tags: [DS/DataAnalysis]
aliases: [EDA]Copy

It's used in exploring, observing, and understanding the dataset, using:

• Descriptive Statistics
• Summary statistics.
• Use data exploration tools.

Benefits:

• Quality Control
• Hypothesis Generation
• Understanding Data
• Data Visualization

Applications:

• Data Preparation
  • Find any Missing Values, wrong data-types values, duplicates.
  • Drop useless columns
  • Rename columns
  • Drop irrelevant data
  • Categorize your values
  • Discover and drop skewed or imbalanced data
  • Create new features
• Feature Extraction: Feature selection is about selecting attributes that have the greatest impact towards the problem you are solving.
  • Specify features
  • Discover important features (or attributes)
  • Identify relationships and correlations in features
• Relationship exploration
• Data summarization
• Pattern discovery
• Uni-variate analysis
• Multi-variate analysis
• Locate any outliers in your dataset

Resources

• EDA in python
  • Exploratory Data Analysis (EDA) - Complete Machine Learning Package
  • Google Colaboratory