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Competition conducted by American Express on HackerEarth Platform to Predict Credit Card Defaulters by building Machine Learning Models for the given data.

Home Page: https://www.hackerearth.com/challenges/competitive/amexpert-code-lab/

Jupyter Notebook 100.00%
machine-learning model-building logistic-regression random-forest deep-neural-networks xgboost-model eda analysis visualization outlier-detection

credit-card-default-risk---amexpert-codelab's Introduction

Credit Card Default Risk - AmExpert CodeLab

Competetion in HackerEarth conducted by American Express on HackerEarth Platform

About the Problem and Task:

Credit card Default risk is the chance that companies or individuals will not be able to return the money lent on time.

Task:

We are given relevant information about the customers of a company. And we're required to build a Machine Learning Model that can predict if there will be CREDIT CARD DEFAULTERS.

Data Description:

DataDescription Table

Evaluation Metrics is: score: 100 * (metrics.f1_score(actual, predicted, average="macro"))

We got the scores from each of the model as given below:

  1. Logistic Regression: 84.85652
  2. Random Forest: 89.27377
  3. Artificial Neural Networks: 87.46621
  4. XGBoost Model: 91.15589

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