🧮 Customer-Spending-Prediction-with-XGBoost

This project focuses on building, tuning, and evaluating multiple regression models to predict how much a customer will spend on a catalog purchase based on demographic and behavioral features.

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📌 Project Overview

• Task: Predict the continuous Purchase amount from customer data.
• Goal: Identify the best regression model that minimizes prediction error.
• Dataset: Customer-level features and their catalog purchase amounts.
• Metric: Root Mean Squared Error (RMSE)

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🧰 Tools & Libraries

• Python, Jupyter Notebook
• scikit-learn
• XGBoost
• LightGBM
• MLPRegressor from sklearn.neural_network

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🧠 Modeling Strategy

• Applied Nested Cross-Validation to ensure unbiased model evaluation.
• Compared models: Linear Regression, KNN, SVR, Decision Tree, Random Forest, Gradient Boosting, XGBoost, LightGBM, and Neural Network.
• Final model was tuned using GridSearchCV and tested on a holdout set.

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✅ Results

• Best model: XGBoost (or Neural Net, depending on part B)
• Robust performance and generalization verified on the holdout set.

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💼 Business Value

Predicting customer spend helps optimize:

• Targeted marketing
• Inventory forecasting
• Personalized promotions

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📂 Explore the full notebook: regression_modeling.ipynb