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Khalil ElKhiari

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Designing a Data Model for 'Catch the Pink Flamingo' Bif data Specialization, Coursera
Data_model
Text prediction
This is a presentation pitching my app for text prediction. this project is a part of my data science specialization by John Hopkins university and Coursera
Compare to a top celebrity
Shiny app calculator to figure out how do we get paid compared to some of the top celebrities.
Exploratory analysis - Capstone project
This is the first milestone report of the capstone project, related to the Data science specialization offered by John Hopkins University (Via Coursera.org). In this capstone project, we are working on understanding and building predictive text models like those used by SwiftKey. When someone types a word, the keyboard presents several options for what the next word might be. Throughout this milestone report (week2 of the capstone project), I'll try to demonstrate that I successfully loaded the data into my R workspace, and will present, step by step, the techniques used to clean the data and to build the corpus (based on the 3 documents provided : blogs, news and twitter).
DDP-W3 Assignment
Data science Specialization - Coursera.org
POI in Marrakech
The 22nd session of the COP22, the world's biggest climate change conference is taking place from 7-18 November 2016 in Marrakech - Morocco. This interactive map is intended to help visitors discovering the most beautiful touristic sites of this warm and amazing city.
Barbell lifting quality
How well are your workouts ? Practical Machine Learning Course Project Coursera.org
Analysis of the severe weather events impact in USA (1995-2011)
This project involves exploring the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage. We’ve narrowed our data down to fewer variables. After performing the necessary transformations, we aggregate the reduced data set by event type, in order to figure out the top 10 of events which are most harmful to population health, and those with the greatest economic consequences.