COURSERA CAPSTONE PROJECT SWIFTKEY

You gonna be in DC anytime soon? Clean means alphabetical letters changed to lower case, remove whitespace and removing punctuation to name a few. Learned the hard way, but I ended up creating a much smaller sample of the raw data with less information to decrease processing time. Data Exploration Now that we have the data in R, we will explore our data sets. Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera.

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. Then dataset is cleansed to remove the following; non-word characters, lower-case, punctuations, whitespaces. Dataset for this project is sourced from this website. Flowing sentences are most accurate in this regard. Your heart will beat more rapidly and you’ll smile for no reason. Note that the document term matrix is a sample of all 3 documents, therefore the visualizations shown below include the 3 document datasets in scope.

Therefore, the analysis shown in this coirsera uses a sample of the whole datasets so that, it can be manageable by the hardware. It was very cute to watch his reaction when he realized he did!

Capstone Project SwiftKey

When the user enters a word or phrase the app will use the predictive algorithm to suggest the most likely sucessive word. Exploratory Analysis There are a few explorations performed. The web-based application can be found here. You gonna be czpstone DC anytime soon?

Disclaimer The datasets required by this Capstone Project are quite large, adding up to MB in size. Loading these data sets into R, requires quite a few resources.

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couresra Speed will be important as we move to the shiny application. 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.

Coursera Swiftkey Word Prediction Capstone Project

Therefore we will create a smaller sample for each file and aggregate all data into a new file. This preliminary report is aimed to create understanding of the data set. Datasets can be found https: 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.

Dataset for this project is sourced from this website. To improve accuracy, Jelinek-Mercer smoothing was used in the algorithm, combining trigram, bigram, and unigram probabilities.

RPubs – Coursera Capstone Project – Swiftkey

The project includes but is not limited too: Executive Summary Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera. The objective of this project was to build a working predictive text model. 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.

We made him count all of his money to make sure that he had enough! The data used in the model came from a corpus called HC Corpora www. Then dataset is cleansed siftkey remove the following; non-word characters, lower-case, punctuations, whitespaces. Finally, we can then visualize our aggregated sample data set using plots and wordcloud.

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coursera capstone project swiftkey

The resulting application will be published as a shiny app, that will be open for review of swiftksy interested. The dataset consists of 3 files all in english language. From our data processing we noticed the data sets are very big.

Now that the data is cleaned, we can captone our data to better understand what we are working with. Note that the document term matrix is a sample of all 3 documents, therefore the visualizations shown below include the 3 document datasets in scope.

Higher degree of N-grams will have lower frequency than that of lower degree N-grams. As part of projecg 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.

Milestone Conclusions Using the raw data sets for data exploration took a significant amount of processing time. The goal on this section, is to do prepare the corpus documents for subsequent analysis.

coursera capstone project swiftkey

Create Uni-grams Uni-gram frequency table is created for the corpus. After we load libraries our first step is to get the data set from the Coursera website.

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. He wanted that game so bad and used his gift card from his birthday he has been saving and the money to get it he never taps into that thing either, that is how we know he wanted it so bad.