https://github.com/hariprasath-v/deloitte_presents_machine_learning_challenge_predict_loan_defaulters

A machine learning model that predicts the loan defaulter.
https://github.com/hariprasath-v/deloitte_presents_machine_learning_challenge_predict_loan_defaulters

catboost exploratory-data-analysis keras klib lightgbm optuna pandas python seaborn xgboost

Last synced: 25 days ago
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A machine learning model that predicts the loan defaulter.

• Host: GitHub
• URL: https://github.com/hariprasath-v/deloitte_presents_machine_learning_challenge_predict_loan_defaulters
• Owner: hariprasath-v
• Created: 2021-12-29T10:44:30.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-01-15T14:56:44.000Z (about 3 years ago)
• Last Synced: 2024-11-13T15:54:31.880Z (3 months ago)
• Topics: catboost, exploratory-data-analysis, keras, klib, lightgbm, optuna, pandas, python, seaborn, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 1.28 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# deloitte_presents_machine_learning_challenge_predict_loan_defaulters

### Competition hosted on  MACHINEHACK 

# About

### Build a machine learning model that predicts the loan defaulter.

#### Initially tried the ensemble models(catboost,xgboost,lightgbm).All the ensemble model's logloss values didn't show any difference. So i have tried neural network model and it gives smaller logloss value than the ensemble models and better leaderboard rank.

### Competition Public LB Rank: 146/465   & Private LB Rank: 146/465

### Final Score 0.3782

### Evaluation Metric is Logloss.

### File information

 * deloitte_ml_challenge_predict_loan_defaulters.ipynb

    ### Packages Used,

        * Sklearn

        * catboost

        * xgboost

        * lgbm

        * keras

        * Pandas

        * klib

        * Numpy

        * Matplotlib

        * Optuna

        * shap

        

     ### Basic Exploratory Data Analysis

     ### Created Catboost classifier model and tune the hyperparameters with the optuna framework.

     ### Created XGboost classifier model and tune the hyperparameters with the optuna framework.

     ### Created Lightgbm classifier model and tune the hyperparameters with the optuna framework.

     ### Model interpretation with shap  

     ### Created keras neural net model

     ### Model Comparison

     

### Feature Importance Catboost  

![Alt text](https://github.com/hariprasath-v/deloitte_presents_machine_learning_challenge_predict_loan_defaulters/blob/main/Catboost_Feature_Importance_Plot.png)

### Feature Importance XGboost  

![Alt text](https://github.com/hariprasath-v/deloitte_presents_machine_learning_challenge_predict_loan_defaulters/blob/main/XGBoost_Feature_Importance_Plot.png)

### Feature Importance Lightgbm  

![Alt text](https://github.com/hariprasath-v/deloitte_presents_machine_learning_challenge_predict_loan_defaulters/blob/main/LGBM_Feature_Importance_Plot.png)

### Model's Validation Logloss comparison

![Alt text](https://github.com/hariprasath-v/deloitte_presents_machine_learning_challenge_predict_loan_defaulters/blob/main/Model's_Validation-Logloss_Comparison.png)