The main purpose of this project is to showcase the knowledge and skills acquired in the Getting and Cleaning Data Course by Johns Hopkins University at Coursera, as a part of the Data Science Specialization.

The main contents of this repository, as required in the project instructions, are:

• The tidy dataset.
• The R script for performing the analyses.
• The codebook that describes the variables, the data, and the data transformations performed.

1. Clone this repository git clone https://github.com/jclopeztavera/human-activity.git.
2. Open the R-project file.
3. Source the run_analysis.R file .
4. Drop me a line if you find any areas for improvement.

• Merge the training and the test sets to create one data set.
• Extract only the measurements on the mean and standard deviation for each measurement.
• Use descriptive activity names to name the activities in the data set
• Appropriately label the data set with descriptive variable names.
• From the data set in the previous step , create a second, independent tidy data set with the average of each variable for each activity and each subject.
• Make code book.
• Submit assignment.

• The submitted data set is tidy.
• The Github repo contains the required scripts.
• GitHub contains a code book that modifies and updates the available code books with the data to indicate all the variables and summaries calculated, along with units, and any other relevant information.
• The README that explains the analysis files is clear and understandable.
• The work submitted for this project is the work of the student who submitted it.

• Read the paper behind the data set.
• Properly explore the data set, look at all variables and understand them.
• Describe the data set in detail.
• Hands on ML: Train, test, and compare classification algorithms (besides the paper, you can read this IPython Notebook by Mark Regan)

• R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch".
• Platform: x86_64-apple-darwin13.4.0 (64-bit).
• RStudio Desktop 1.0.136 - makes R easier to use.

• Jeff Leek, Roger D. Peng, and Brian Caffo from the Bloomberg School of Public Health.
• R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL.
• Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013. URL.