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Stock Market Prediction Using Linear Regression

Ankan Ghosh, Kohinoor Mallick, Saikat Khamaru, Rudrasom Shee, Aritra Das, Dr. Anindita Mukherjee

Abstract


Stock prediction analysis is crucial for investors to make informed decisions in financial markets. This project explores the application of linear regression in predicting stock prices using Python. Leveraging Python's strong libraries such as NumPy, Pandas, and scikit-learn, we preprocess historical stock data, select relevant features, and train linear regression models. Through thorough evaluation and prediction, we aim to identify trends and patterns in stock data, enabling more accurate forecasting of future stock prices. By conducting stock prediction analysis using linear regression with Python, this project seeks to provide investors with valuable insights and tools for navigating the complexities of financial markets and making informed investment decisions.


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