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Feature Engineering Learning

Template

Feature Engineering Learning:

1. <Name of the video>
<link>
<Notes - minimum 5, maximum 10>
<Energy - minimum 2, maximum 5>
2. <Name of the video>
<link>
<Notes - minimum 5, maximum 10>
<Energy - minimum 2, maximum 5>

Example

Feature Engineering Learning:

1. Feature Engineering Basics - corporatefinanceinstitute

https://corporatefinanceinstitute.com/resources/knowledge/data-analysis/feature-engineering/

1) feature - variable in dataset(can often be described as a column)

2. why feature engineer?
  - improves the performance of the model by selecting the right features and preparing the features in a way that is suitable for the model.
  - slim down and manipulate features to produce a set of effective predictor variables.
  - Involves questions like(example: car price predictor)
      Is number of seats a good predictor
      Should there be a predictor variable for the shoulder width of every seat?
      Should horsepower and torque be separate predictor variables, or do they provide similar information and only one of them is needed?
  - no single correct method of conducting feature engineering - highly dependent on the dataset and the target variables

3. Steps involved
    - Data Cleansing
    - Data Transformation
    - Feature Extraction
    - Feature Selection
    - Feature Iteration

4. Data Cleansing
    - process of dealing with errors or inconsistencies in the data
    - involves identifying incorrect data, missing data, duplicated data, and irrelevant data
    - process of deleting, replacing, or modifying data to remove outliers and incorrect values.

5. Data Transformation
    - process of transforming the data from one layout to another
    - Transformation needs to occur in a way that does not change the meaning of the original data

Energy:
1. I feel good after reading this article.
2. Topics where written in a clear way and was wasy to understand. Learnt a lot