/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.

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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

    Basic Exploratory Data Analysis

    Packages Used,

     * seaborn
 * Pandas
 * Numpy
 * Matplotlib
  • machinehack-analytics-olympiad-2022-model.ipynb Open in Kaggle

    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.

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

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Catboost – SHAP feature importance

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Catboost – SHAP top feature impact

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Top feature influences for class 1

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Top feature influences for class 0

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Overall Train and Validation Logloss

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