抽象的

Prediction Using Back Propagation and k- Nearest Neighbor (k-NN) Algorithm

Tejaswini patil, Karishma patil, Devyani Sonawane, Chandraprakash

Prediction of Stock Prices is not only inquisitiveness but also the very challenging topic. This paper intension is predict stock prices for sample of some major companies using back propagation and k-nearest neighbor algorithm, to help out executive, investors, user and choice makers in making valuable decisions. Stockpile market give lots of profit or benefit with low risk because it is treating as memorable field. For business researchers and data mining the stock market is most suitable environment because of its large and continually changing information. Predicting stock price with traditional time it has been proven easier done. An artificial neural network might be more compatible for task primarily because, neural network is more calibers to predict stock prices more accurate than current using technique. It also takes out huge amount of information from different sources. We have study architecture of neural network. We will build best model by analyzing various parameter of neural network and also study supplementary model to compare accuracy of model in terms of error rate price, turnover as input. Input is previous stock data and output is future stock price prediction.