Open AccessStock Picker using Machine Learning
Author(s) -
Nithya Kalyani M,
R Rajdev,
R. Santhosh,
Sai Eeshwar D,
G Sivakamasundari
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f9341.038620
Subject(s) - stock (firearms) , stock market , computer science , econometrics , random forest , stock market prediction , stock market index , machine learning , data mining , artificial intelligence , economics , engineering , geography , mechanical engineering , context (archaeology) , archaeology
The main objective of this paper is to build a model to predict the value of stock market prices from the previous year's data. This project starts with collecting the stock price data and pre-processing the data. 12 years dataset is used to train the model by the Random Forest classifier algorithm. Backtesting is the most important part of the quantitative strategy by which the accuracy of the model is obtained. Then the current data is collected from yahoo finance and the data is fed to the model. Then the model will predict the stock that is going to perform well based on its learning from the historical data. This model predicted the stocks with great accuracy and it can be used in the stock market institution for finding the good stock in that index.