Tesla Stock Price Prediction Using LSTM

Overview

This project demonstrates how to predict Tesla's stock prices using a Long Short-Term Memory (LSTM) model with TensorFlow. It includes steps for loading data, preprocessing, model creation, training, evaluation, and visualization of the predictions compared to actual stock prices.

Requirements

To run this project, you will need the following libraries:

• pandas
• numpy
• scikit-learn
• TensorFlow
• matplotlib

You can install these libraries using pip: pip install pandas numpy scikit-learn tensorflow matplotlib

Dataset

The dataset used in this project is Tesla's stock prices obtained from a CSV file named TSLA.csv. The CSV file should contain daily stock prices with at least the following columns: Date, Open, High, Low, Close, Volume.

Usage

1. Prepare the Data: Place your TSLA.csv file in the project directory.
2. Run the Code: Execute the provided Python script to train the LSTM model and predict Tesla's stock prices.
3. Visualize Predictions: The script will generate plots showing the actual vs. predicted stock prices.

Code Structure

• Data Loading and Preprocessing: The script starts by loading the Tesla stock price data from a CSV file, focusing on the Close prices. The data is then normalized using MinMaxScaler.
• Creating Sequences: A helper function create_dataset is used to create sequences from the time series data, which are used as input for the LSTM model.
• Model Creation: An LSTM model is defined using TensorFlow's Keras API, consisting of two LSTM layers followed by two dense layers.
• Training: The model is trained on the preprocessed dataset.
• Prediction and Evaluation: The script predicts stock prices on the training and test datasets and evaluates the model's performance using RMSE.
• Visualization: Finally, the actual vs. predicted prices are plotted using matplotlib.

Results

After running the script, you will see a plot comparing the actual Tesla stock prices with the predictions made by the LSTM model. The console will also display the RMSE values for both the training and test datasets.

Customization

You can customize the LSTM model, adjust the sequence length, modify the train-test split ratio, or experiment with different hyperparameters to improve the model's performance.

License

This project is open-source and available under the MIT license.