Credit Card Churn Prediction
– Powered by GridMaster
This project predicts credit card churn risk using machine learning models like logistic regression, random forests, XGBoost, lightGBM and CatBoost. Powered by GridMaster, my custom AutoML framework, it automates hyperparameter tuning, model comparisons, and performance visualization — helping businesses proactively retain customers.
Recommender System from Scratch
This project builds a recommender system entirely from scratch, using collaborative filtering, content-based filtering, and hybrid approaches. Without relying on external libraries, it demonstrates deep algorithmic understanding, system design, and performance evaluation — delivering personalized recommendations and insights into user preferences.
Heart Disease Data Analysis
· Medical Data Insights
· Statistical Modeling
· Risk Factor Exploration
Ensemble Methods and Boosting
· Bagging & Boosting
· Ensemble Learning
· Model Performance
Data Visualization
· Interactive Dashboards
· Insights Uncovered
· Design for Impact
Regression and Clustering
· Predictive Models
· Unsupervised Learning
· Pattern Discovery
Python Programming
· Scripting & Automation
· Problem Solving
· Code Craftsmanship
Airline Business
Intelligence Project
· Business Intelligence
· Data Integration
· Dashboard Design
Java Sorting and
Searching Program
· Algorithm Design
· Data Structures
· Performance Optimization