As the user types, the algorithm analyzes the words and comes up with a suggested words list. From our data processing we noticed the data sets are very big. 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. Less data has its cost, I assume it will decrease the accuracy of the prediction. Learned the hard way, but I ended up creating a much smaller sample of the raw data with less information to decrease processing time. The datasets required by this Capstone Project are quite large, adding up to MB in size.
Data Preparation From our data processing we noticed the data sets are very big. A profanity filter was also utilized on all output using Google’s bad words list. Less data has its cost, I assume it will decrease the accuracy of the prediction. 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 algorithm developed to predict the next word in a user-entered text string was based on a classic N-gram model.
Coursera Data Science Capstone: A profanity filter was also utilized on all output using Google’s bad words list. Cleaning the data is a critical step for ngram and tokenization process. The accuracy of the prediction depends on the continuity of the text entered.
We notice three different distinct text files all in English language. Term Frequencies Term frequencies are identified for the most common words in the dataset and a frequency table is created.
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 capstonw that thing either, that capstne how we know he wanted it so bad. 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.
Coursera Capstone Project. Text Mining: Swiftkey. Word Prediction
Tokenize and Clean Dataset Tokenization is performed by splitting each line into sentences. The dataset consists of 3 files all in english language. Use of the application is straightforward and can be easily adapted to many educational and commercial uses.
Your heart will beat more rapidly and you’ll smile for no reason. Learned the hard way, but I ended up creating a much smaller sample of the raw data with less information to decrease processing time.
We made courxera count all of his money to make sure that he had enough! The objective of this project was to build a working predictive text model.
The resulting application will be published as a shiny app, that will be open for review of anyone interested.
Create Tri-grams Tri-gram frequency table is created for the corpus. Now that the data is cleaned, we can visualize our data to better understand what we are working with.
Coursera Swiftkey Word Prediction Capstone Project
The data used in the model came from a corpus called HC Corpora www. Finally, we can then visualize our aggregated sample data set using plots and wordcloud. It has provided some interesting facts about how the data looks like. Therefore we will create a smaller sample for each file and aggregate all data into a new file. Stored N-gram frequencies of the corpus source is used to predicting the successive word in a sequence of words. This preliminary report is aimed to create understanding of swfitkey data set.
Speed will be important as we move to the shiny application. Tokenization coursefa performed by splitting each line into sentences. He also does a very good job of letting Lola feel like she is playing too, by letting her switch out the characters! 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 switkey unigrams, and swiftkeh the most likely next word based on the linear interpolation of trigrams, bigrams, and unigrams.
Milestone Conclusions Using the raw data sets for data exploration took a significant amount of processing time. Create Word Cloud Word Cloud is generated on the dataset.
Love to see you. She loves it almost as much as him. Higher degree of Lroject will have lower frequency than that of lower degree N-grams. Clean means alphabetical letters changed to lower case, remove whitespace and removing punctuation to name a few. Been way, way too long.