The stock market is an emerging sector in any country in the world. Many people are directly related to this sector. Stock market prediction is the act of trying to determine the future value of company stock or another financial instrument. When publicly traded, companies issue shares of stock to investors, every one of those shares is assigned monetary value or price. Stock prices can go up or down depending on different factors. Stock prices can be affected by several things including volatility in the market, current economic conditions, and the popularity of the company. The successful prediction of a stock's future price could yield a significant profit. Along with the development of the stock market, forecasting has become an important topic. Since the finance market has become more and more competitive, stock price prediction has been a hot research topic in the past few decades. Predicting stock price is regarded as a challenging task because the stock market is essentially nonlinear, on-parametric, noisy, and a chaotic system. The trend of a market depends on many things like liquid money human behavior, news related to the stock market, etc. All this together controls the behavior of trends in a stock market with the advancement of the computing technology we use machine learning techniques, like Support Vector Regression, K-nearest neighbor, liner Regression, Random Forest Regression, for analyzing time-series data to predict stock price. In this paper, we try to develop a forecasting model by stacking multiple methods to find the best forecast of the stock price.