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.

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.

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

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.

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

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

A Streamlit-based churn prediction app using a trained Random Forest model to analyze customer behavior and predict churn based on demographics, spending, interaction history, and service usage.

The logistic regression model was used in analyzing customer retention.

an implementation of ann-based model to predict customer retention

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

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