[Feature]: Bivariate Analysis of Engineered Features with Parental Status Hue #11
Description
So, what is it about?
Overview
This issue proposes the implementation of a bivariate analysis of engineered features within our customer dataset, utilizing is_parent as a hue to distinguish between parental statuses. The goal is to combine insights from this analysis with previous univariate analyses to extract key findings that can drive informed business decisions.
Objectives
- To perform a feature correlation analysis to understand the relationships between different customer attributes.
- To conduct a bivariate analysis with a focus on the 'is_parent' feature to observe how parental status may affect other variables.
- To synthesize the results from this bivariate analysis with prior univariate analysis to compile a comprehensive report on customer behavior and trends.
Expected Outcomes
- A correlation matrix that highlights significant correlations between features.
- Visual representations (scatter plots, pair plots) that showcase the bivariate relationships with 'is_parent' as a hue.
- A summary of key insights that have been derived from the combination of univariate and bivariate analyses, potentially revealing patterns unique to parents or non-parents within the dataset.
Code of Conduct
- I agree to follow this project's Code of Conduct