Stock Prediction Using Artificial Intelligence

Machine learning is a novel strategy that processes massive volumes of data. In machine learning, K-nearest neighbours (KNN) and linear regression models are frequently employed for a range of analytical and predictive uses. KNN is a powerful, easy-to-use, and very successful technique for handling classification problems. Based on their proximity to one another, data points are grouped. However, the best method for predictive modelling is linear regression as it creates a linear relationship between the input and output variables. By contrasting these models using publicly accessible datasets, one may ascertain the models’ efficacy in various contexts as well as their advantages and disadvantages. KNN and linear regression are important tools in a data scientist’s toolkit because of how well they handle non-linear data patterns and how well they may give clear insights into data trends. Sophisticated machine learning models like Random Forests, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks are frequently used to forecast future stock closing values.