Authors - S.K. Manjula Shree, Shreya Vytla, J. Santharoopan, Harisudha Kuresan, A.Anilet Bala, D.Vijayalakshmi Abstract - The goal is to use a Random Forest classifier to categorize future price movements as "up" or "down" in order to forecast stock market trends. In order to guide investing strategies, this model will examine pertinent attributes and previous stock data. The effectiveness of Logistic Regression, Support Vector Machines (SVM), and Random Forest Classifier in forecasting stock market movements is compared in this study. The ensemble approach Random Forest is very resilient under erratic market situations since it is excellent at handling noisy, complex data and capturing non-linear patterns. SVM performs best on smaller, more structured datasets, however noise and non-linearity might be problematic. Despite its simplicity and interpretability, logistic regression is constrained by its linear character and finds it difficult to account for the dynamic, non-linear behavior of stock prices. In recall focused tasks, logistic regression is helpful because it performs well in identifying true positives (such preventing missed opportunities in stock predictions). SVM's reliance on kernel functions makes it computationally expensive, but it can also be helpful when handling smaller datasets with clear patterns and where accuracy is needed. All things considered, Random Forest offers the greatest results around 99% especially for difficult stock market prediction assignments.
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