A detailed 3-part tutorials that walks you through every single step of a Data Science/Machine Learning project: from data collection, data wrangling, plots, transformations to model fitting and parameter tuning. The final part of the series walks you through various machine learning models, cross-validation method to evaluate performance and Grid Search as a way to tune parameters. Machine Learning models under consideration are linear regression, decision tree and support vector machines. Read more...