This project aims to predict Tesla's stock prices by implementing Long Short-Term Memory (LSTM) networks. The workflow encompasses data loading, preprocessing, visualization, model building, and evaluation.
The dataset utilized is named TESLA.csv, and contains historical stock prices with the following columns:
- Date
- Close (closing price)
- Loading the Dataset: Use Pandas to load the CSV file.
- Date Conversion: Convert the Date column to a datetime format and set it as the index.
- Column Management: Drop unnecessary columns for analysis.
- Historical Trends: Create plots to visualize stock price trends over time using Matplotlib and Seaborn.
- Data Normalization: Normalize the data to prepare it for LSTM input.
- Train-Test Split: Divide the dataset into training and testing sets to evaluate model performance.
- LSTM Model Creation: Build and train the LSTM model using TensorFlow/Keras.
- To quantify prediction accuracy, assess model performance using metrics such as Mean Squared Error (MSE).
To successfully run this project, ensure you have the following Python libraries installed:
You can install these libraries using: pip install pandas numpy matplotlib seaborn TensorFlow
The project will produce:
- Visualizations comparing historical and predicted stock prices.
- Evaluation metrics demonstrating model accuracy.
To enhance the model's predictive capabilities, consider:
- Incorporating additional features like trading volume or external market indicators.
- Experimenting with different deep learning architectures or hyperparameter tuning for better performance.
This project is licensed under the MIT License, allowing for free usage and modification.