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Building a Stock Price Predictor using LSTM in Keras
Rating: 4.5 out of 5(17 ratings)
116 students

Building a Stock Price Predictor using LSTM in Keras

LSTM Stock Price Prediction — Time Series Forecasting, Deep Learning, Data Preprocessing, and Google Colab Deployment
Created byKarthik Karunakaran, Ph.D.
Last updated 4/2025
English

What you'll learn

  • Understand the fundamentals of time series forecasting with LSTM (Long Short-Term Memory) models
  • Collect and visualize stock price data using Yahoo Finance and Matplotlib
  • Preprocess financial data and apply feature scaling techniques
  • Create sequence datasets suitable for LSTM networks
  • Build and train an LSTM-based neural network using TensorFlow/Keras
  • Apply model checkpointing and early stopping for optimal performance
  • Make future predictions and rolling forecasts of stock prices
  • Visualize model performance and export predictions to CSV
  • Save trained models and scalers to Google Drive for future use
  • Evaluate model performance using RMSE and MAE metrics

Course content

2 sections • 28 lectures • 1h 9m total length

  • About the Project1:18
  • Real-World Applications3:36
  • Make money by learning and applying3:10

Requirements

  • Basic understanding of Python programming
  • A Google account to run and save files

Description

In this hands-on course, you'll learn how to build a complete Stock Price Prediction System using LSTM (Long Short-Term Memory) networks in Python — one of the most powerful deep learning architectures for time series data. Designed for learners with basic programming knowledge, this course walks you through real-world financial forecasting using historical stock market data.

You will begin with data collection from Yahoo Finance using yfinance, and learn how to preprocess and visualize stock price data with pandas, NumPy, and matplotlib. You’ll then dive deep into sequence modeling using LSTM from TensorFlow/Keras — a powerful neural network for capturing patterns in sequential data like stock prices. We will cover model architecture design, training strategies using early stopping and checkpointing, and advanced features such as rolling window forecasting and future prediction.

Additionally, you’ll learn how to deploy your project on Google Colab with GPU acceleration, and save models, scalers, metrics, and results directly to your Google Drive for seamless storage and access.

By the end of this course, you'll be equipped to develop your own time series forecasting tools — a valuable skill in finance, AI applications, and predictive analytics. Whether you're a student, developer, or aspiring data scientist, this project-based approach ensures you can apply your knowledge in the real world.

Who this course is for:

  • Data science and AI enthusiasts interested in time-series forecasting
  • Beginners and intermediate learners looking for a practical deep learning project
  • Finance professionals who want to understand stock prediction using neural networks
  • Students building academic or industry-ready projects
  • Anyone curious to learn how to forecast stock prices using real-world data and LSTM

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