Akshaya Reddy Ragi

This project focuses on predicting stock market trade volumes using machine learning techniques and financial market data. The dataset was collected from multiple financial sources including Alpha Vantage, Yahoo Finance, Nasdaq Trader, and FRED using API access and web scraping methods. The collected data included features such as adjusted closing price, market capitalization, exchange information, interest rates, and trading volume indicators. The data was cleaned, transformed, and prepared for predictive modeling. Three machine learning models were implemented: Linear Regression, Random Forest, and Gradient Boosting. The dataset was divided into training and testing sets to evaluate model performance. The results showed that ensemble learning models performed better than traditional regression. The tuned Random Forest model achieved the highest predictive performance with an R² score of 0.9273, demonstrating strong capability in predicting stock market trade volumes.

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