Welcome to the Stock Market Prediction using LSTM project! This repository contains the code and resources for predicting stock market trends using Long Short-Term Memory (LSTM) neural networks. With the power of deep learning, we aim to forecast stock prices and make informed investment decisions.
In this project, we leverage historical stock market data to train an LSTM model. The model learns from past price patterns and trends, enabling it to predict future stock prices. The LSTM network is specifically designed to capture long-term dependencies and has proven to be effective in time series forecasting tasks.
Recurrent Neural Networks (RNNs) are the time lords of the neural network world! They're designed to work with sequential data, making them perfect for tasks like stock price prediction.
Key features:
- 🎭 Process input sequences of any length
- 💾 Maintain an internal state (memory)
- 🔄 Share parameters across time steps
The Vanishing Gradient Problem is the arch-nemesis of deep neural networks, especially RNNs. It's like trying to whisper a message through a long line of people - by the time it reaches the end, the message is lost!
Key points:
- 🔬 Gradients become extremely small during backpropagation
- 🐌 Earlier layers or time steps learn very slowly
- 🕰️ Particularly problematic for long-term dependencies
- 🧠 Makes it difficult for the network to learn from distant past
Long Short-Term Memory (LSTM) networks are the superheroes that save us from the vanishing gradient problem! They're specially designed to capture long-term dependencies in sequential data.
Key features:
- 🗃️ Introduce a memory cell for long-term information storage
- 🚪 Use gating mechanisms to control information flow
- 🧠 Can learn to store relevant information for long periods
- 📚 Effective for tasks requiring understanding of long-term context
We use a publicly available dataset containing historical stock prices of various companies. The dataset includes features like opening price, closing price, volume, etc. We preprocess the data, splitting it into training and testing sets, and perform any necessary data transformations.🫡
The LSTM model is built using deep learning frameworks like TensorFlow or PyTorch. We train the model on the training dataset, adjusting hyperparameters such as the number of hidden layers, the number of neurons per layer, and the learning rate. We employ techniques like regularization and dropout to prevent overfitting.
Once the model is trained, we evaluate its performance on the testing dataset. We compute various metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) to assess the model's accuracy. We visualize the predicted stock prices alongside the actual prices to gain insights into the model's performance.
To run the project locally, follow these steps:
- Clone this repository:
git clone https://github.com/034adarsh/Stock-Price-Prediction-Using-LSTM - No need to download any dataset, this project uses Yahoo finance library to directly fetch data, just write the correct company code.
- Run the
training codescript in the notebook to train the LSTM model. - Run the
predict codescript in the notebook to make predictions on new data.
In this section, I present the results of our stock market prediction experiments. I discuss the model's performance, its strengths, limitations, and potential areas of improvement. I also provide visualizations of the predicted stock prices and compare them with the actual prices.
I welcome contributions to enhance the project and make it even more effective. If you have any suggestions, bug fixes, or new features to add, please submit a pull request. I appreciate your contributions!
This project is licensed under the MIT License.
For any questions or inquiries, feel free to reach out to me:
- Email: [email protected]
- LinkedIn: Sai Abhiram Bussa
Let's make accurate stock market predictions together!
Thank you for visiting my project repository. Happy predicting! 😇