An in-depth analysis of factors affecting loan default using data from Lending Club.
- This project aims to analyze and identify key factors that contribute to loan default using Lending Club's dataset.
- The background of the project involves understanding the risk factors in lending and improving loan approval processes by predicting potential defaults.
- Business Problem: The project addresses the problem of predicting loan defaults to aid in better risk assessment and decision-making for lending institutions.
- Dataset: The dataset used in this project is obtained from Lending Club, containing various features such as income, loan amount, credit history, and employment length, among others.
- Conclusion 1: Higher income levels are generally associated with a lower likelihood of default.
- Conclusion 2: Larger loan amounts have a higher tendency for default.
- Conclusion 3: Good credit history significantly reduces the risk of loan default.
- Conclusion 4: Longer employment length correlates with lower default rates, indicating job stability as a crucial factor.
- Pandas - version 1.2.4
- NumPy - version 1.20.1
- Matplotlib - version 3.3.4
- Seaborn - version 0.11.1
- Scikit-learn - version 0.24.1
- Jupyter Notebook - version 6.3.0
- This project was inspired by the need for better risk assessment in financial lending.
- Data sourced from Lending Club's publicly available datasets.
- This project was based on various tutorials and resources available online, especially those focusing on Exploratory Data Analysis (EDA) and predictive modeling.
Created by [@mrchandrayee] Chand Rayee - feel free to contact me!
Created by [@chanchalakusum] Kusum Chanchala - feel free to contact me!