(EDA Part-5) Multivariate Analysis — Wrapping Up EDA and What Comes Next

1. 1Perform multivariate analysis using pandas and matplotlib to identify relationships between multiple features.
2. 2Visualize the results of multivariate analysis to gain insights into the data.
3. 3Use the insights gained from EDA to inform the next steps in the data science workflow, such as feature engineering and model selection.
4. 4Apply dimensionality reduction techniques, such as PCA or t-SNE, to simplify complex datasets.
5. 5Use clustering algorithms, such as k-means or hierarchical clustering, to identify patterns in the data.

Data scientists and machine learning engineers can benefit from this article to improve their EDA skills and understand the next steps in the data science workflow.

💡 Multivariate analysis is a crucial step in EDA that helps identify relationships between multiple features and informs the next steps in the data science workflow.

📊 Perform multivariate analysis to uncover relationships between features and inform your next steps in machine learning! #MachineLearning #EDA