https://github.com/hariprasath-v/hackerearth_transunion-data-science-analytics-hiring-challenge_2022

Machine learning model to classify the credit score based on people bank details and credit related information.
https://github.com/hariprasath-v/hackerearth_transunion-data-science-analytics-hiring-challenge_2022

catboost exploratory-data-analysis klib machine-learning matplotlib numpy optuna pandas python seaborn sklearn

Last synced: 6 days ago
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Machine learning model to classify the credit score based on people bank details and credit related information.

• Host: GitHub
• URL: https://github.com/hariprasath-v/hackerearth_transunion-data-science-analytics-hiring-challenge_2022
• Owner: hariprasath-v
• License: apache-2.0
• Created: 2022-06-21T05:47:26.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-06-21T05:59:24.000Z (over 2 years ago)
• Last Synced: 2023-03-04T22:49:19.558Z (over 1 year ago)
• Topics: catboost, exploratory-data-analysis, klib, machine-learning, matplotlib, numpy, optuna, pandas, python, seaborn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 4.84 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Hackerearth_transunion-data-science-analytics-hiring-challenge_2022

### Competition hosted on  Hackerearth 

# About

### Machine learning model to classify the credit score based on people bank details and credit related information.

### Final Score 78.03

### Evaluation Metric is roc_wighted_ovr

### File information

 * HE_transunion_data_science_analytics_hiring_challenge_EDA.ipynb

    ### Packages Used,

        * seaborn

        * Pandas

        * klib

        * Numpy

        * Matplotlib

        * re

    ### Basic Exploratory Data Analysis

    

    

    

* transunion-data-science-analytics-hiring-challenge_model.ipynb

    ### Packages Used,

        * Sklearn

        * re

        * Pandas

        * Numpy

        * Matplotlib

        * catboost

     ### Data Pre-processing

         

     ### Created catboost classifier with 5-fold stratified cross validatation and tuned the hyperparameters with optuna                framework.

     ### Model explanation with SHAP

     ### [More information about data pre-processing and model tuning](https://github.com/hariprasath-v/Hackerearth_transunion-data-science-analytics-hiring-challenge_2022/blob/main/Approach%20%26%20Solution_transunion-data-science-analytics-hiring-challenge_2022.pdf)

 

    

### Catboost classifier model feature importances  

![Alt text](https://github.com/hariprasath-v/Hackerearth_transunion-data-science-analytics-hiring-challenge_2022/blob/main/Feature%20Importance%20Plot.png)

### Top features impact for Good class 

![Alt text](https://github.com/hariprasath-v/Hackerearth_transunion-data-science-analytics-hiring-challenge_2022/blob/main/Top%20features%20impact%20for%20class%20Good.png)

### Top features impact for Standard class

![Alt text](https://github.com/hariprasath-v/Hackerearth_transunion-data-science-analytics-hiring-challenge_2022/blob/main/Top%20features%20impact%20for%20class%20Standard.png)

### Top features impact for Poor class

![Alt text](https://github.com/hariprasath-v/Hackerearth_transunion-data-science-analytics-hiring-challenge_2022/blob/main/Top%20features%20impact%20for%20class%20Poor.png)