Analyzing Portfolio of Biotech Companies and Predicting Stock Market using Machine Learning

Prediction of Stock is one of the most demanding and fascinating topics nowadays. With emerging Machine Learning, various techniques and algorithms are derived to predict the market movement. Stock Prediction is challenging in itself because of the number of discrete variables that are involved. This paper presents an analysis of Stock Prediction of around 250 Biotech Companies. Furthermore, it draws a detailed analysis of the techniques used in this process. Diverse techniques like Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Logistic Regression (LR) are used to determine the most effective and desired output. These algorithms are accessed, evaluated, and analyzed thereafter deployment of a web application is done and the conclusion is drawn. At last, we present the scope for further research in this field.