Stock Price Prediction System Using Machine Learning: Final-Year Project Guide

1. 1Collect historical stock price data using APIs or web scraping
2. 2Preprocess the data by handling missing values and normalizing it
3. 3Train a machine learning model using the preprocessed data
4. 4Evaluate the model's performance using metrics such as mean absolute error or mean squared error
5. 5Deploy the model as a web application or API for real-time predictions

Data scientists and machine learning engineers can work together to develop and deploy this system, while product managers can oversee the project's scope and timeline

💡 Machine learning can be used to predict stock prices by analyzing historical data and patterns

Build a stock price prediction system using ML for your final-year project #machinelearning #stockmarket