features

• End-to-end classification pipeline with data cleaning, feature engineering, and model tuning
• Compared Decision Tree and Random Forest classifiers across accuracy, F1, and AUC-ROC
• Identified income and spending as top predictors; flagged ethical implications of financial bias

Technology Used

• Python · Scikit-learn · Pandas · Matplotlib
• RandomizedSearchCV & GridSearchCV for hyperparameter tuning
• Pipelines with StandardScaler, OrdinalEncoder, OneHotEncoder via ColumnTransformer

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