Stock Price Prediction using GRU | Deep Learning Project in Tamil | Gated Recurrent Unit

Welcome back to our Deep Learning in Tamil series. In this video, we take the next practical step after understanding the theory of GRU by building a complete real-world project on Stock Price Prediction using Gated Recurrent Units. In the previous video, we clearly explained how GRU works internally, how update and reset gates control information flow, and why GRU is considered a faster and simpler alternative to LSTM. If you have watched that video, this session will help you apply those concepts practically by implementing a full end-to-end deep learning project using Python. The focus of this tutorial is not just on coding, but on understanding how GRU models learn from historical stock price data and how we can use them to predict future price trends in a time-series forecasting problem. In this video, we start from scratch by collecting real stock market data and preparing it for deep learning. You will learn how to preprocess financial time-series data, handle missing values, scale numerical features correctly, and convert raw price values into sequential input suitable for GRU networks. Since GRU expects sequential data, we explain clearly how sliding windows work and how past time steps influence future predictions. All these steps are explained slowly and intuitively in Tamil, making this tutorial beginner-friendly while still being valuable for intermediate learners. We then build the GRU model using TensorFlow and Keras, define the architecture, choose appropriate hyperparameters, compile the model, and train it on historical stock price data. As the training progresses, you will understand how GRU learns temporal patterns in stock prices and how its simpler architecture helps in faster convergence compared to LSTM. We also discuss why GRU can sometimes perform equally well or even better than LSTM for stock price prediction, especially when the dataset is limited. After training, we evaluate the model, generate predictions on unseen test data, and vis