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README.md
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README.md
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# How well do you perform your work-out? - ML Case Study
In this project we can see in action and in detail a big part of the ML pipeline (data wrangling,model building, model evaluation) that comprises
different algorithms and approaches such as Decision Trees (RPART), Linear Discriminant Analysis (LDA), Gradient Boosting Machne (GBM),
Random Forest (RF) Support Vector Machine (SVM) with or without Model Stacking, with or without Dimensionality Reduction
(with Principal Component Analysis (PCA) or Near-Zero Variance Predictors Filtering)
This is the final report of the Peer Assessment project from Coursera’s course [Practical Machine Learning](https://www.coursera.org/learn/practical-machne-learning/),
as part of [Johns Hopkins University Data Science Specialization](https://www.coursera.org/specializations/jhu-data-science) carried out in October 2019.
Using data from wearable devices(accelerometers on the belt, forearm, arm, and dumbell) the project's goal is to quantify
(not how much of a particular physical activity a group of people does but **how well** they do it (5 classes).
The machine learning algorithm selected and built through the process described in this report is later applied to the 20 cases available in the test data
and the predictions are submitted for grading.