Aditya Hande

Churn_WebApp

June 16, 2026 – Present

Customer churn is one of the most important business challenges for subscription-based companies such as telecom providers, SaaS businesses, streaming platforms, and financial services. This project builds a machine learning-powered web application that predicts customer churn probability and provides business recommendations for customer retention

sentiment_analysis

June 15, 2026 – Present

Customer reviews contain valuable insights about products, services, and customer satisfaction.In this project, Natural Language Processing (NLP) techniques were used to classify movie reviews as positive or negative using TF-IDF vectorization and machine learning models.

Fraud_Detection

June 14, 2026 – Present

In this project, machine learning models were used to detect fraudulent credit card transactions using an extremely imbalanced dataset. The project compares: - Logistic Regression - Random Forest - XGBoost and evaluates their effectiveness using fraud-specific metrics such as Precision, Recall, F1-Score, and ROC-AUC.

Customer_Liftime_Value

June 13, 2026 – Present

Customer Lifetime Value (CLV) is one of the most important metrics in retail, e-commerce, and customer relationship management. Rather than treating all customers equally, businesses aim to identify customers who are likely to generate the highest future revenue and allocate marketing resources accordingly.

Marketing-Campaign-Analytics

June 13, 2026 – Present

Marketing campaigns are a critical tool for increasing customer engagement, conversions, and revenue. However, not every customer responds to marketing efforts. This project analyzes customer behavior, campaign performance, and purchasing patterns to identify factors that influence campaign success and predict whether a customer will convert.

retail_demand_forecasting

June 12, 2026 – Present

Demand forecasting is one of the most important machine learning applications in retail, e-commerce, supply chain management, and inventory planning. In this project, historical sales data from multiple retail stores was used to predict future sales demand using machine learning techniques.

Customer-Churn-Prediction

June 11, 2026 – Present

Customer churn is one of the most important business challenges faced by telecom, subscription, banking, and e-commerce companies. This project predicts whether a customer is likely to leave a telecom service provider using machine learning techniques and customer behavioral data.

Recommendation_Cosine_Corr_Analysis

June 10, 2026 – Present

Movie Recommendation System using MovieLens 100K A Python-based movie recommender that builds a user-movie matrix and generates item-based collaborative filtering suggestions using Pearson correlation and cosine similarity, with analysis in Jupyter Notebook.

Customer-Segmentation-RFM-KMeans

June 9, 2026 – Present

Built a customer analytics solution using RFM Analysis and K-Means Clustering on 397K+ retail transactions. Segmented 4,338 customers into Champions, Loyal, At-Risk, and Lost groups, uncovering that 14.6% of customers generated nearly 49% of total revenue. Developed actionable retention and loyalty strategies based on customer behavior.

Titanic-Survival-Prediction

June 8, 2026 – Present

Machine Learning project predicting Titanic passenger survival using Random Forest Classifier.