https://github.com/hariprasath-v/machinehack_analytics_olympiad_2023

Create a machine learning model to determine the likelihood of a customer defaulting on a loan based on credit history, payment behavior, and account details.
https://github.com/hariprasath-v/machinehack_analytics_olympiad_2023

binaryclassification catboost exploratory-data-analysis machine-learning numpy pandas python scikit-learn shap

Last synced: 6 days ago
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Create a machine learning model to determine the likelihood of a customer defaulting on a loan based on credit history, payment behavior, and account details.

• Host: GitHub
• URL: https://github.com/hariprasath-v/machinehack_analytics_olympiad_2023
• Owner: hariprasath-v
• License: apache-2.0
• Created: 2023-10-10T13:03:40.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-10T14:13:14.000Z (about 1 year ago)
• Last Synced: 2024-01-29T06:17:27.502Z (10 months ago)
• Topics: binaryclassification, catboost, exploratory-data-analysis, machine-learning, numpy, pandas, python, scikit-learn, shap
• Language: HTML
• Homepage:
• Size: 6.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machinehack_analytics_olympiad_2023

### Competition hosted on Machinehack

# About

### Create a machine learning model to determine the likelihood of a customer defaulting on a loan based on credit history, payment behavior, and account details.

### The Final Competition score is 1.0

### Leaderboard Rank is 5/158

### The Evaluation Metric is roc_auc_score.

### File information

 

 * analytics-olympiad-2023-eda.ipynb [![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/code/hari141v/analytics-olympiad-2023-eda)

    #### Basic Exploratory Data Analysis

    #### Packages Used,

        * seaborn

        * Pandas

        * Numpy

        * Matplotlib

* machinehack-analytics-olympiad-2022-model.ipynb [![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/code/hari141v/analytics-olympiad-2023-model)

    #### Data Pre-processing and model. 

    #### Packages Used,

        * Sklearn

        * Pandas

        * Numpy

        * Matplotlib

        * catboost

        * shap

     #### The Catboost model was trained separately for both targets, using default parameters.

     #### The model was evaluated at each iteration using validation data.

     #### The model's performance was assessed using an accuracy score. 

     #### [For more detailed information about the model.](https://github.com/hariprasath-v/Machinehack_analytics_olympiad_2023/blob/main/Analytics%20Olympiad%202023.pdf)

     

### Catboost – SHAP feature importance for primary close flag

![Alt text](https://github.com/hariprasath-v/Machinehack_analytics_olympiad_2023/blob/main/EDA_and_Model_Interpretation_Visualization/SHAP_Global_feature_importance_Primary_close_flag.png)

### Catboost – SHAP feature importance for final close flag

![Alt text](https://github.com/hariprasath-v/Machinehack_analytics_olympiad_2023/blob/main/EDA_and_Model_Interpretation_Visualization/SHAP_Global_feature_importance_Final_close_flag.png)