Data Analysis using Python

In the credit card attrition analysis project, I utilized Python along with essential libraries like NumPy, Pandas, Seaborn, Matplotlib, and Plotly Express to conduct comprehensive data analysis. My tasks encompassed handling unknown values using statistical methods such as standard deviation and credit limit, ensuring data integrity. I explored correlations within numerical categories through correlation matrices, providing insights into dataset relationships. Meticulous data cleaning procedures were also implemented to rectify inconsistencies and errors, ensuring dataset reliability. These tasks formed the foundation of the data analysis process, enabling me to gain a comprehensive understanding of the credit card attrition dataset and extract meaningful insights to inform decision-making.

• visualization
• Problem statement
• View in GitHub