Surendra S
- Research Program Mentor
MCIT candidate at Amity University
Expertise
Machine Learning and AI
Bio
I am passionate about the intersection of finance, technology, and artificial intelligence. With a Master’s degree in Computer Science and over two decades of experience in financial technology, I specialize in developing machine learning models for financial markets, algorithmic trading, and AI-driven wealth management solutions. I have worked on large-scale trading systems, portfolio optimization, and risk management tools, helping professionals in finance leverage data science to make informed decisions. Beyond my professional work, I actively mentor top college students from institutions such as Georgia Tech and top universities, helping them secure positions at leading tech companies like Meta, Google, Amazon, and Microsoft. Through Formation.dev, I guide aspiring engineers in mastering data structures, algorithms, and system design to excel in technical interviews and build successful careers in top-tier technology firms. I also enjoy outdoor adventures, traveling, and exploring different cultures. Whether it's analyzing market trends, coding intelligent trading algorithms, or preparing students for FAANG+ interviews, I find joy in sharing knowledge and helping others grow in their careers.Project ideas
Stock Price Prediction using Regression Models - Beginner
This project introduces students to the fundamentals of financial analytics and machine learning. Students will learn how to collect stock market data from APIs like Yahoo Finance, clean and preprocess the data using Pandas, and apply basic regression techniques such as Linear Regression, Lasso, and Ridge to predict stock prices. Additionally, students will create simple visualizations using Matplotlib and Seaborn to analyze stock trends. Skills Covered: Data collection and cleaning Exploratory data analysis (EDA) Introduction to regression models Basic financial market indicators Visualization techniques for stock trends Outcome: A Python-based stock price prediction model A research report explaining the prediction process and findings A presentation with key insights on market trends
Liquidity Forecasting and Investor Behavior Classification - Intermediate
This project builds on basic ML skills by introducing students to dual-model financial predictions. Students will implement both regression models (Random Forest, Gradient Boosting) for liquidity forecasting and classification models for investor behavior prediction. Feature engineering techniques will be applied to enhance model accuracy. The final results will be presented through an interactive dashboard for financial insights. Skills Covered: Advanced feature engineering for financial datasets Implementing Random Forest and Gradient Boosting Investor behavior classification using logistic regression and decision trees Model evaluation techniques (cross-validation, AUC-ROC) Interactive financial dashboards with Plotly Outcome: A dual-model system predicting liquidity and investor behavior A research paper analyzing feature importance and financial drivers An interactive visualization comparing model performance
AI-Powered Financial Analytics Platform for Market Forecasting - Advanced
This advanced project challenges students to develop a full-fledged financial analytics platform incorporating multiple machine learning models. Students will use ensemble techniques and time-series forecasting models to enhance prediction accuracy. They will also deploy the system with API endpoints, simulating real-world applications used by hedge funds and investment banks. Skills Covered: Time series forecasting (ARIMA, LSTM) Ensemble learning for boosting model performance API development for financial analytics services Real-world implementation of trading strategies Performance benchmarking against industry models Outcome: A production-ready financial analytics platform A research paper comparing ML-based market forecasting techniques A deployed API for real-time financial predictions