vmieres / Machine-Learning

This repo is about Machine Learning and Classification

Machine-Learning

Machine Learning

This assignment is about evaluating several machine-learning models to predict credit risk using free data from LendingClub. Credit risk is an inherently imbalanced classification problem, so I used different techniques for training and evaluating models with imbalanced classes. I used the imbalanced-learn and Scikit-learn libraries to build and evaluate models using the two following techniques:

Resampling

Imbalanced learn library for resampling the LendingClub data and building and evaluating logistic regression classifiers using the resampled data.

Findings

Which model had the best balanced accuracy score?

• Naive Random Oversampling

Which model had the best recall score?

• Cluster Centroids

Which model had the best geometric mean score?

• SMOTE

Ensemble Learning

Used two different ensemble classifiers to evaluate each model and predict loan risk and . As well as the Balanced Random Forest Classifier and the Easy Ensemble Classifier.

Findings

Which model had the best balanced accuracy score?

• Easy Ensemble Classifier

Which model had the best recall score?

• Easy Ensemble Classifier

Which model had the best geometric mean score?

• Easy Ensemble Classifier

What are the top three features?

• (0.09175752102205247, 'total_rec_prncp')
• (0.06410003199501778, 'total_pymnt_inv')
• (0.05764917485461809, 'total_pymnt')