https://github.com/batthulavinay/tesla-stock-prices-predictions-lstm

This repository contains a project aimed at predicting Tesla's stock prices using Long Short-Term Memory (LSTM) networks.
https://github.com/batthulavinay/tesla-stock-prices-predictions-lstm

data-visualization deep-learning ltsm matplotlib numpy pandas python seaborn tensorflow train-test-using-sklearn

Last synced: 4 days ago
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This repository contains a project aimed at predicting Tesla's stock prices using Long Short-Term Memory (LSTM) networks.

• Host: GitHub
• URL: https://github.com/batthulavinay/tesla-stock-prices-predictions-lstm
• Owner: BatthulaVinay
• Created: 2025-01-17T10:20:51.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-02-13T09:53:26.000Z (7 days ago)
• Last Synced: 2025-02-13T10:34:25.928Z (7 days ago)
• Topics: data-visualization, deep-learning, ltsm, matplotlib, numpy, pandas, python, seaborn, tensorflow, train-test-using-sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 211 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Project Overview: Tesla Stock Price Prediction Using LSTM

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, training, and evaluation. 

### **Dataset**

The dataset utilized is named `TESLA.csv`, and contains historical stock prices with the following columns:

- **Date**

- **Close** (closing price)

### **Workflow**

#### **1. Data Preprocessing**

- **Loading the Dataset**: Use [Pandas](https://pandas.pydata.org/) 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.

#### **2. Visualization**

- **Historical Trends**: Create plots to visualize stock price trends over time using [Matplotlib](https://matplotlib.org/) and [Seaborn](https://seaborn.pydata.org/).

#### **3. Model Development**

- **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](https://www.tensorflow.org/).

#### **4. Evaluation**

- To quantify prediction accuracy, assess model performance using metrics such as Mean Squared Error (MSE).

### **Dependencies**

To successfully run this project, ensure you have the following Python libraries installed:

- [Pandas](https://pandas.pydata.org/)

- [NumPy](https://numpy.org/)

- [Matplotlib](https://matplotlib.org/)

- [Seaborn](https://seaborn.pydata.org/)

- [TensorFlow/Keras](https://www.tensorflow.org/)

You can install these libraries using:

pip install pandas numpy matplotlib seaborn TensorFlow

### **Results**

The project will produce:

- Visualizations comparing historical and predicted stock prices.

- Evaluation metrics demonstrating model accuracy.

### **Future Improvements**

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.

### **License**

This project is licensed under the MIT License, allowing for free usage and modification.