Indian Stock Markets Data Analysis and Prediction using Macroeconomics Indictors in Machine Learning
Jaskarn Singh1, Amit Chhabra2

1Jaskarn Singh, Student Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar (Punjab), India.
2Amit Chhabra, Assistant Professor, Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar (Punjab), India.
Manuscript received on July 24, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on August 10, 2020. | PP: 484-486 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J76150891020 | DOI: 10.35940/ijitee.J7615.0891020
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Abstract: Machine Learning plays a unique role in the world of stock market when it comes to the trend prediction. Machine learning library MLIB helps in determining the future values of stocks. With the help of this research one can find the ups and downs of stock market by providing a signal for the same and done by analyzing the previous stock data. This study is based on analysis of stock data from 2000 to 2009 which includes top fifty companies of various sectors from all over India. Six stock data indicators known as, Bollinger Band, Relative Strength Index(RSI), Stochastic Oscillator, Williams % R, Moving Average Convergence Divergence (MACD), Rate of Change applied on the nineteen years of stock data then results of these indicators are compiled and finally with the use of machine learning libraries like Numpy, Pandas, Matplotlib, Sklearn a random forest algorithm is applied on the compiled result to predict the stock movement , these libraries which splits the results into two sets training set and testing set which also boost up the result and gives you the better prediction. 
Keywords: Random Forest, Stock, Machine Learning.
Scope of the Article: Machine Learning