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https://github.com/sarahm44/credit-risk-predictor

Uses several machine learning models to predict credit risk.
https://github.com/sarahm44/credit-risk-predictor

balanced-random-forest cluster-centroids cluster-centroids-undersampling credit-risk easy-ensemble-classifier ensemble-learning fintech logistic-regression machine-learning naive-random-oversampler random-forest resampling smote smote-oversampler smoteenn-combination

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Uses several machine learning models to predict credit risk.

Host: GitHub
URL: https://github.com/sarahm44/credit-risk-predictor
Owner: sarahm44
Created: 2022-05-04T02:04:10.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2022-08-08T12:04:37.000Z (over 2 years ago)
Last Synced: 2025-01-26T05:18:42.326Z (18 days ago)
Topics: balanced-random-forest, cluster-centroids, cluster-centroids-undersampling, credit-risk, easy-ensemble-classifier, ensemble-learning, fintech, logistic-regression, machine-learning, naive-random-oversampler, random-forest, resampling, smote, smote-oversampler, smoteenn-combination
Language: Jupyter Notebook
Homepage:
Size: 9.54 MB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

        # Credit Risk Predictor

 

![](https://github.com/sarahm44/credit-risk-predictor/blob/main/images/credit-risk.jpg)

## Table of Contents

- [Overview](#overview)

- [Resampling](#resampling)

  * [Logistic Regression](#logistic-regression)

  * [Oversampling](#oversampling)

  * [Undersampling](#undersampling)

  * [Combination Sampling](#combination-sampling)

  * [Conclusions](#conclusions)

- [Ensemble Learning](#ensemble-learning)

  * [Balanced Random Forest](#balanced-random-forest)

  * [Easy Ensemble Classifier](#easy-ensemble-classifier)

  * [Conclusions](#conclusions-1)

## Overview

This task required that I build and evaluate several machine learning models to predict credit risk using data you'd typically see from peer-to-peer lending services. 

Credit risk is an inherently imbalanced classification problem (the number of good loans is much larger than the number of at-risk loans), so I employed different techniques for training and evaluating models with imbalanced classes. 

I used the [imbalanced-learn](https://imbalanced-learn.org/stable/) and [Scikit-learn](https://scikit-learn.org/stable/) libraries to build and evaluate models using the two following techniques, each contained in their own code file:

1. [Resampling](https://github.com/sarahm44/credit-risk-predictor/blob/main/credit_risk_resampling.ipynb)

2. [Ensemble Learning](https://github.com/sarahm44/credit-risk-predictor/blob/main/credit_risk_ensemble.ipynb)

## Resampling

### Logistic Regression

I completed a simple linear regression. I then generated the balanced accuracy score, confusion matrix and classification report, as below:

![](https://github.com/sarahm44/credit-risk-predictor/blob/main/images/logistic_regression.png)

### Oversampling

In this section, I compared two oversampling algorithms to determine which algorithm results in the best performance. 

These algorithms were: 

* naive random oversampling algorithm

* SMOTE algorithm. 

I then generated the balanced accuracy score, confusion matrix and classification report for each.

See naive random oversampling algorithm results below:

![](https://github.com/sarahm44/credit-risk-predictor/blob/main/images/nro.png)

See SMOTE algorithm results below:

![](https://github.com/sarahm44/credit-risk-predictor/blob/main/images/smote.png)

### Undersampling

In this section, I tested an undersampling algorithm to determine which algorithm results in the best performance compared to the oversampling algorithms above. 

I undersampled the data using the Cluster Centroids algorithm.

I then generated the balanced accuracy score, confusion matrix and classification report.

See Cluster Centroids algorithm results below:

![](https://github.com/sarahm44/credit-risk-predictor/blob/main/images/cc.png)

### Combination Sampling

In this section, I tested a combination over- and under-sampling algorithm to determine if the algorithm results in the best performance compared to the other sampling algorithms above. 

I resampled the data using the SMOTEENN algorithm.

I then generated the balanced accuracy score, confusion matrix and classification report.

See the SMOTEENN algorithm results below:

![](https://github.com/sarahm44/credit-risk-predictor/blob/main/images/smoteenn.png)

### Conclusions

#### Best Balanced Accuracy Score

| Model | Balanced Accuracy Score |

| --- | --- |

| Simple logistic regression | 0.9892813049736127 |

| Naive random oversampling | 0.9946414201183431 |

| SMOTE oversampling | 0.9946680739911509 |

| Cluster Centroids undersampling | 0.9932813049736127 |

| SMOTEENN combination sampling | 0.9946680739911509 |

   

SMOTE oversampling and combination sampling with SMOTEENN had the best balanced accuracy scores.

#### Best Recall Score

| Model | High Risk | Low Risk | Average |

| --- | --- | --- | --- |

| Simple logistic regression | 0.98 | 0.99 | 0.99 |

| Naive random oversampling | 1.00 | 0.99 | 0.99 | 

| SMOTE oversampling | 1.00 | 0.99 | 0.99 |

| Cluster Centroids undersampling | 0.99 | 0.99 | 0.99 |

| SMOTEENN combination sampling | 1.00 | 0.99 | 0.99 |

   

Naive random oversampling, SMOTE oversampling and combination sampling with SMOTEENN have the best recall scores.

#### Best Geometric Mean Score

| Model | High Risk | Low Risk | Average |

| --- | --- | --- | --- |

| Simple logistic regression | 0.99 | 0.99 | 0.99 |

| Naive random oversampling | 0.99 | 0.99 | 0.99 |

| SMOTE oversampling | 0.99 | 0.99 | 0.99 |

| Cluster Centroids undersampling | 0.99, | 0.99 | 0.99 |

| SMOTEENN combination sampling | 0.99 | 0.99 | 0.99 |

   

Every model had the same geometric mean score.

## Ensemble Learning

In this section, I trained and compared two different ensemble classifiers to predict loan risk and evaluate each model. 

These were: 

* Balanced Random Forest Classifier 

* Easy Ensemble Classifier

I generated the balanced accuracy score, confusion matrix and classification report for each.

### Balanced Random Forest

See the Balanced Random Forest Classifier results below:

![](https://github.com/sarahm44/credit-risk-predictor/blob/main/images/random_forest.png)

### Easy Ensemble Classifier

See the Easy Ensemble Classifier results below:

![](https://github.com/sarahm44/credit-risk-predictor/blob/main/images/easy_ensemble.png)

### Conclusions

#### Best Balanced Accuracy Score

| Model | Balanced Accuracy Score |

| --- | --- |

| Balanced Random Forest Classifier | 0.795829959187949 |

|Easy Ensemble Classifier | 0.9263912558266958 |

   

Easy Ensemble Classifier had the best balanced accuracy score.

#### Best Recall Score

| Model | High Risk | Low Risk | Average |

| --- | --- | --- | --- |

| Balanced Random Forest Classifier | 0.71 | 0.88 | 0.88 |

| Easy Ensemble Classifier | 0.91 | 0.94 | 0.94 |

   

Easy Ensemble Classifier had the best recall score.

#### Best Geometric Mean Score

| Model | High Risk | Low Risk | Average |

| --- | --- | --- | --- |

| Balanced Random Forest Classifier | 0.79 | 0.79 | 0.79 |

| Easy Ensemble Classifier | 0.93 | 0.93 | 0.93 |

   

Easy Ensemble Classifier had the best geometric mean score.

#### Top three features

The top three features are:    

   * total_rec_prncp

   * total_rec_int

   * total_pymnt_in