Project Report: Stock Price Prediction

Project Report Link

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Stock Price Prediction

This repository contains a Jupyter Notebook focused on stock price prediction, utilizing machine learning techniques.

Features

• Data preprocessing and cleaning.
• Exploratory Data Analysis (EDA) with visualizations.
• Machine learning model training and evaluation.
• Performance metrics for model assessment.

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Technologies Used

• Jupyter Notebook
• Python
• Pandas, NumPy for data manipulation
• Matplotlib, Seaborn for visualization
• Scipy for statistical analysis
• Scikit-learn for machine learning

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Algorithms & Models Used

• Linear Regression (from scikit-learn) for stock price prediction.
• Train-Test Split (80% training, 20% testing) for model evaluation.
• Performance Metrics:
  • Mean Absolute Error (MAE)
  • Mean Squared Error (MSE)
  • R² Score

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Usage

• Modify and run code cells for different analyses.
• Update markdown cells for documentation purposes.
• Export the notebook to HTML or PDF as needed.

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Contact

For any inquiries, feel free to reach out via GitHub Issues.