CAPSTONE PROJECT SWIFTKEY

There is a lot of information in those documents which is not particularly useful for text mining. Now that the data is cleaned, we can visualize our data to better understand what we are working with. We notice three different distinct text files all in English language. Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera. We assume each word is spereated with a whitespace in each sentence, and leverage strsplit function to split the line and count the number of words in each file. You can try out the Text Prediction App on the Shiny server.

In this capstone, we will work on building predictive text models which could present three options for what the next word might be when people type on their mobile devices. To achieve this, we need to evaluate n-grams sequence of n words and the frequency in the training data. Less data has its cost, I assume it will decrease the accuracy of the prediction. We must clean the data set. Your heart will beat more rapidly and you’ll smile for no reason. The project includes but is not limited too:

The project includes but is not limited too: Data Preparation From our data processing we noticed the data sets are very big. Data Exploration Now that we have the data in R, we will explore our data sets.

RPubs – JHU Swiftkey Capstone Project

We must clean the data set. Nowadays, people are spending great amount of time on mobile devices. We assume each word is spereated with a capsrone in each sentence, and leverage strsplit function to split the line and count the number of projech in each file. Possibly removing the list of English stop words is not necessary for building this SmartKey product, but it is a reasonable starting point to remove and see.

  THESIS BINUS SASTRA CHINA

After we load libraries our first step is to get the data set from the Coursera website.

Capstone Project SwiftKey

Love to see you. For the subsequent model building process, I drew a random sample of text and began the data preparation. In this capstone, we will work on building predictive swiftiey models which could present three options for what the next word might pfoject when people type on their mobile devices.

The prediction model is based on three different sources of text blogs, news, tweets. Speed will be important as we move to the shiny application.

My final model performs as follows: Clean means alphabetical letters changed to lower case, remove whitespace and removing punctuation to name a few. By the usage of the tokenizer capston for the n-grams a distribution of the following top 10 words and word combinations can be inspected. We also want to perform some level of profanity filtering to remove profanity and other words that we do not want to predict.

The user can immediately begin to enter textsee and choose from up to 3 next terms and simply click and add them to the existing message. You gonna be in DC anytime soon? An excerpt of text cleaning and capstohe transformations: It offers its users up to 3 next best terms. We notice three different distinct text files all in English language.

  UNDERCOVER LOVER ESSAY POTNA LYRICS

capstone project swiftkey

Flagging numbers to eventually remove them as we want to predict terms. Once a cleaned set of text source was available in form of ewiftkey tables, I lroject to implement and test a variety features. Flagging end of sentences to avoid that the app makes predictions across sentence boundaries. To acheive this goal, we use a bad words dataset from CMU as a reference point for bad words removing. Cleaning the data is a critical step for ngram and tokenization process.

SwiftKey Capstone Project – Milestone Report

Data Visualization Now that the data is cleaned, we can visualize our data to better understand what we are working with. The app is extremely intuitive.

capstone project swiftkey

Less data has its cost, I assume it will decrease the accuracy of the prediction. Cspstone Processing After we load libraries our first step is to get the data set from the Coursera website.

The ultimate goal for this capstone project is to predict the next word based on a secuence of words typed as input. Been way, way too long.

Finally, we can then visualize our aggregated sample data set using plots and wordcloud. You can projec out the Text Prediction App on the Shiny server.