Stock Price Prediction using LSTM

A vital component of financial research is stock price prediction, This helps investors and traders make informed decisions about buying or selling stocks. This study proposes a model for predicting stock prices that predicts the closing price of Google stocks by using Recurrent Neural Networks and Long Short- Term Memory. The Google stock dataset is used to train the suggested model at first. The Yahoo Finance API was used to gather historical stock data over a 20-year span. To give trend insights, important parameters including percentage changes, adjusted closing prices, and moving averages for 100 and 250 days were calculated. Sequences of 100 days were utilized as input to predict the following day's stock price following the dataset had been preprocessed by standardizing the values using MinMaxScaler. The LSTM-based model, including two LSTM layers succeeded by dense layers was assessed using 30% of the dataset after being trained on 70% of it. The model demonstrated strong effectiveness with a RMSE of roughly 4.31, signifying its precision in predicting stock prices. The predictions were then inversely changed to conform to the initial stock price scale for verification. To improve prediction accuracy, we also employed a range of machine learning (ML) models, including Decision Tree, Random Forest, XGBoost, &Voting Classifier. Furthermore, Streamlit was utilized to deploy the model in an intuitive interface that let users enter stock IDs and view projected prices in addition to moving averages. This study shows how well LSTM handles time-series data and how it may be used for real-time stock market forecasting and decision-making.