COMPARITIVE ANALYSIS OF MACHINE LEARNING ALGORITHM TO FORECAST INDIAN STOCK MARKET

Abstract

The Indian stock market is highly dynamic and influenced by various economic, political, and global factors, making accurate prediction a challenging task. Traditional statistical models often fail to capture the complex and non-linear patterns present in stock price movements. This project focuses on performing a comparative analysis of different machine learning algorithms to forecast stock prices in the Indian stock market. The system utilizes historical stock data such as opening price, closing price, high, low, and trading volume obtained from stock exchanges like NSE and BSE. Data preprocessing techniques such as normalization, handling missing values, and feature engineering are applied to improve model performance. Machine learning algorithms including Linear Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks are implemented and compared. Experimental results show that advanced models such as Random Forest and LSTM outperform traditional models in capturing market trends and improving prediction accuracy. The system is implemented using Python in Jupyter Notebook and deployed using a web interface for real-time forecasting. This study helps investors and analysts make informed decisions by providing reliable stock price predictions.