To conduct a thorough exploratory data analysis (EDA) and deep analysis of a comprehensive dataset containing basic customer details and extensive credit-related information. The aim is to create new, informative features, calculate a hypothetical credit score, and uncover meaningful patterns, anomalies, and insights within the data.
Project Summary: Credit Score Analysis and Feature Engineering
This project aims to analyze banking and credit data to develop a hypothetical credit score by leveraging exploratory data analysis (EDA), feature engineering, and scoring methodologies. The objective is to uncover patterns influencing creditworthiness and provide insights into potential risk mitigation strategies.
Key Objectives:
- Exploratory Data Analysis (EDA)
Perform a deep dive into the dataset to understand distributions, relationships, and anomalies.
Handle missing values, data inconsistencies, and outliers.
Use visualizations such as histograms, scatter plots, and correlation matrices to derive insights.
2.Feature Engineering
Create new features based on domain knowledge and insights from EDA.
Aggregate data at the customer level for better analysis.
3.Hypothetical Credit Score Calculation
Develop a methodology to compute a credit score using 5 to 10 relevant features.
Take inspiration from FICO scoring systems to assign weights to features.
Assign a credit score to each customer based on calculated attributes.
Experiment with different weighting techniques to refine the scoring model.
Analysis & Insights
Interpret key factors influencing the credit score. Assess if scores can be calculated based on different time frames (e.g., last 3 months or last 6 months). Identify trends that impact customer creditworthiness.
Outcome & Impact:
The project provides a data-driven approach to evaluating credit risk by constructing an effective credit scoring model. The insights derived can help financial institutions assess borrower reliability, improve lending decisions, and enhance risk management strategies.