The project is a binary classification problem to predict whether the case outcome is going in favour of either complainant or respondent based on the historical judgment copies, using machine learning and deep learning algorithms

• Addresses problem to analyse required skills from large skills footprint (> 2000+ Products, 5000+ Skills).
• Addresses problem to analyse from 20+ different dimensions including Engineer’s Availability (Time Zone, PTO), Load, Citizen Constraints etc

The project involved identification of potential customers for the personal loan product of a bank. Ensemble modelling techniques were leveraged to predict the propensity of a prospect to purchase the loan. This enabled the bank to devise targeted marketing campaigns to increase the conversion rate with minimal budget

Applied K-Means clustering to identify different segments of customers for a Broadband Service Provider. Based on analysis of the customer clusters, attributes that drive churn were identified which enabled the company to improve their services in a strategic manner.