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.