COURSERA CAPSTONE PROJECT SWIFTKEY

Create Uni-grams Uni-gram frequency table is created for the corpus. Data Preparation From our data processing we noticed the data sets are very big. Cleaning the data is a critical step for ngram and tokenization process. Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera. Datasets can be found https:

The data used in the model came from a corpus called HC Corpora www. It is assumed that the below libraries are aready installed. The resulting application will be published as a shiny app, that will be open for review of anyone interested. White paper can be found http: Executive Summary Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera. Our second step is to load the date set into R.

She loves it almost as much as him. To improve accuracy, Jelinek-Mercer smoothing was used in the algorithm, combining trigram, bigram, and unigram probabilities.

Capstone Project SwiftKey

Create Word Cloud Word Cloud is generated on the dataset. There are 3 files coming from blogs, news and twitter data.

coursera capstone project swiftkey

The accuracy of the prediction depends on the continuity of the text entered. Therefore, the analysis shown in this report uses a sample of the whole datasets so that, it can be manageable by the hardware. Create Uni-grams Uni-gram frequency table is created for the corpus. Btw thanks for the RT.

RPubs – Coursera Capstone Project- Swiftkey

Finally, we can then visualize our aggregated sample data set using plots and wordcloud. You gonna be in DC anytime soon?

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A corpus is body of text, usually containing a large number of sentences. Milestone Conclusions Using the raw data sets for data exploration took a significant amount of processing time.

coursera capstone project swiftkey

Exploratory Analysis There are a few explorations performed. The web-based application can be found here. White paper can be found http: Coursera Data Science Capstone: The datasets required by this Capstone Project are quite large, adding up to MB in size.

Conclusion This preliminary report is aimed to create understanding of the data set. 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 Data Science Capstone: SwiftKey Project

Cleaning the data is a critical step for ngram and tokenization process. Then dataset is cleansed to remove the following; non-word characters, lower-case, punctuations, whitespaces.

Term frequencies are identified for the most common words in the dataset and a frequency table is created. 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.

The data used in the model came from a corpus called HC Corpora www. The resulting application will be published as a shiny app, that will be open for review of anyone interested. We made him count all of his money to make sure that he had enough!

Coursera Capstone Project. Text Mining: Swiftkey. Word Prediction

This preliminary report is aimed to create understanding of the data set. Stored N-gram frequencies of the corpus source is used to predicting the successive word in a sequence of words. Using the algorithm, a Shiny Natural Language Processing capshone 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. Data Visualization Now that the data is cleaned, we can visualize our data to better understand what we are working with.

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coursera capstone project swiftkey

Therefore we will create a smaller sample for each file and aggregate all data into a new file. The source files for this application, the data creation, and this presentation can be found here.

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.

Executive Summary Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera. Loading these data sets into R, requires quite a few resources.

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.