Time Series Data Classification for Precise Stock Market Price Prediction using ML

The use of previous historical stock price information in predicting future movements in prices is commonly referred to is "time period categorisation for stock price prediction" within the area of machine learning. There are many factors that affect stock values, and most of them are not easy to predict. However, classification of time series methods will be used to identify patterns in the data, leading to more precise projections of future price movements. After the machine learning principle been successfully skilled, & thus correlated for making prediction regarding the future price fluctuations of a fresh stock or a previous stock with an alternate price history. The model's output, a probability distribution across the conceivable changes in stock price, can be used to guide investment decisions. Classifying stock price predictions is an area where our suggested approach excels over prior art. The testing findings show that the suggested LSTM is 96.5% accurate with a loss of only 0.019. This study presents a potentially useful method for forecasting stock prices in today's dynamic corporate environment.