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Loan classification with sklearn

Using sklearn module to create and evaluate various machine learning models. This is the final project of the Coursera's Machine Learning with Python course.

About dataset

This dataset is about past loans. The Loan_train.csv data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:

Field Description
Loan_status Whether a loan is paid off on in collection
Principal Basic principal loan amount at the
Terms Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule
Effective_date When the loan got originated and took effects
Due_date Since it’s one-time payoff schedule, each loan has one single due date
Age Age of applicant
Education Education of applicant
Gender The gender of applicant

We use the training set to build an accurate model and then use the test set to report the accuracy of the model using the following algorithms:

  • K Nearest Neighbor(KNN)
  • Decision Tree
  • Support Vector Machine
  • Logistic Regression

Report :

Algorithm Jaccard F1-score LogLoss
KNN 0.703704 0.686067 NA
Decision Tree 0.796296 0.780146 NA
SVM 0.722222 0.621266 NA
Logistic Regression 0.740741 0.630418 0.493198

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Using sklearn module to create and evaluate various machine learning models.

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