This project aims to perform a comprehensive analysis of loan data to identify patterns and risk factors related to loan approvals, defaults, and borrower profiles. Through data exploration and visualization in Jupyter Notebook, it seeks to help stakeholders understand key factors that affect loan outcomes and provide insights for better risk management.
- Data Cleaning and Preprocessing: Handles missing data, outliers, and prepares the dataset for analysis.
- Exploratory Data Analysis (EDA): Visualizations and summary statistics to explore key patterns and relationships.
- Risk Analysis: Identifies factors that increase the risk of loan default.
- Insights for Decision-Making: Provides data-driven insights to inform lending practices.
- Analyze loan approval rates based on borrower profiles.
- Study demographic and financial factors that correlate with loan defaults.
- Determine common characteristics of approved vs. declined loans.
- Explore credit scores, loan amounts, purposes, and default rates.
The dataset includes various fields related to loan applications and borrower details, such as:
- Demographic Information: Age, gender, location, etc.
- Financial Information: Income, credit score, loan amount, interest rate.
- Loan Characteristics: Loan purpose, term, approval status, and default status.
Note: The dataset is either publicly sourced or simulated data, ensuring no sensitive information is exposed.
- Python 3.x
- Jupyter Notebook
- Clone the Repository:
git clone <repository-url> cd <repository-folder>