/Stock-Price-Prediction

This project addresses the problem of predicting direction of movement of stock and stock price index for Indian stock markets. The study compares four prediction models, Gradient Boost Regression, Support Vector Machine (SVM), random forest regression and Reinforcement learning (Bagging Model) with two approaches for input to these models. The first approach for input data involves computation of ten technical parameters using stock trading data (open, high, low & close prices) while the second approach focuses on representing these technical parameters as trend deterministic data. Accuracy of each of the prediction models for each of the two input approaches is evaluated. Evaluation is carried out on specific years when stock prices rise or fall. Accuracy of each of the 4 machine learning algorithms is calculated and printed with the correlation between stock opening price and oil price.

Primary LanguagePython

Stock-Price-Prediction