Stock Market Price Prediction Using Machine Learning Algorithm

Stock Market Price Prediction Using Machine Learning Algorithm

ANITHA C1, BERNET ANTO M2 , RISHABAVARTHINI M 3 , LOGESH S4 , MRS. VISHNU PRIYA V V 5

1,2,3,4 B.E, Student Department Computer Science And Engineering, Angel College Of Engineering And Technology, Tirupur, Tamil Nadu, India.

5 Asst. Professor, Department Computer Science And Engineering, Angel College Of Engineering And Technology, Tirupur, Tamil Nadu, India.

ABSTRACT:

Stock market trading prediction involves forecasting the future movements of stock prices or determining whether to buy, sell, or hold a particular stock. Predicting stock market movements is a challenging task due to the dynamic and complex nature of financial markets. Various methods, including traditional statistical models and machine learning algorithms, can be employed for stock market trading prediction. Using Convolutional Neural Networks (CNNs) for stock market price prediction involves applying deep learning techniques to analyses historical price data and capture patterns that can assist in forecasting future price movements. While CNNs are more commonly associated with image recognition tasks, they can also be adapted for time-series data like stock prices. Classifying stock market trading involves predicting whether the price of a stock will go up, down, or remain stable. Machine learning algorithms can be applied to this task. Here's a general guide on how you might approach classifying stock market trading using machine learning like SVM, Decision Tree.

INDEXTERMS: Data preparation, Model architecture, Model training, Evaluation & predictions.