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Machinehack-analytics-olympiad-2022\n\n### Competition hosted on \u003ca href=\"https://machinehack.com/hackathons/analytics_olympiad_2022/overview\"\u003eMachinehack\u003c/a\u003e\n\n# About\n\n### 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.\n\n### The Final Competition score is 0.68081\n\n### Leaderboard Rank is 24\n\n### The Evaluation Metric is Logloss.\n\n### File information\n \n * machinehack-analytics-olympiad-2022-eda.ipynb [![Open in Kaggle](https://img.shields.io/static/v1?label=\u0026message=Open%20in%20Kaggle\u0026labelColor=grey\u0026color=blue\u0026logo=kaggle)](https://www.kaggle.com/code/hari141v/machinehack-analytics-olympiad-2022-eda/notebook)\n    #### Basic Exploratory Data Analysis\n    #### Packages Used,\n        * seaborn\n        * Pandas\n        * Numpy\n        * Matplotlib\n* machinehack-analytics-olympiad-2022-model.ipynb [![Open in Kaggle](https://img.shields.io/static/v1?label=\u0026message=Open%20in%20Kaggle\u0026labelColor=grey\u0026color=blue\u0026logo=kaggle)](https://www.kaggle.com/code/hari141v/machinehack-analytics-olympiad-2022-model/notebook)\n    #### Data Pre-processing and model. \n    #### Packages Used,\n        * Sklearn\n        * Pandas\n        * Numpy\n        * Matplotlib\n        * catboost\n        * optuna\n        * shap\n     #### Created catboost classifier model and tuned the hyperparameters by using optuna framework. Model evaluated with Logloss. \n     #### [For more detailed information about the model.](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Approach_Machinehack_analytics_olympiad_2022.pdf)\n     \n\n### Catboost model Optimization History - Explains the best score at each trials.\n![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20optuna%20optimization%20history%20for%20100%20trials.png)\n\n### Catboost – SHAP feature importance\n![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20SHAP%20feature%20importances.png)\n\n### Catboost – SHAP top feature impact\n![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20SHAP%20top%20feature%20impact%20the%20model.png)\n\n### Top feature influences for class 1\n![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20SHAP%20top%20feature%20influences%20for%20class%201.png)\n\n### Top feature influences for class 0\n![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20SHAP%20top%20feature%20influences%20for%20class%200.png)\n\n### Overall Train and Validation Logloss\n![Alt text](https://github.com/hariprasath-v/Machinehack-analytics-olympiad-2022/blob/main/Model%20Visualization/Catboost%20optuna%20overall%20train%20and%20validation%20logloss.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhariprasath-v%2Fmachinehack-analytics-olympiad-2022","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhariprasath-v%2Fmachinehack-analytics-olympiad-2022","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhariprasath-v%2Fmachinehack-analytics-olympiad-2022/lists"}