Fixed Deposit Campaign-Analysis and Prediction Model

Project Aim:

Conducted clustering analysis on predicted data using logistic regression and K-means, identifying 5 distinct customer segments. Analyzed variable importance to guide decision-making.

Responsibilities:

1. Enhanced clustering accuracy by implementing deep learning techniques, resulting in a 15% improvement.

2. Developed an end-to-end predictive model, achieving a 92% accuracy rate.

3. Collaborated with cross-functional teams to create a marketing strategy, leading to a 20% increase in brand awareness, 30% improvement in customer engagement, and a 25% boost in campaign effectiveness.