COURSERA CAPSTONE PROJECT SWIFTKEY

COURSERA CAPSTONE PROJECT SWIFTKEY

White paper can be found http: To improve accuracy, Jelinek-Mercer smoothing was used in the algorithm, combining trigram, bigram, and unigram probabilities. Executive Summary Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera. We notice three different distinct text files all in English language. We must clean the data set. We made him count all of his money to make sure that he had enough! From our data processing we noticed the data sets are very big.

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. White paper can be found http: Less data has its cost, I assume it will decrease the accuracy of the prediction. It has provided some interesting facts about how the data looks like. When the user enters a word or phrase the app will use the predictive algorithm to suggest the most likely sucessive word. He also does a very good job of letting Lola feel like she is playing too, by letting her switch out the characters!

We must clean the data set. The web-based application swiftiey be found here. Coursera Data Science Capstone: Your heart will beat more rapidly and you’ll smile for no reason. Therefore, the analysis shown in this report uses a sample of the whole datasets so that, it can be manageable by the hardware.

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Coursera Capstone Project. Text Mining: Swiftkey. Word Prediction

The project includes but is not limited too: White paper can be found http: Dataset for this project is sourced from this website. Finally, we can then visualize our aggregated sample data set using plots and wordcloud. The source files for this application, the data creation, and this presentation can be found here. Datasets can be found https: Next step of this capstone project would be to tune and precision the predictive algorithm model, and deploy the same using Shiny app.

Capstone Project SwiftKey

A corpus is body of text, usually containing a large number of sentences. The datasets required by this Capstone Project are quite large, adding up to MB in size. Using the algorithm, a Shiny Natural Language Processing application was developed that accepts a phrase as input, suggests word completion from the unigrams, and predicts the most likely next word based on the linear interpolation of trigrams, bigrams, and unigrams.

Tokenization is performed by splitting each line into sentences. Data Processing After we load libraries our first step is to get the data set from the Coursera website. Then dataset is cleansed to remove the following; non-word characters, lower-case, punctuations, whitespaces. Term Frequencies Term frequencies are identified for the most common words in the dataset and a frequency table is created.

The data used in the model came from a corpus called HC Corpora www. Executive Summary Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera.

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Coursera Data Science Capstone: SwiftKey Project

Less data has its cost, I assume it will decrease the accuracy of the prediction. Milestone Conclusions Using the raw data sets for data exploration took a significant amount of processing time. This project will focus on the English language datasets. As the user types, the algorithm analyzes the words and comes up with a suggested words list.

Coursera Swiftkey Word Prediction Capstone Project

Create Uni-grams Uni-gram frequency table is created for the corpus. The resulting application will capstne published as a shiny app, that will be open for review of anyone interested.

coursera capstone project swiftkey

After we load libraries prouect first step is to get the data set from the Coursera website. She loves it almost as much as him. Been way, way too long. There are 3 files coming from blogs, news and twitter data.

coursera capstone project swiftkey

Stored N-gram frequencies of the corpus source is used to predicting the successive word in a sequence of words. Exploratory Analysis There are a few explorations performed.

Use of the application capsone straightforward and can be easily adapted to many educational and commercial uses. Loading these data sets into R, requires quite a few resources.