Banking-Dataset-Marketing-Targets

Using machine learning to determine which model is best at predicting credit risk amongst random oversampling, SMOTE, ClusterCentroids, SMOTEENN, Balanced Random Forest, or Easy Ensemble Classifier (AdaBoost).

Analyzing credit card risk with machine learning models!

An analysis on credit risk

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries

Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.

Testing various supervised machine learning models to predict a loan applicant's credit risk.

Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, you’ll need to employ different techniques to train and evaluate models with unbalanced classes. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company,

Analyze of several Machine Learning techniques in order to help Jill decide on a most effective Machine Learning Model to analyze Credit Card Risk applications.

Machine learning models for predicting credit risk in LendingClub dataset.

Credit_Risk_Analysis using Machine Learning