• Devi.P.P
• Aarthy.N

Generally, predicting how the stock market will perform is one of the most difficult things to do. It can be described as one of the most critical process to predict that. This is a very complex task and has uncertainties. To prevent this problem in One of the most interesting (or perhaps most profitable) time series data using machine learning techniques. Hence, stock price prediction has become an important research area. The aim is to predict machine learning based techniques for stock price prediction results in best accuracy. The analysis of dataset by supervised machine learning technique (SMLT) to capture several information’s like, variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparing and data visualization will be done on the entire given dataset. To propose a machine learning-based method to accurately predict the stock price Index value by prediction results in the form of stock price increase or stable state best accuracy from comparing supervise classification machine learning algorithms. Additionally, to compare and discuss the performance of various machine learning algorithms from the given dataset with evaluation of GUI based user interface stock price prediction by attributes. dataset with evaluation classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with precision, Recall and F1 Score.

Stock Price Prediction, SMLT, Dataset

"A SYSTEM FOR STOCK MARKET ANALYSIS AND REAL TIME PREDICTION USING SUPERVISED LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 12, page no.a14-a17, December-2021, Available :http://www.jetir.org/papers/JETIR2112004.pdf

"A SYSTEM FOR STOCK MARKET ANALYSIS AND REAL TIME PREDICTION USING SUPERVISED LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 12, page no. ppa14-a17, December-2021, Available at : http://www.jetir.org/papers/JETIR2112004.pdf