Stock Price Prediction using LSTM

• Tech Stack: Streamlit, Python, numpy, Sklearn, pandas, Tensorflow, RNN, LSTM
• Website URL: Link
• Github URL: Project Link

Predicting stock prices is an uncertain task which is modelled using machine learning to predict the return on stocks. There are a lot of methods and tools used for the purpose of stock market prediction. The stock market is considered to be very dynamic and complex in nature. An accurate prediction of future prices may lead to a higher yield of profit for investors through stock investments. As per the predictions, investors will be able to pick the stocks that may give a higher return.

Over the years, various machine learning techniques have been used in stock market prediction, but with the increased amount of data and expectation of more accurate prediction, the deep learning models are being used nowadays which have proven their advantage over traditional machine learning methods in terms of accuracy and speed of prediction.

In this task, we will fetch the historical data of stock data and fit the LSTM model on this data to predict the future prices of the stock.