Evaluation of Stocks to find Multibaggers using Decision Tree Algorithm in Python

Abstract

The idea is to identify a multibagger trade by researching past Multibagger Shares and their characteristics. The decision tree approach is utilized for carrying out statistical operations like data discovery, text extraction, and erroneous information detection in a class. In this article, researchers sought to analyze information on the stock market over the last 20 years. Samples containing basic and technological data are taken into account. The techniques of machine learning and data science methods are used in this investigation. Machine learning is highly efficient in many industries for automating activities that used to require human labor. One such application of ML involves predicting whether or not a certain deal will be profitable. In this study, the stock analysis of Tata Motors, Indian Oil, and Tata Steel are utilized as demonstrations. The findings from this study will assist investors in making faster and better judgments so that they can make money from the investments they make.