Credit Score Home Credit Indonesia

Problem Statement :
How do you help the assessment team examine customer loans?

Goals :
Increase the speed of filing inspection without increasing costs

Objective :
Create a system to help loan assessments automatically

Business Metrics :

• daily resolved applications
• average resolved time

Result :

Tools: Python, JupyterLab, Git

Libraries: Pandas, Numpy, Feature-engine, Scikit-learn, Imbalanced-learn, statistic-learn, imputer-learn, WoE Binning

Dataset: Home Credit Default Risk [source]

Summary of the analysis

• This dataset train have application train 307,511 observations and 122 variables with 106 numerical variables, 16 categorical variables and 1 target variable.
• This dataset test have application test 48,744 observations and 121 variables with 105 numerical variables, and 16 categorical variables

What I have learned

• Framing the business problem.
• Create a machine learning model with optimal of number of approved and number of rejected
• Create a scorecard that can generate credit score who rejected
• Make a business simulation from machine learning model.

File Dictionaries

• Credit_Score_Home_Credit_Indonesia.ipynb: this notebook contains all of project details, such as Problem Research, exploratory data analysis & insights from dataset, data preprocessing, modeling, scorecard, business recommendation
• VIX_Credit_Score_HCI_Archie_Citra_Muhammad.pdf: summary of the project.