COURSERA CAPSTONE PROJECT SWIFTKEY

  • June 24, 2019

Clean means alphabetical letters changed to lower case, remove whitespace and removing punctuation to name a few. Been way, way too long. To improve accuracy, Jelinek-Mercer smoothing was used in the algorithm, combining trigram, bigram, and unigram probabilities. Conclusion This preliminary report is aimed to create understanding of the data set. This preliminary report is aimed to create understanding of the data set. Tokenize and Clean Dataset Tokenization is performed by splitting each line into sentences.

Your heart will beat more rapidly and you’ll smile for no reason. Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera. 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. Tokenize and Clean Dataset Tokenization is performed by splitting each line into sentences. Disclaimer The datasets required by this Capstone Project are quite large, adding up to MB in size. The objective of this project was to build a working predictive text model. Create Word Cloud Word Cloud is generated on the dataset.

The goal on this section, is to do prepare the corpus documents for subsequent analysis. Our second step is to load the date set into R. 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.

  CASE STUDY 5.1 VETEMENTS LTEE

Coursera Capstone Project. Text Mining: Swiftkey. Word Prediction

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

The accuracy of the prediction depends on the continuity of the text entered. Love to see you.

coursera capstone project swiftkey

Now that the data is cleaned, we can visualize our data to better understand what we are working with. Exploratory Analysis There are a few explorations performed. Data Processing After we load libraries our first step is to get the data set from the Coursera website.

Coursera Data Science Capstone: SwiftKey Project

Stored N-gram frequencies of the corpus source is used to predicting the successive word in a sequence of words.

It has provided some interesting facts about how the data looks voursera. Higher degree of N-grams will have lower frequency than that of lower degree N-grams. Coursera and SwiftKey have partnered to create this capstone project as the final project for the Data Scientist Specilization from Coursera. As depicted below, the user begins just by typing some text without punctuation in the supplied input box.

When the user enters a word or phrase the app will use the predictive algorithm to suggest the most likely sucessive prjoect.

coursera capstone project swiftkey

The project includes but is not limited too: Sampling the corpus and create the Document Term Matrix. By the usage of the tokenizer function for the coursra a distribution of the following top 10 words and word combinations can be inspected. Datasets can be found https: Less data has its cost, I assume it will decrease the accuracy of the prediction.

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Cleaning the data is a critical step for ngram and tokenization process. After we load libraries our first step is to get the data set from the Coursera website.

We notice three different distinct text files all in English language. From our data processing we noticed the data sets are very big.

coursera capstone project swiftkey

Conclusion This preliminary report is aimed to create understanding of the data set. Then dataset is cleansed to remove the following; non-word characters, lower-case, punctuations, whitespaces.

Create Tri-grams Tri-gram frequency table capstoje created for the corpus. Clean means alphabetical letters changed to lower case, remove whitespace and removing punctuation to name a few.

Capstone Project SwiftKey

A corpus is body of text, usually containing a large number of sentences. Next step of this capstone project would be to tune and precision the predictive algorithm model, and deploy the same using Shiny app.

Data Preparation From our data processing we noticed the data sets are very big. Create Uni-grams Uni-gram frequency table is created for the corpus. Tokenization is performed by splitting each line into sentences. The goal of this capstone project is for the courzera to learn the basics of Natural Language Processing Courseea 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.