Analyzing and predicting banking share dataset using data mining approaches

Analyzing and predicting banking share dataset using data mining approaches

연구 분야: Databases

학회: Multimedia Tools and Applications

Data mining is a process of finding and extracting hidden information from a larger dataset. It is used to analyse data patterns in volume of data using one or more data mining techniques. Data mining has applications in multiple fields, like science, engineering, banking, share marketing, and various research fields. A stock market is similarly called a share market. The stock market helps you trade financial instruments and shares of companies and allows trading of shares. Machine learning, within the realm of artificial intelligence (AI), is dedicated to crafting algorithms and models that empower computers to acquire knowledge from data and subsequently make predictions. In this paper, analysis and prediction consider State Bank of India (SBI) shares using different parameters, namely trading date, price, high, low, volume, and changes in share. The analysis and suitable parameters for prediction using supervised machine learning approaches, namely linear regression, random forest, random tree, and REP tree. Numerical illustrations also prove the results and discussions using different parameters.