Supervised scikit-learn machine learning models using several sampling techniques.
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Updated
Sep 25, 2020 - Jupyter Notebook
Supervised scikit-learn machine learning models using several sampling techniques.
The purpose of this study is to recommend whether PureLending should use machine learning to predict credit risk. Several machine learning models are built employing different techniques, then they are compared and analyzed to provide the recommendation.
Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.
Apply machine learning to solve the challenge of credit risk
Analysis of a dataset using different techniques to train and evaluate models with unbalanced classes, aimed at reducing bias and predicting accurate credit risk.
This project applies supervised machine learning models to predict credit risk, and compare algorithm effectiveness in an unbalanced classification problem
Using Supervised Machine Learning algorithms to identify credit risks
Using supervised machine learning to predict credit risk. Trying oversampling, under sampling, combination sampling and ensemble learning to find the model with the best fit
Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.
About Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results. Topics
A fraud detection (exploratory) project using machine learning algorithms over 11 million real transactions
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