A stock market model using statistical analysis is used to predict sales by an investment manager in such a way as to recognize the main aspects that affect the same business strategy planning. The basic understanding of the necessary data for evaluating a predictive model is achieved using statistical methods. This helps the management to define the marketing strategy while preparing a report that helps the analyst to analyze the results obtained by the marketing officer. This paper presents a theoretical and analytical framework implementation using a support vector machines for stock market forecasting. The model tries to predict whether a stock price will be higher or lower in the future or it is on a given day which helps to determine whether to invest or not. Technical and fundamental analysis or time series analysis used by most of the stockbrokers for forecasting the stocks. Machine learning algorithms implemented using Python programming language predicts the stock market. However, this paper proposes that a machine learning (ML) approach based on these aspects will use the available stock information and use the resulting information to make accurate predictions.