📈 Stock Price Prediction using Machine Learning

This project focuses on predicting stock prices using historical market data. Various models such as Linear Regression, LSTM, and Random Forest are explored to evaluate performance on time-series financial data.

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📁 Dataset

• Historical stock price data (CSV format)
• Columns include: Date, Open, High, Low, Close, Volume
• You can use data from Yahoo Finance, NSE/BSE, or Alpha Vantage

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🧰 Tools & Libraries

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn
• TensorFlow / Keras (for LSTM models)

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📊 Project Highlights

• Data cleaning & feature engineering
• Visual analysis of stock trends
• Building ML models for price prediction
• Evaluating model performance (MAE, RMSE, R²)
• Predicting future prices (1-day / n-day ahead)

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📈 Models Implemented

• Linear Regression
• Random Forest Regressor
• LSTM (Long Short-Term Memory)

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🚀 How to Run

1. Clone this repository
2. Install required packages using pip
3. Run the Jupyter Notebook: stock_price_prediction.ipynb