/German_Credit_Risk_Classification

Primary LanguageJupyter Notebook

💳 German Credit Risk Classification 💳

Alt Text

We made credit risk prediction on the data set taken from kaggle.

Dataset Information

The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes. The link to the original dataset can be found below.

Requirements

There are some general library requirements for the Project. The general requirements are as follows.

  • Numpy
  • Pandas
  • Scikit-learn

For Visualization

  • Matplotlib
  • Seaborn
  • Plotly

The library requirements specific to some methods are:

  • DecisionTree Model
  • GradientBoosting Model
  • XGBoost Model
  • LightGBM Model

Content

It's a classic dataset of Good and Bad Loans

  • Import Module and Data
  • Data Analysis
  • Data Classification
  • Data Visualization
  • Data Preprocessing
  • Building Models
    • DecisionTree Model
    • GradientBoosting Model
    • XGBoost Model
    • LightGBM Model

Members

Project Team
Furkan KARAKUZ
Oğuzhan AKKURT
Ali Cenk BAYTOP
Muhammed Nafiz CANITEZ