Stock Price Prediction using Machine Learning in Python This project implements a machine learning model to predict stock prices using historical data. The goal is to forecast future stock prices based on various features such as historical prices, and other relevant data.

Features: Data Collection: Utilizes publicly available stock price datasets. Data Preprocessing: Data cleaning and transformation techniques are applied to make the dataset ready for training. Feature Engineering: Various features like moving averages, Relative Strength Index (RSI), and others are extracted to improve prediction accuracy. Model Selection: Multiple machine learning models, such as Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks, are tested and compared for performance. Model Training & Evaluation: The models are trained on the dataset and evaluated based on metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared. Prediction: Once trained, the model makes predictions on future stock prices. Requirements: Python 3.x pandas numpy matplotlib seaborn