Stock Price Prediction using LSTM | Deep Learning Project in Tamil | Python Tutorial

Welcome back to our Deep Learning in Tamil series. In this video, we are diving deep into one of the most practical and highly demanded real-world applications of deep learning — Stock Price Prediction using LSTM. If you have watched the previous video where we clearly understood the theory and working of LSTM networks from scratch in Tamil, this video will take you one step ahead by applying that knowledge to a complete end-to-end project. We will build a stock price forecasting model with Python, TensorFlow, Keras, and financial market data. This tutorial is specially designed for students, developers, machine learning beginners, working professionals, and anyone who wants to understand how deep learning can be used to analyze stock market trends. Even if you are new to time series, don’t worry, every concept is explained in simple Tamil so you can follow along without confusion. In this session, we start by collecting real stock historical data using Yahoo Finance, preparing the dataset, scaling values for neural network training, splitting into training and testing sets, and converting raw values into sequences suitable for LSTM input shape. You will learn why LSTM is widely used for sequential forecasting and how memory cells help the model understand past price movements to predict future trends. Together, we will write Python code step by step, build the LSTM architecture, compile and train the model, evaluate the results, and finally visualize the prediction graph to compare real vs predicted stock movement. You will understand not just how to train, but also how to tune parameters, how time windows affect accuracy, and how we can further improve predictions using additional data features like volume, indicators, or sentiment. The goal is to make you confident in handling any time series prediction project in the future. By the end of this video, you will have a complete Stock Market Price Prediction model using Deep Learning LSTM, running on your machine.