# E-Commerce Customer Spending Prediction Model
**Stack:** Python, scikit-learn, pandas, Linear Regression, Excel, Jupyter
**Date:** September 2025
## Overview
Linear regression analysis predicting annual ecommerce customer spending from session behavior, app and website usage, and membership duration.
## Key Highlights
- Built a linear regression model to predict annual customer spending from membership, session, and website usage features.
- Explored an ecommerce dataset with 500 customers and key features such as Avg. Session Length, Time on App, Time on Website, and Length of Membership.
- Analyzed model coefficients to identify feature importance and customer value drivers.
- Evaluated performance with MAE, MSE, and RMSE metrics, achieving an RMSE around $10.
## Features
- Dataset exploration with pandas and summary statistics
- Visual analysis of feature relationships and correlations
- Linear Regression modeling with train/test split for generalization
- Feature importance analysis from model coefficients
- Prediction validation using MAE, MSE, and RMSE
- Residual analysis with scatter and normality checks
- Jupyter notebook walkthrough for reproducible modeling
## Tech Stack
Python, scikit-learn, pandas, Linear Regression, Excel, Jupyter
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