PROJECTS
This Customer Segmentation project involves analyzing customer purchasing behavior using RFM (Recency, Frequency, Monetary) analysis and advanced clustering techniques. The project segments customers based on their purchasing patterns, helping businesses identify groups like "Best Customers" and "At Risk." By applying K-Means clustering and evaluating cluster performance with silhouette scores, the project uncovers deeper insights into customer behavior, enabling data-driven marketing strategies.
In this project, I built a predictive model to estimate Customer Lifetime Value (CLV) using the Online Retail II dataset. By applying Random Forest Regression and feature engineering (RFM analysis), I identified key drivers of CLV and provided actionable insights for customer segmentation. The model helps businesses focus on retaining high-value customers, increasing engagement with low-value customers, and growing mid-tier customers through targeted strategies. The project emphasizes the importance of customer-centric marketing and improving overall customer retention and profitability.
This project shows the trajectory of pizza sales in 2015 for a pizza place. The data set was downloaded from the maven analytics data playground. The analysis was done using SQL and the visualization was done using tableau.
This project shows the relationship between the purchasing of bicycles and factors such as marital status, the demographic of the population, education, professions etc. The data was cleaned, analyzed and visualized using excel.
This project shows how different users use bicycles belonging to a bike share company. Data cleaning and analysis were done using the r programming language and data visualization was done using tableau.
