This project focus on customer analysis and segmentation. Which help to generate specific marketing strategies targeting different groups. RFM Analysis, Cohort Analysis, and K-means Clusters were conducted on a UK-based online retail transaction dataset with 1,067,371 rows of records hosted on the UCI Machine Learning Repository.

Here you can see the projects I'm been involved with. ☺☺

PwC Switzerland Power BI Job Simulation on Forage - March 2025.

Customer retention cohort analysis using Pandas, Seaborn, and MatPlotLib python libraries.

An comprehensive data analysis of a particular market and its customers.

Enhanced telecom customer retention with a dynamic Power BI dashboard. Analyzed customer data to proactively identify churn risks, visualizing trends and insights. Empowered data-driven strategies for effective customer retention.

Predicting customer retention in an e-commerce platform using classification models. Includes data preprocessing, feature engineering, and model evaluation (Logistic Regression, SVM, Random Forest, KNN, Decision Tree). Best model achieves 83% accuracy and perfect recall. Ideal for business use.

This project encompasses feature engineering, exploratory data analysis (EDA), customer retention analysis, RFM segmentation, and in-depth statistical analysis to gain actionable insights.

The logistic regression model was used in analyzing customer retention.

an implementation of ann-based model to predict customer retention