Self introduction, Q&A on Data Science applications. Statistical methods to detect and impute outliers in the dataset. how to use machine learning techniques such as naive bayes, decision trees, random forest, boosted trees, logistic regression, stepwise/regularized regression to construct prediction models and clustering. Cross validation when evaluating my models by comparing R2 or confusion matrices. Previous works. The importance of statistical quality control charts in manufacturing plants