What is the process of converting unstructured raw data with variances into structured data?
a Data cleansing
b Data Shining
c Lemmetization
d Natural Language Processing (NLP
Answers
Answer:
I guess
Explanation:
option A is correct
hope it helps
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Answer:
You must first determine which data sources are necessary for the data analysis before you can start. Un - structured data sources can be obtained in a variety of formats, including chats, emails from customers, web pages, audio and video recordings, and text files. Only those unstructured sources of data that are entirely pertinent should be examined and used.
Explanation:
The seven steps for analyzing unstructured data to derive insights from structured data are listed below.
1. Understand what would be done with the analysis' results.
If the outcome is not clear, the research can be meaningless. For the final outcomes to be used more effectively for commercial, market, or other institution gains, a clear road chart should be developed.
2. Choose the technologies for data collection and storage based on company requirements.
Although the unsupervised learning will come from various sources, the analysis's results must be put into a technology stack in order for them to be immediately usable. The volume, adaptability, velocity, and diversity of demands are the sole determinants of the characteristics that are crucial for choosing the data retrieval and storing.
3. Keep the data in a data warehousing till the very end.
Real-time pricing is becoming very necessary for online retailers. This calls for keeping an eye on and following the actions of rivals in real time, as well as developing offers based on the quick output of analytics tools. Software for tracking competitor prices is one of these pricing innovations.
4. Create information for storage
Metadata or other materials that could benefit in the investigation, if not then then in the future, should indeed be stored and material should be carefully preserved in its native incarnation until it is genuinely judged important and essential for a particular diagnosis.
5. Know the text flow and identify trends
The best solution is to clean one of the copies while keeping the original data files if you need to enable data usage. Manner that avoids and symbols should always be removed when converting text.
6. Web scraping and text mining
Text analytics, sentiment analysis, and parts-of-speech labeling could be used to recover commonly used terms like "person," "location," and "company," as well as the values associated with them. In order to comprehend the patterns in data and the text diagram shows the flow, you can achieve this by creating a maximum - likelihood matrix.
7. deploy project evaluation and impact it
Whatever the case, the outcome is what matters most. The results must be presented in the right way, gaining insights into data structure from unstructured information and distributing them.
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