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Stock costs in the securities exchange vacillate at each passing second; making it hard to anticipate the real stock value it will close at, at its end time. This leaves the financial backer in question about his benefit/misfortune edge against his speculation for that specific day. As APPLE Inc. is perhaps the most significant tech monster of the 21st century, many significant financial backers purchase APPLE (AAPL) stocks in the desire to make a fortune. Most existing arrangements utilize stock's end cost to decide its present market worth and utilize both Linear and Non-Linear Machine Learning models which appear to be incorrect. We adopted another strategy to decide APPLE Inc. stock cost by utilizing the OPENING worth of stock and foresee the stock's CLOSING value, which the financial backer acquires by the day's end and utilized Linear Regression model as it has ended up being quite possibly the most exact answers for stock forecast issues. Dataset was preprocessed, then, at that point prepared the model and, in the end, correlations show the genuine outcomes to approve the precision of my methodology and it end up being more than 95%. Results were practically equivalent to the current APPLE Inc. stock value which demonstrates the precision of my model.