Stock Market Prediction with High Accuracy using Machine Learning Techniques

Technical analysis of stock markets has remained a focal area of interest in recent years due to huge profit making opportunities. In recent years with the advent of various ML techniques, predicting models are more advanced and dynamic to handle the patterns within large sets of data. This research work is centered on a highly robust stock market prediction model using different types of machine learning algorithms such as decision tree, support vector machines, and neural networks. It is based on historical stock price records, different technical indicators and macro economic variables to predict the future stock price in an accurate manner. In this study, we discuss data preprocessing like; Normalization, feature selection, and handling missing values to guarantee the good input data. Accuracy of the predictions is the major criterion for comparing different ML algorithms, and a method for combining these algorithms is also proposed. The experiments presented here demonstrate that adding feature engineering on top of the neural network provides a much higher accuracy compared to other approaches. However, difficulties like overfitting of models, the requirement for prescient data, vagaries of stock markets due to unprecedented circumstances globally are also considered by this research. This analysis indicates that whilst machine learning does provide potential solutions in relation to stock predictions there is still a need for on-going improvement and updating for the techniques to become more effective if used in the long-term. The model described hereby gives high accuracy and can be very beneficial for investors who seek to make rational choices in the stock exchange market.