Credit risk prediction using machine learning models involves using algorithms to assess the likelihood of a borrower defaulting on a loan or failing to meet their financial obligations. This process is crucial for financial institutions to make informed decisions about lending money to individuals or businesses. Processes executed/done involved :
- Data Collection and Preprocessing
- Feature Selection/Engineering
- Splitting the Data
- Model Selection
- Model Training
- Model Evaluation
The dataset used in this project is provided by prolifics ,hyderabad . This project is mainly focused on comparison of different accuracy scores and precision of the regression line on the following graphs by using different ML models such as :
- Desicion Tree
- Random Forest
- SVM (Support Vector Machine)
- Linear Regression
- XGboost (Extreme Gradient Boosting)
- CATboost (Categorical Boosting)