Using ARIMA (Auto-Regressive Integrated Moving Average) which is a model, this research study presents a method for anticipating stock market values. To begin, historical stock price data is gathered and pre-processed. Pre-processing is a subcategory of Data Mining in which raw original data is processed into a specified format so that the model can use it. Pre-processing is done to fill in the missing values and arrange the data based on the qualities required for developing the prediction model because the raw data to be processed is partly partial or inconsistent with certain errors and missing values. After the data has been filtered and classified, it is standardized by transforming it into a common format that can be used to train the prediction model. The obtained data is then divided into two data - training and testing data, with the majority of the data being used to train the model. And using the variables linked with the stock price, the testing data is fed to the ARIMA model to predict the corresponding stock price on a particular date. The results are used to determine the model's accuracy.