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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: 5 days ago
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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 (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-10T13:10:28.000Z (over 1 year ago)
- Last Synced: 2024-11-13T15:54:32.736Z (2 months 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
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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)