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Data Preprocessing | Identifying Fake News
Identifying Fake News
course content

Course Content

Identifying Fake News

bookData Preprocessing

As a mandatory step in our analysis, we must preprocess our data. Data preprocessing is the process of cleaning, transforming, and organizing the data to make it more suitable for analysis and modeling. This typically involves several steps, such as the following:

  • removing missing or duplicate values;
  • correcting inconsistencies;
  • transforming the data into a format that is easier to manage.

Task

  1. Remove unnecessary columns (for our further analysis): 'title', 'subject', and 'date'.
  2. Use the appropriate method to remove duplicates.
  3. Use the appropriate methods to shuffle the DataFrame and reset its index.
  4. Use the appropriate method to check for missing values (NaN values).

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As a mandatory step in our analysis, we must preprocess our data. Data preprocessing is the process of cleaning, transforming, and organizing the data to make it more suitable for analysis and modeling. This typically involves several steps, such as the following:

  • removing missing or duplicate values;
  • correcting inconsistencies;
  • transforming the data into a format that is easier to manage.

Task

  1. Remove unnecessary columns (for our further analysis): 'title', 'subject', and 'date'.
  2. Use the appropriate method to remove duplicates.
  3. Use the appropriate methods to shuffle the DataFrame and reset its index.
  4. Use the appropriate method to check for missing values (NaN values).

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 3
AVAILABLE TO ULTIMATE ONLY
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