I have completed this specialization nearly a year ago but I never wrote about it in detail. Unigram Analysis The first analysis we will perform is a unigram analysis. This milestone report is based on exploratory data analysis of the SwifKey data provided in the context of the Coursera Data Science Capstone. Description of the theoretical model As I mentioned before, the Katz backoff formulas in many web pages about Natural LAnguage Processing are wrong. This concludes the exploratory analysis. Rmd, which can be found in my GitHub repository https:
Below you can find a summary of the three input files. Sample Summary A summary for the sample can be seen on the table below. An analysis in PDF format is produced. The particular discounting strategy is not as important as the fact that some probability is left remaining for the unseen n-grams. Highlights — Built a prediction model with the Random Forest classifier using the caret R package — Applied Prediction Study design principles like creation of training, validation and test sets, as well as model selection and cross validation — Created a HTML report with R Markdown and knitr R package. The app can be found at:
We will use the Ngram dataframes created to calculate the probability of the next word occuring. This is a collection of notes from my learning journey that is attempt to be a cross reference between language implementations for common data science related tasks.
Introduction This milestone report is daat on exploratory data analysis of the SwifKey data provided in the context of the Coursera Data Science Capstone. R news and tutorials contributed by R bloggers.
At first, it was hard as I had to read a lot and write a lot code that is not needed in programs such as SPSS or Stata. You may want to start by taking a look at the app. All probabilities for n-grams are computed with a discount smoothing strategy.
Next, we will do the same for Bigrams, i. Never miss an update! Now that we have our corpus item, we need to clean capstons.
In this project, I worked on the Storm Events Database to produce an analysis of the impact of weather events in the United States. In this project, I analyzed the provided dataset and created a regression model to answer questions on motor car trends. You can read more about this specialization here. Description of the theoretical model As I mentioned before, the Katz backoff formulas in many web pages about Natural LAnguage Processing are wrong.
This concludes the exploratory analysis.
If you are an R blogger yourself you are invited to add your own R content feed to this site Non-English R bloggers should add themselves- here. You can see the capstne file, tidy dataset and codebook on Github. Here you can find R material that includes quizzes, assignments, exercises and my own tricks and functions that I created for courses contained in the specialization.
Drew Conway Personal Projects Data Science Cross Reference Notes [On-going] This is a collection of notes from my learning journey that is attempt to be a cross reference between language implementations for common data science related tasks. Another assumption is that the command wc is available in the target courseera.
In order to do that, we will transform capatone characters to lowercase, we will remove the punctuation, remove the numbers and the common english stopwords and, the, or etc. This theoretical part is specially important because I have found in the Web quite a few wrong descriptions of this type of models.
This article was first published on Reimagined Inventionand kindly contributed to R-bloggers. No other quizzes or assignments than those related to configure and use Github. Highlights — Visualized data using base R graphics and ggplot2 package Github Getting and Cleaning Data In this project, I cleaned a raw data source and produced a tidy dataset.
This will create a unigram Dataframe, which we will gihhub manipulate so we can chart the frequencies using ggplot. Of jellies, fishes and some.
The app can be found at: Full list of contributing R-bloggers. These are the corrected formulas I have used for my model: Rda” ggplot head bigram. For this project, I worked on the Human Activity Recognition dataset where data are recorded by sensors in wearable activity trackers similar to the products created by Nike and Fitbit. Here you will find daily news and tutorials about Rcontributed by over bloggers. The numbers have been calculated by using the wc projecf. If you are running windows, you can download the GnuWin32 utility set from http: Blog Post Github Tableau Visualization.
The main repostitory with the code of the project is:.