/Credit_Risk_Model_VIX_ID-X_Partners

The objective project is to decrease the company's losses by up to 30% through bad loans by creating a machine learning system to assist in automating loan assessments

Primary LanguageJupyter Notebook

Credit_Risk_Model_VIX_ID-X_Partners

Problem Statement : the number of customers defaults reached 50,968 people and the company suffered a loss of $743,972,450 in 2007 to 2014

Goals : reduce company losses by up to 30 percent of bad loan

Objective : Create a ML system to help loan assessments automatically

Business Metrics : Cost

Result :

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Tools: Python, JupyterLab, Git

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

Dataset: Loan Data 2007-2014 [source]

Summary of the analysis

  • This dataset have 466,285 observations and 74 variables with 52 numerical variables, and 22 categorical variables.

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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.

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