German Credit Risk by Kaggle Data

Introducing my latest Python data analysis project, the "German Credit Python Data Analysis Project." This project delves into the Kaggle database, offering valuable insights obtained through essential data analysis tasks.

Before embarking on my professional career, I dedicated myself to the intricacies of this captivating project, focusing on key data analysis tasks:

- DataPreparation and DataCleaning: With meticulous attention to detail, I organized and refined the dataset, ensuring precision and reliability.

- DataExploratory Analysis and DataVisualization: Armed with powerful Python libraries, I delved deep into the data, uncovering fascinating patterns and trends. Through visually engaging plots and graphs, I brought the data to life, making complex information easily understandable.

I am thrilled to share the remarkable findings from this compelling analysis. Check out the complete project on GitHub: [German Credit Project](GitHub: German Credit Project)

Key Insights:

- German credits skew towards higher amounts for vacation purposes, with the majority allocated for purchasing cars.

- Highly skilled workers tend to request higher credit amounts, highlighting a correlation between professional expertise and credit needs.

- House owners boast the best credit scores, indicating a potential link between homeownership and financial responsibility.

- Young adults aged 25 to 30 exhibit a higher risk of bad credits, shedding light on a demographic with potential credit challenges.