Tesla Stock Price Forecasting (ML Pipeline)

A machine learning pipeline for forecasting Tesla's closing stock prices using:

• Linear Regression
• Support Vector Machine (SVM)
• LSTM (optional extension)

This project includes: ✅ Data preprocessing
✅ Sliding window supervised dataset creation
✅ Model training & evaluation
✅ Recursive future prediction
✅ Pytest unit tests
✅ GitHub Actions continuous integration

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Evaluation Metrics:

Mean Absolute Error (MAE)
Root Mean Squared Error (RMSE)
Visual Actual vs Predicted plots
Model comparison bar charts
Hyperparameter tuning with GridSearchCV and KerasTuner

## 📂 Project structure

my-tesla-ml-project/ ├── .github/workflows/python-tests.yml # GitHub Actions CI workflow ├── tests/test_ml_pipeline.py # Pytest unit tests ├── requirements.txt # Project dependencies ├── src/project_tesla.py # code └── README.md # Project documentation

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## ⚙️ Setup

### Clone the repository
```bash
git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name

Install dependencies

pip install -r requirements.txt

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🚀 Run the pipeline

You can run your ML code from src/ (or main notebook/script).
Example:

python src/your_ml_code.py

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🧪 Run tests

To run tests locally:

pytest -v tests/

GitHub Actions will run tests automatically on push / PR.

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📈 Example output

• Plots: Actual vs predicted prices
• Metrics: MAE, RMSE reported for test data
• Future forecasts: Recursive predictions for future closing prices

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🔑 Notes

• Ensure you have access to the Tesla stock CSV dataset (e.g. TSLA.csv).
• The pipeline assumes time series order (no shuffle in train/test split).

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🛠 TODO

• Add more features (Open, High, Low, Volume)
• Add stacked models / ensembles
• Add hyperparameter tuning for LSTM

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