A Machine Learning Model for Stock price Prediction. stock market prediction is that the act of making an attempt to determine the future value of a corporation stock or different financial instrument listed on a money exchange. ... proposed model is predicated on the study of stocks historical data and technical indicators.
Stock market prediction is the act of making an attempt to determine the future value of a corporation stock or different financial instrument listed on a financial exchange. The successfull prediction of a stock's future price can maximize investor’s gains.
STOCK price prediction has been at focus for years since it will yield vital profits. Predicting the stock market is not a straightforward task, primarily as a consequence of the about to radom-walk behaviour of a stock time series. fundamental and technical analyses were the primary two methods wont to forecast stock prices. Artificial Neural networks (ANNs) is the most normally used technique . In most cases ANNs suffer from over-fitting problem due to the big range of parameters to repair, and therefore the very little previous user data regarding the connection of the inputs in the analysed problem . Also, Support vector machines (SVMs) had been developed as another that avoids such limitations. Their practical successes will be attributed to solid theoretical foundations supported VC-theory . SVM reason globally best solutions, in contrast to those obtained with ANNs, that tend to represent local minima .
Keywords:
Machine Learning, stock market, Artificial neural networks, Bombay stock exchange, Support vector machine.
libraries that we will need for this program
import quandl
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.svm import svm
from sklearn.model_selection import train_test_splitGramm