Sneha Pradeep

Developed and implemented a client segmentation model using Decision Tree, Random Forest, Logistic Regression algorithms. Utilized client priority rules based on customer attributes (holding value, login activity, trade recency) and other behaviors to identify and categorize inactive clients (greater than 6 months). Leveraged the model’s findings to provide actionable insights and devised a Short-Term strategy to retain approximately 6,000 customers nearing account expiry, resulting in a significant reactivation rate increase from 4.1 percent benchmark to 14 percent. Developed a real-time dashboard using Tableau to track and analyze performance across various channels such as Retail Branches, Segments, Open Markets, Internet Banking, etc.

Utilised NIFTY 500 index historical stock data, technical indicators (RSI, Aroon, Stochastic Oscillator), and sentiment analysis of tweets to predict stocks with potential 2 percent daily returns using Naive Bayes, SVM, Gradient Boosting Classifier, etc. Achieved 70 percent prediction accuracy on total stocks and conducted upcoming 10-day trend analysis for selected stocks to gain insights into stock behavior.

Courses included Advanced Statistical Learning, Machine Learning, Natural Language Processing, Computer Vision, Applied Time Series Analysis.