Welcome to the Stock Price Predictor repository! This project applies Machine Learning (ML) techniques to predict stock prices and visualize trends. It is designed for beginners interested in data science, financial analytics, and stock market analysis.

• Features
• Installation
• Usage
• Project Structure
• Requirements
• Model and Techniques
• Dataset
• Results
• Contributing
• License

• Historical stock price analysis using time series data.
• Implementation of ML algorithms for stock price prediction.
• Visualization of trends and predictive results using Power BI.
• Focused on delivering accurate predictions using:
  • Linear Regression
  • Decision Trees
  • Neural Networks (if applicable in the notebook).

1. Clone this repository:

   git clone https://github.com/yourusername/Stock-Price-Predictor.git
   cd Stock-Price-Predictor

2. Set up a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install the required Python libraries:

   pip install -r requirements.txt

Execute the main notebook Stock_Price_Predictor.ipynb to:

• Preprocess the dataset.
• Train ML models.
• Evaluate the model performance.
• Generate predictions.

jupyter notebook Stock_Price_Predictor.ipynb

• Export the predictions and trends as .csv files.
• Use the provided Power BI Template (if available) or design custom visuals.

.
├── Stock_Price_Predictor.ipynb    # Main Jupyter Notebook
├── requirements.txt               # Python dependencies
├── data/                          # Datasets (add your own dataset here)
├── results/                       # Predictions and saved models
├── visuals/                       # Power BI visuals (optional)
└── README.md                      # Documentation

• Python 3.8+
• Jupyter Notebook
• Key Python libraries:
  • pandas
  • numpy
  • scikit-learn
  • matplotlib
  • seaborn

Install all dependencies using:

pip install -r requirements.txt

• Preprocessing:

  • Missing value handling.
  • Feature engineering (if applicable).
  • Data scaling.

• Machine Learning Models:

  • Linear Regression for basic trend analysis.
  • Decision Trees for non-linear patterns.
  • Additional ML or Deep Learning techniques, if applicable.

• Include your dataset in the data/ directory.
• Ensure the dataset contains:
  • Date: Time series feature.
  • Price: Stock price values.
  • Additional features for enhanced modeling, such as volume or moving averages.

• Evaluate the model's accuracy using metrics like:

  • Mean Absolute Error (MAE).
  • Mean Squared Error (MSE).
  • R-squared (R²).

• Visualize predictions and actual trends for better insights.

Contributions are welcome! If you have suggestions for improving the accuracy or adding more features, feel free to:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-name).
3. Commit your changes (git commit -m 'Add feature').
4. Push to the branch (git push origin feature-name).
5. Open a Pull Request.

This project is licensed under the MIT License. See the LICENSE file for more details.

For questions, issues, or suggestions, reach out to:

• Email: naveenkumarmohanarajan38@gmail.com
• GitHub: https://github.com/naveenkm21