https://github.com/hariprasath-v/machinehack-analytics-olympiad-2022

Create a machine learning model to help an insurance company understand which claims are worth rejecting and the claims which should be accepted for reimbursement.
https://github.com/hariprasath-v/machinehack-analytics-olympiad-2022

catboost-classifier exploratory-data-analysis logloss machinehack numpy optuna pandas python scikit-learn shap

Last synced: 2 months ago
JSON representation

Create a machine learning model to help an insurance company understand which claims are worth rejecting and the claims which should be accepted for reimbursement.

• Host: GitHub
• URL: https://github.com/hariprasath-v/machinehack-analytics-olympiad-2022
• Owner: hariprasath-v
• License: apache-2.0
• Created: 2022-11-10T13:46:30.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2023-10-10T13:10:28.000Z (over 2 years ago)
• Last Synced: 2025-03-02T13:50:37.587Z (over 1 year ago)
• Topics: catboost-classifier, exploratory-data-analysis, logloss, machinehack, numpy, optuna, pandas, python, scikit-learn, shap
• Language: HTML
• Homepage:
• Size: 4.14 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Machinehack-analytics-olympiad-2022

### Competition hosted on Machinehack

# About

### Create a machine learning model to help an insurance company understand which claims are worth rejecting and the claims that should be accepted for reimbursement.

### The Final Competition score is 0.68081

### Leaderboard Rank is 24

### The Evaluation Metric is Logloss.

### File information

 

 * machinehack-analytics-olympiad-2022-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/machinehack-analytics-olympiad-2022-eda/notebook)

    #### 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/machinehack-analytics-olympiad-2022-model/notebook)

    #### Data Pre-processing and model. 

    #### Packages Used,

        * Sklearn

        * Pandas

        * Numpy

        * Matplotlib

        * catboost

        * optuna

        * shap

     #### Created catboost classifier model and tuned the hyperparameters by using optuna framework. Model evaluated with Logloss. 

     #### [For more detailed information about the model.](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Approach_Machinehack_analytics_olympiad_2022.pdf)

     

### Catboost model Optimization History - Explains the best score at each trials.

![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20optuna%20optimization%20history%20for%20100%20trials.png)

### Catboost – SHAP feature importance

![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20SHAP%20feature%20importances.png)

### Catboost – SHAP top feature impact

![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20SHAP%20top%20feature%20impact%20the%20model.png)

### Top feature influences for class 1

![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20SHAP%20top%20feature%20influences%20for%20class%201.png)

### Top feature influences for class 0

![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20SHAP%20top%20feature%20influences%20for%20class%200.png)

### Overall Train and Validation Logloss

![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20optuna%20overall%20train%20and%20validation%20logloss.png)