Long-term research has focused heavily on the prediction of stock values. Predicting stock prices has been one of the biggest concerns in recent years. Trading stocks is a significant economic activity that contributes to society and enables individuals to increase their income. Making a prediction about the stock market involves attempting to anticipate the future value of a company's stock or another financial instrument traded on a stock exchange. Gains for investors will be maximized if a stock's future price can be accurately predicted. The stock market's issue is that it occasionally displays an erratic pattern that could result in a crash. Stock price predictions frequently involve machine learning techniques. We employ a variety of machine learning techniques, both supervised and unsupervised, to inform investors of stock price increases and decreases. Data acquisition, dataset preprocessing, feature extraction, feature-based stock price prediction, and result presentation were the five stages of the process. Data is initially gathered from various social media platforms and historical company information. Pre-processing is the second phase, where incorrect, duplicate, and dirt data are removed. In the third phase, data sets are reduced and meaningful data are chosen. In the fourth phase, predictions are made utilizing various machine learning methods, including supervised and unsupervised learning techniques. Now, various methods are used in the final phase to determine correctness.

Keywords : Tesla, Elon Musk, Stock Market, Linear Regression, Machine Learning