ABSTRACT: The previous dataset can be utilized to obtain the current prediction of price. The function of predicting stock prices involves determining the future price of a company's stock or other traded commercial instrument. The stock market and exchanges offer significant financial gains, which attract investors. Although the prediction of stock prices is a challenging issue, it can be resolved by implementing various techniques. We propose to gather input data for this project, remove unwanted null values, extract relevant data for feature engineering, and then apply Decision tree and Linear regression models for training purposes. The stock market is characterized as dynamic, unpredictable, and non-linear in nature, making it difficult to predict stock prices. Several factors, including but not limited to political conditions, global economy, company financial reports, and performance, contribute to the challenge of predicting stock prices.
Keywords: Prediction, Machine Learning, Data Set, Stock Price prediction, Decision tree algorithm, and linear regression.