From Data to Dashboard: Predicting Stock Market Trends using LSTM

In this talk, I will explain how I built a stock market trend prediction system using deep learning. The goal of the project is to predict whether the S&P 500 index will move up or down in the next time period using historical data.

I will walk through how the data is collected and preprocessed, and how it is converted into sequences using a rolling window approach. I will also explain the use of technical indicators like Simple Moving Average (SMA) and Relative Strength Index (RSI) to improve the model’s performance.

The model is built using Long Short-Term Memory (LSTM) networks in TensorFlow. I will also explain how the model is trained and evaluated, along with challenges such as overfitting and handling noisy financial data.

I will demonstrate a live Streamlit web application where users can visualize trends and see prediction probabilities through an interactive dashboard.

Finally, I will share key learnings from building this project and how beginners can start developing similar real-world machine learning applications.

• Understand how LSTM models are used for time-series prediction

• Learn how to preprocess financial data and create input sequences

• Explore the use of technical indicators like SMA and RSI

• Get insights into challenges like overfitting and data volatility

• See how to deploy ML models using Streamlit for real-time visualization