This project involved building a stock price prediction tool using Python and machine learning. Historical stock data was fetched using the yfinance API and processed to predict the next day's closing price. It focused on making short-term forecasts using real-world financial data. A Linear Regression model was trained on the closing prices and evaluated using Mean Squared Error to assess prediction accuracy. The dataset was split into training and testing sets to validate performance. Visualization was done using matplotlib to compare actual vs predicted prices. The project helped me understand time series forecasting, regression modeling, and data preprocessing. It also strengthened my skills in model evaluation and working with structured datasets. This task demonstrated how AI can support decision-making in financial applications.