Online Retail Customer Analysis & VIP Prediction Using Machine Learning

Analyzed an online retail dataset using Python, Pandas, NumPy, Matplotlib, and Scikit-learn. The project includes data cleaning and preprocessing, exploratory data analysis, customer feature engineering, customer spending analysis, and VIP customer classification using Logistic Regression.

Key Highlights:

- Cleaned and preprocessed retail transaction data.

- Engineered customer features such as Recency, Frequency, and Total Spend.

- Identified VIP customers based on spending thresholds.

- Built and evaluated a Logistic Regression classification model.

- Achieved 93.78% test accuracy in the notebook's saved results.