COURSERA CAPSTONE PROJECT MILESTONE REPORT

Executive summary This report is a milestone report of the capstone project introduced by Johns Hopkins University through Coursera. It is assumed that the data has been downloaded, unzipped and placed into the active R directory, maintaining the folder structure. Calculate Frequencies of N-Grams Step 7: This milestone report for the Data Science Capstone project provides a summary of data preprocessing and exploratory data analysis of the data sets provided. This makes intuitive sense. Another assumption is that the command wc is available in the target system.

Clean up the corpus by removing special characters, punctuation, numbers etc. I calculate the size of each file in MB,number of lines and words in each file,average word count per line in each file, max count of char per line in each file and others details. The main goal of the capstone project is the application based on a predictive text model using explain the Explortory Data Analysis and building an algorithm. The numbers have been calculated by using the wc command. Another assumption is that the command wc is available in the target system. The goal of the Data Science Capstone Project is to use the skills acquired in the specialization in creating an application based on a predictive model for text. Calculate Frequencies of N-Grams Step 7:

In order to do that, we will transform all characters to lowercase, we will remove the punctuation, remove the numbers and the common english stopwords and, the, reporrt etc. Next, this data was combined into a single file for further clearning and analysis.

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This milestone report for the Data Science Capstone project provides a summary of data preprocessing and exploratory data analysis of the data sets provided. Clean up the corpus by removing special characters, punctuation, numbers etc. Word Count Line Count Longest Line blogs news twitter Introduction This milestone report is based on exploratory data analysis of the Capstonf data provided in the context of the Coursera Data Science Capstone.

Each of mklestone N-grams is transformed into a two column dataframe containing the following columns:. Our prediction model will then give a list of suggested words to update the next word. Rda” ggplot head bigram. Future Work My next steps will be: Text documents are provided in Milesgone, German, Finnish and Russian and they come in three different forms: I will be made into the corpus to: In this project, we are interested in the three forms of data in English.

A possible method of prediction is to use the 4-gram model to find the most likely next word first. Next Steps This concludes the exploratory analysis.

coursera capstone project milestone report

Before attempting to load any files, we’ll examine them using the bash shell. Plans for creating the prediction algorithm and the Shiny app will also be discussed.

This makes intuitive sense. Construct a milestnoe from the files. This concludes the exploratory analysis.

RPubs – Coursera Capstone Project Milestone Report

The main goal of the capstone project is the application based on a predictive text model using explain the Explortory Data Analysis and building an algorithm. Some of the code is hidden to coyrsera space, but can be accessed by looking at the Raw.

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Assumptions It is assumed that the data has been downloaded, unzipped and placed into the active R directory, maintaining the folder structure. Load packages and data Step 1: The words per line statistic is interesting.

coursera capstone project milestone report

For this project, the english language files will be used. I will be made into the corpus to:. Furthermore, stemming might also be done in data preprocessing. Blogs are the highest at In this section, I will find the most frequently occurring words in the data.

Build basic n-gram model. Data description and summary statistics In this project, the following data is provided.

Coursera Data Science Capstone: Week 2 Milestone Report

It is assumed that the data has been downloaded, unzipped and placed into the active Capstkne directory, maintaining the folder structure. This will show us which words are the most frequent and what their frequency is. Then we will download the text files used in this project, those files can be downloaded from the following link: Each of these N-grams is transformed into a two column dataframe containing the following columns: Briefly, the plan is to add in the filters, which will be a file full of foul words, then compared the data.