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Cleansing Data
Cleansing Data in Big Data Analytics
- The process next to the collection of data from various sources is to form the sources of data homogenous and persist to design own data product
- the loss of data may persist as data are arriving from various sources, in this scenario, consider an alternative
- Building a regression model or classification model depending upon data sources and working with different sources is an advantage or loss of information , all these decision making process makes analytics appealing and challenging
- For reviewing, there are 2 options for considering language for every data source
- Homogenization supports converting various language to the language supporting more data
- It costs when converting lots of data with an Application Programming Interface
- Heterogenization supports to design a solution foe every language
- Do build up a suggesting option for every language
- It tuning is easier for a few languages
Sample Mini Project on Twitter
- In the current box, it is proposed to clean the unformatted data and then change into a matrix of data to apply modeling areas.
- While receiving data from twitter, a lot of characters are not required for use
- So, in the process of cleansing data the strange characters are to be illuminated
- The specified emotions in a tweet , In this case, to clean such things,
- The beneath R programming language script will support to remove those unstructured data
- The last step of cleansing data related to the mini project is to get cleaned data
- To achieve this, converting to a matrix and supplement an algorithm
- From the clean tweets vector the stored data is simply altered to a collection of matrix terms and implement an algorithm of unsupervised learning
e.g.
![Cleansing Data cleansing data img1](https://www.assignmenthelp.net/webimg/ahp/big-data-assignment-help/cleansing-data-img1.jpg)
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