BRANDING - RAVEN STUDIO
PREDICTIVE LOAN RISK ASSESSMENT WITH MACHINE LEARNING
YEAR
2023
MAIN GOAL
PREDICTIVE LOAN RISK ASSESSMENT
SERVICES
DATA ANALYSIS - VISUALIZATION & MACHINE LARNING
📂 PYTHON FULL SOURCE CODE
DESCRIPTION
When a bank lends money to an individual, it takes on the risk that the person might not repay the loan within the agreed timeframe. This risk is known as Credit Risk. Before granting a loan, banks assess whether the applicant is likely to repay the loan or not.
This assessment involves analyzing several factors, such as income, assets, current expenses, and more. In many banks, this analysis is still done manually, which is time-consuming and resource-intensive. With machine learning, this process can be automated, providing more accurate predictions about which customers are at risk of defaulting on their loans.
🎯GOALS
- Clean and structure raw data to facilitate its utilization.
- Apply machine learning algorithms to extract accurate and relevant insights from the analyzed data.
- Visualize the results in the form of charts and actionable reports to assist decision-making.
- Develop a machine learning model for prediction.
Skills demonstrated :
- Data Analysis
- PythonÂ
- Data Science
- Credit Scoring
ABOUT
This project involves several important stages for predicting creditworthiness using machine learning techniques. Here’s a summary of each step:
- Preliminary Data Cleaning: Preprocessing the dataset to handle missing values, outliers, and ensure data consistency.
- Descriptive Analysis of Key Variables: Analyzing the main variables to understand patterns and relationships in the data.
- Model Selection and Phenomenon Modeling: Choosing appropriate machine learning models and building them to predict credit risk.
- Recommendations: Providing insights and actionable recommendations based on the model’s outcomes.
Overall, the project uses a structured approach to automate and enhance credit risk evaluation through data-driven methods.
