COURSERA CAPSTONE PROJECT SWIFTKEY

Btw thanks for the RT. Clean means alphabetical letters changed to lower case, remove whitespace and removing punctuation to name a few. Term Frequencies Term frequencies are identified for the most common words in the dataset and a frequency table is created. As depicted below, the user begins just by typing some text without punctuation in the supplied input box. The source files for this application, the data creation, and this presentation can be found here. The accuracy of the prediction depends on the continuity of the text entered.

Remove Profanity words Profanity words are removed from the corpus data. Been way, way too long. This preliminary report is aimed to create understanding of the data set. To improve accuracy, Jelinek-Mercer smoothing was used in the algorithm, combining trigram, bigram, and unigram probabilities. The accuracy of the prediction depends on the continuity of the text entered.

Data Preparation From our data processing we noticed the data sets are very big.

coursera capstone project swiftkey

The algorithm developed to predict the next word in a user-entered text string was based on a classic N-gram model. The objective of this project was to build a working predictive text model.

Create Uni-grams Uni-gram frequency table is created for the corpus. The web-based application can be found here. Remove Profanity words Profanity words are removed from the corpus data. From our data processing we noticed the data sets are very big. As the user types, the algorithm analyzes the words and comes up with a suggested words list.

As depicted below, the user begins just by typing some text without punctuation in corusera supplied input box.

  GOOD GOVERNANCE ESSAY JWT

coursera capstone project swiftkey

The goal on this section, is to do prepare the corpus documents swiftkeu subsequent analysis. Higher degree of N-grams will have lower frequency than that of lower degree N-grams.

Term frequencies are identified for the most common words in the dataset and a frequency table is created. Disclaimer The datasets required by this Capstone Project are quite large, adding up to MB in size. Your heart will beat more rapidly and you’ll smile for no reason.

Capstone Project SwiftKey

Use of the application is straightforward and can be easily adapted to many educational and commercial uses. Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera. Data Visualization Now that the data is cleaned, we can visualize our data to better understand what we are working with. Conclusion This preliminary report is aimed to create understanding of the data set.

Now that the data is cleaned, we can visualize our data to better understand what we are working with. As part of the prediction model, the generated stems will be used to gererate and algorithm to match input phrases, in order to predict the word that will be displayed next.

It is assumed that the below libraries are aready installed.

Coursera Data Science Capstone: SwiftKey Project

Then dataset is cleansed to remove the following; non-word characters, lower-case, punctuations, whitespaces. When the user enters a word or phrase the app will use the predictive algorithm to suggest the most likely sucessive word. Tokenization is performed by splitting each line into sentences.

  OAA TRAINEE ESSAY

After we load libraries our first siftkey is to get the data set from the Coursera website. We made him count all of his money to make sure that he had enough! You gonna be in DC anytime soon? Speed will be important as we move to the shiny application. The source files for this application, the data creation, and this presentation can be found here.

coursera capstone project swiftkey

Milestone Conclusions Using the raw data sets for data exploration took projecf significant amount of processing time. Executive Summary Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera.

The datasets required by this Capstone Project are quite large, adding up to MB in size. Therefore we will create a smaller sample for each file and aggregate all data into a new file.

By the usage of the tokenizer function for the n-grams a distribution of the following top 10 words and word combinations can be inspected.

She loves it almost as much as him. Cleaning the data is a critical step for ngram and tokenization process. A corpus is body of text, usually containing a large number of sentences.