⭐ Key Project for Data Analyst Portfolio
This project performs advanced analysis on e-commerce data to understand customer behavior, retention patterns, and sales performance. It includes techniques like customer segmentation, forecasting, and recommendation systems to generate deeper business insights.
To analyze customer purchasing behavior and help businesses improve retention, increase revenue, and make data-driven marketing and product decisions.
- Python (Pandas, Scikit-learn, Statsmodels)
- SQL
- Power BI
The project uses three datasets: Orders, Customers, and Products. These datasets were merged to create a unified dataset for analysis.
- Data cleaning and preprocessing using Pandas
- Merged multiple datasets using joins
- Feature engineering (Revenue, Profit, Time features)
- Exploratory Data Analysis (EDA)
- Customer segmentation using RFM (Recency, Frequency, Monetary)
- Applied K-Means clustering to group customers
- Performed cohort analysis for retention tracking
- Built time-series forecasting model using ARIMA
- Implemented recommendation system using Apriori algorithm
- Created visualizations and dashboard for insights
- High customer retention rate (~89%) indicating strong loyalty
- Majority of customers fall under medium-value segment
- Revenue is concentrated among a small group of high-value customers
- Highest revenue generated by key cities like Delhi and Mumbai
- No strong product bundling patterns observed
- Certain products generate significantly higher margins
This project helps businesses identify high-value customers, improve retention strategies, and forecast future sales. It also supports targeted marketing and product recommendations based on customer behavior.
Power BI dashboard visualizing customer segments, revenue trends, retention analysis, and product performance.