Loan-Prediction-by-XGBoost-Model

Predict the potential loan defaulters

Problem statement:

Predict the potential loan defaulters using the Gradient Boosting Method. Following attributes are available

About the Data

The risk_flag indicates whether there has been a default in the past or not.

Usage

• Just run jupyter notebook in terminal and it will run in your browser.

  Install Jupyter here i've you haven't.

• install xgboost by using pip install xgb in command line prompt/ anconda i've you haven't.

Modules needed:

- xgboost
- Pandas
- Scikit-Learn &
- seaborn

Steps

Model performance

• I have acheived model accuracy 82%, but the goal is predict the correctly classified who, approved for loan, means we have more focused on recall, (81%)

             precision   recall  f1-score   support
  
       0       0.97      0.83      0.90     66329
       1       0.40      0.80      0.54      9271
  

  accuracy 0.83 75600 macro avg 0.68 0.82 0.72 75600 weighted avg 0.90 0.83 0.85 75600

• 82.96% Accurate

Dataset:

https://github.com/rahkum96/Loan-Prediction-by-XGBoost-Model/blob/main/Loan%20Prediction_Training%20Data.csv