Tesla Stock Price Prediction using RNN | Deep Learning Project in Tamil | AI in Stock Market скачать в хорошем качестве

Tesla Stock Price Prediction using RNN | Deep Learning Project in Tamil | AI in Stock Market

In this video, we will build a complete Stock Price Prediction Project using Recurrent Neural Networks (RNN) — explained clearly in Tamil. This is a practical continuation of our Deep Learning series in Tamil, where we have already explored the concepts of RNN and the drawbacks of RNN in the previous videos. If you are a Tamil learner who wants to understand how deep learning can be applied to real-world financial data such as stock prices, this video will guide you step by step. We take the Tesla stock dataset (TSLA) and train an RNN model to predict future stock prices based on historical data. You will understand how time series data is preprocessed, how to convert it into supervised learning form using timesteps, and how RNNs capture sequential patterns over time. The goal of this project is to help Tamil students and beginners see how deep learning models work beyond theory and can be applied to real data science and machine learning projects. Throughout the video, we will cover how to scale the data, prepare training and testing sets, and feed the inputs into the RNN. You will also learn about model layers such as SimpleRNN, Dropout, and Dense layers, and understand how the number of units, timesteps, and input shape affect model performance. The project demonstrates how the trained RNN model predicts Tesla’s stock “Open” value, how to visualize the results, and how to interpret model predictions in practical terms. This Tamil deep learning tutorial not only explains coding but also focuses on the logic behind every step, helping you truly understand how RNNs work for stock market prediction. We will discuss why RNNs are suitable for sequential data like stock prices and how they remember past information through hidden states. You will also see how scaling and inverse transformations are applied to make the final predictions interpretable. By the end of the video, you will be able to: Build a working RNN model from scratch using Python and Keras Use past stock data to predict future trends Understand the impact of timesteps, sequence length, and hidden units Visualize predicted vs actual stock prices using Matplotlib This video is entirely in Tamil and is designed for college students, beginners in machine learning, and Tamil audiences who prefer learning deep learning concepts in their native language. Whether you are preparing for placements, working on projects, or trying to strengthen your understanding of neural networks, this video gives you a hands-on experience with a real project. Make sure you have watched the previous two videos in this Deep Learning in Tamil series to fully understand the background of RNNs before moving to this implementation. This stock prediction project forms a strong foundation before we explore more advanced architectures like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units) in upcoming videos. Dataset : https://www.kaggle.com/datasets/boris... Code : https://github.com/AdityaTheDev/AdiEx... If you find this tutorial helpful, don’t forget to subscribe to the channel, Adi Explains, and stay tuned for the upcoming videos in the Deep Learning series. Each video is structured to make complex machine learning and deep learning topics simple and understandable in Tamil, bridging the gap for Tamil students to learn world-class AI concepts effectively. #python #tamil #coding #programming #project #college #machinelearning #deeplearning #deeplearning #nlp#education #artificialintelligence