Welcome back to the Continuous Improvement podcast, where we explore the latest trends, tools, and techniques in technology and personal growth. I’m your host, Victor Leung. Today, we’re diving into an exciting area of machine learning—using Recurrent Neural Networks, specifically LSTM layers, to predict stock prices. If you’re interested in financial markets and data science, this episode is for you!

In this episode, we’ll walk through the process of building an LSTM-based RNN to predict the stock price of Nvidia, leveraging historical data to make informed predictions. Let’s get started!

To begin, we use a dataset containing historical stock prices of Nvidia, or NVDA, including other related financial metrics. The dataset is divided into training and testing sets, with data before January 1, 2019, used for training, and data after this date reserved for testing. This split ensures our model is trained on historical data and validated on more recent data to assess its predictive power.

We load the dataset, convert the date into a proper format, and split it into training and testing sets. This foundational step ensures our model has a reliable dataset to learn from and be evaluated on.

Next, we build our LSTM model using TensorFlow’s Keras API. Our model comprises four LSTM layers with varying units, each followed by a dropout layer to prevent overfitting. The final layer is a dense layer, responsible for outputting the predicted stock price.

This architecture allows the model to capture complex temporal dependencies in the data, crucial for predicting stock prices, which are inherently sequential.

Once the model architecture is set, we train it on the training data. Training involves optimizing the model parameters to minimize the loss function, in our case, the mean squared error between the predicted and actual stock prices. We use a batch size of 32 and train the model for 10 epochs.

This process helps the model learn the underlying patterns in the historical data, enabling it to make predictions on unseen data.

Before making predictions, we prepare the test data similarly to the training data, including scaling and creating sequences. This step is crucial to ensure the model’s predictions are comparable to actual stock prices.

By standardizing the data and creating sequences, we align the input format with the model’s training conditions, improving prediction accuracy.

With our model trained and test data prepared, we proceed to make predictions. These predictions are then scaled back to the original data range to compare them accurately with actual stock prices.

Scaling the predictions allows us to visualize and evaluate the model’s performance against real-world data.

Finally, we visualize the predicted stock prices against the actual stock prices. This visualization is a critical step in assessing the model’s accuracy and understanding its strengths and weaknesses.

The comparison between predicted and actual prices provides valuable insights into the model’s performance, highlighting areas for improvement and refinement.

Building an RNN with LSTM layers for stock prediction is a powerful technique, leveraging the ability of LSTM networks to capture long-term dependencies in sequential data. This approach can be adapted to various types of sequential prediction tasks, making it a versatile tool in your machine learning toolkit.

Thank you for joining me on this episode of Continuous Improvement. I hope you found this exploration of LSTM-based stock prediction insightful and inspiring. If you have any questions or topics you’d like me to cover in future episodes, feel free to reach out. Don’t forget to subscribe and leave a review if you enjoyed the show. Until next time, keep learning and improving!