For the Shiny app, the plan is to create an app with a simple interface where the user can enter a string of text. Data description and summary statistics In this project, the following data is provided. Trigram Analysis Finally, we will follow exactly the same process for trigrams, i. Rda” ggplot head bigram. The cleaning procedure performed the following actions: Before attempting to load any files, we’ll examine them using the bash shell. Exploratory Analysis Top 10 used words.
Then, we can clean data by removing numbers, white spaces, special characters, and profanity words which has been downloaded from the following link: The model will be trained using a collection of English text corpus that is compiled from 3 sources – news, blogs, and tweets. Each of these N-grams is transformed into a two column dataframe containing the following columns:. This makes intuitive sense. Each of these N-grams is transformed into a two column dataframe containing the following columns: I made a wordcloud. Trigram Analysis Finally, we will follow exactly the same process for trigrams, i.
The model will be trained using a collection of English text corpus that is compiled from 3 sources – news, blogs, and tweets. The number of lines and total number of words are as follows: Before moving to the next step, we will save the corpus in a text file so we have it intact for future reference. While the strategy for modeling and prediction has not been finalized, the n-gram model with a frequency look-up reporrt might be used based on the analysis above.
Another assumption is that the command wc is available in the target system. The Coursera Data Science Capstone involves predictive text analytics.
In this project, we are interested in the three forms of data in English. Capstonee will require the following helper functions in order to prepare our corpus.
This report is an exploratory analysis of the training data supplied for the capstone project. Rda” ggplot head unigram. As a next step a model will be created and integrated into a Shiny app for word prediction. Set the correct working directory setwd “C: This milestone report is based on exploratory data analysis of the SwifKey data provided in the context of the Coursera Data Science Capstone.
Clean up the corpus by removing special characters, punctuation, numbers etc. We will pass the argumemnt 1 to get the unigrams. Blogs are the highest at The purpose of this Milestone Report is to demonstrate progress towards the end goal of this project.
Some of the code is hidden to preserve space, but can be accessed by looking at the Raw. In this case, we created four different N-grams as follows:.
The cleaning procedure performed the following actions: As an alternative of the last plots, and to give a quick impression of the most common words, this graph shows the most common words of the corpus.
Now that we have our corpus item, we need to clean it. Rda” ggplot head trigram. The model will then be integrated into a shiny application that will provide a simple and intuitive front end for the ned user.
Here we list the most common unigrams, bigrams, and trigrams. Each of these Capsyone is transformed into a two column dataframe containing the following columns:. Removal of profanity will be a consideration for the predicted text.
Next Steps This concludes the exploratory analysis. Sample Summary A summary for the sample can be seen on the table below. This concludes the exploratory analysis. Our target files are: Next, this data was combined into a single file couraera further clearning and analysis. We follow exactly the same process, but this time we will pass the argument 2.
Assumptions It is assumed that the data has been downloaded, unzipped and coursega into the active R directory, maintaining the folder structure. The full dataset will be used later in creating the prediction algorithm.
Exploratory analysis For each Term Document Matrix, we list the most common unigrams, bigrams, trigrams and fourgrams.