“Stop Rushing to Build Models — Here’s Why EDA Should Be Your First Love in Data Science”

1. 1Conduct EDA on your dataset to understand distributions and relationships
2. 2Visualize your data using plots and charts to identify patterns and outliers
3. 3Apply statistical methods to summarize and describe your data
4. 4Use EDA to inform feature engineering and selection for modeling
5. 5Iterate on your EDA process based on initial findings and model performance

Data scientists and analysts can benefit from EDA to improve their data understanding and modeling outcomes, while team leaders can encourage EDA as a best practice

💡 EDA is a crucial step in the data science process that can improve model performance and reduce unnecessary complexity

💡 Prioritize EDA over model building for better data insights #datascience #eda