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.

Stop losing SaaS users silently. Catch cancel intent in real time, score every user by churn risk, and act before they leave. Free plan available.

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.

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

Built 6 interactive Power BI dashboards for a PwC Switzerland Forage simulation — analyzing call center performance, customer churn (34.1%), and workforce diversity. Includes stakeholder email and actionable business recommendations.

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

AI-powered churn prediction pipeline integrating clustering, association rule mining, and ensemble learning,built for interpretable customer retention analytics.

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.

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

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

A data-driven framework to identify high-potential markets and operational risks in e-commerce expansion. Delivered in 4 hours

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.

Analyzed Flipkart customer support data using Excel to uncover customer retention drivers and built an Excel dashboard for actionable insights.

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

Customer churn is one of the most critical KPIs any business tracks. This project digs into churn patterns across dimensions like geography, age, gender, credit score, and credit card status — giving decision-makers a clear visual picture of which customer segments are most at risk.

The logistic regression model was used in analyzing customer retention.

an implementation of ann-based model to predict customer retention