Supervised Machine Learning is a field and study of data where some part of it is known to us before we use it for predictive applications. Most common example is a labeled data where the previous values of a target or a dependent variable is known. This program focus on building a predictive & recommendation based engine which returns a recommended output given certain inputs. For this to work, we collect labeled training data, split the data, derive equations and measure their accuracy. Powerful metrics are used like accuracy_score, _coef,intercept and confusion matrix to check the overall fitment. This program will give a jump start to machine learning to create recommendation based systems in software engineering and data science.