Building a Stock Price Prediction Model using LSTM

Project Overview

In this project, I demonstrate a comprehensive application of Long Short-Term Memory (LSTM) models to predict the future closing price of stocks given historical data. The selection of stocks includes prominent names like BlackRock, Goldman Sachs, JPMorgan Chase, and D. E. Shaw Group.

The project encompasses the entire lifecycle of a machine learning project, starting from data collection and pre-processing, model building and training, to evaluation and prediction.

Key Components

1. Data Collection

The first step was utilizing the Yahoo Finance API to download historical stock data for selected companies, including open, high, low, close, and volume information for each trading day.

2. Data Preprocessing

Preprocessing involved scaling the closing prices of stocks between 0 and 1, ensuring uniformity in the data and enabling effective model learning.

3. Model Building

A LSTM model was used, consisting of two LSTM layers followed by dropout layers to prevent overfitting, and two Dense layers. LSTM networks are particularly suitable for time series data due to their ability to remember long-term dependencies.

4. Model Training

The model was trained using:

• Adam optimizer
• Mean Squared Error (MSE) as the loss function

5. Model Evaluation

Performance was assessed using Mean Squared Error (MSE) between actual and predicted stock prices.

6. Prediction

The final step involved using the trained LSTM model to predict future stock closing prices.

Getting Started

To use this LSTM stock price prediction model:

1. Clone the repository: git clone https://github.com/DhruvAjayToshniwal/Stock-Price-Prediction-Using-LSTM.git
2. Install required libraries: pip install -r requirements.txt
3. Customize scripts for stock symbols, date range, and model parameters
4. Run scripts and review results

Wrapping Up

This LSTM model allows users with basic Python knowledge to build and test stock price prediction models, potentially gaining insights into stock trading.

Future work includes:

• Improving model performance by experimenting with different architectures
• Exploring additional data sources like company financials
• Developing a comprehensive trading strategy based on predictions