Machinehack_analytics_olympiad_2023

Competition hosted on Machinehack

About

Create a machine learning model to determine the likelihood of a customer defaulting on a loan based on credit history, payment behavior, and account details.

The Final Competition score is 1.0

Leaderboard Rank is 5/158

The Evaluation Metric is roc_auc_score.

File information

• analytics-olympiad-2023-eda.ipynb

  Basic Exploratory Data Analysis

  Packages Used,

   * seaborn
   * Pandas
   * Numpy
   * Matplotlib
  

• machinehack-analytics-olympiad-2022-model.ipynb

  Data Pre-processing and model.

  Packages Used,

    * Sklearn
    * Pandas
    * Numpy
    * Matplotlib
    * catboost
    * shap
  

  The Catboost model was trained separately for both targets, using default parameters.

  The model was evaluated at each iteration using validation data.

  The model's performance was assessed using an accuracy score.

  For more detailed information about the model.

Catboost – SHAP feature importance for primary close flag

Catboost – SHAP feature importance for final close flag