Stock_Prediction

Zero to Deployment in 'TSLA' Stock prediction based analytical and trading volume.

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Tesla Stock Price Prediction (Zero to Deployment)

Overview

This project builds a time series forecasting model to predict Tesla's stock prices using LSTM (Long Short-Term Memory) networks. The model is trained on historical stock data and deployed as an API using FastAPI with Uvicorn.

Features

  • Data Collection: Fetching historical Tesla stock prices.
  • Data Preprocessing: Normalization, feature engineering, and sequence generation.
  • Model Training: Implementing LSTM with TensorFlow/Keras.
  • Evaluation & Optimization: Hyperparameter tuning and performance analysis.
  • Deployment: Serving predictions using FastAPI.

Dataset

  • Source: Yahoo Finance
  • Data: Tesla (TSLA) stock prices
  • Features: Date, Open, High, Low, Close, Volume

Tech Stack

  • Python
  • TensorFlow & Keras
  • Pandas & NumPy
  • Scikit-learn
  • Matplotlib & Seaborn (for visualization)
  • FastAPI & Uvicorn (for API deployment)
  • Docker (optional for containerization)

Installation

  1. Clone this repository: bash git clone https://github.com/yourusername/tesla-stock-prediction.git cd tesla-stock-prediction
  2. Install dependencies: bash pip install -r requirements.txt

Model Training

Run the training script:

python train.py

This will:

  • Load and preprocess the Tesla stock dataset
  • Train an LSTM model
  • Save the trained model as model.h5

Deployment

  1. Start the FastAPI server: bash uvicorn app:app --reload
  2. API Endpoint: bash GET /predict?days=5 Example request: bash curl -X GET "http://127.0.0.1:8000/predict?days=5" Example response: json { "predicted_prices": [880.45, 890.12, 905.67, 915.32, 925.89] }

Future Improvements

  • Integrate Sentiment Analysis on news headlines for better forecasting.
  • Implement GRU or Transformer-based models for comparison.
  • Deploy on Cloud (AWS/GCP/Azure) for real-time inference.

Contributing

Feel free to fork this repository and submit a pull request if you have any improvements!

License

This project is licensed under the MIT License.

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