CAPSTONE PROJECT SWIFTKEY

Therefore we will create a smaller sample for each file and aggregate all data into a new file. You can try out the Text Prediction App on the Shiny server. So before proceeding any further, we clean things up a bit. The goal of this capstone project is for the student to learn the basics of Natural Language Processing NLP and to show that the student can explore a new data type, quickly get up to speed on a new application, and implement a useful model in a reasonable period of time. Data Visualization Now that the data is cleaned, we can visualize our data to better understand what we are working with.

The final app offers a variety of benefits to its users: First we convert all of the text to lowercase and then remove punctuation, numbers and common English stopwords. The app is extremely intuitive. The objective of the capstone project was to 1 build a model that predicts the next term in a sequence of words, and to 2 encapsulate the result in an appropriate user interface using Shiny. You can try out the Text Prediction App on the Shiny server. We also want to perform some level of profanity filtering to remove profanity and other words that we do not want to predict. We must clean the data set.

It offers its users up to 3 next best terms.

The ultimate goal for this capstone project is to predict the next word based on a secuence of words typed as input. An excerpt of text cleaning and other transformations: To acheive this goal, we use a bad words dataset from CMU as a reference point for bad words removing.

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

capstone project swiftkey

You gonna be in DC anytime soon? Post A Comment Cancel Reply. It allows native German-speakers to use the app as well experimental. Removal of any Internet related content hyperlinks, emails, retweets. We must clean the data set.

SwiftKey Capstone Project – Milestone Report

Data Exploration Now that we have the data in R, we will explore our data sets. First we convert all of the text to lowercase and then remove punctuation, numbers and common English stopwords. Milestone Conclusions Using the raw data sets for data exploration took a significant amount of processing time.

Btw thanks for the RT. We are given datasets for training purposes, which can be downloaded from this link. 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. 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.

Your heart will beat more rapidly and you’ll smile for no reason. The final app offers a variety of benefits to its users: Cleaning the data is a critical step for ngram and tokenization process. Therefore we will create a smaller sample for each file and aggregate all data into a new file.

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Once a cleaned set of text source was available in form of n-gram tables, I began to implement and test a variety features. Today is a great … day.

SwiftKey Capstone Project – Milestone Report

After we load libraries our first step is to get the data set from the Coursera website. Conversion of text to lower case and removal of any unnecessary whitespaces. My final model performs as follows: I utilized the benchmark code by Swfitkey to test the performance of the next term prediction app. Data Preparation From our data processing we noticed the data sets are very big.

Capstone Project SwiftKey

Executive Summary Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera.

You can try out the Text Prediction App on the Shiny server. Love to see you. But typing on mobile devices becomes a serious pain for many swirtkey. Flagging end of sentences to avoid that the app makes predictions across sentence boundaries.

capstone project swiftkey

For the subsequent model building process, I drew a random sample of text and began the data preparation.