Developed Machine Learning Models to Predict Credit Risk
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Updated
Mar 27, 2022 - Jupyter Notebook
Developed Machine Learning Models to Predict Credit Risk
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).
We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.
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,
An analysis on credit risk
Analyzing credit card risk with machine learning models!
Apply machine learning to solve the challenge of credit risk
Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
Testing various supervised machine learning models to predict a loan applicant's credit risk.
Build and evaluate several machine learning algorithms by resampling models to predict credit risk.
Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn
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.
Built several supervised machine learning models to predict the credit risk of candidates seeking loans.
I am asked to resample the credit card data since it is not balanced. First, I start to split the data and perform oversampling with RandomOverSampler and SMOTE method, and I undersample with ClusterCentroids algorithm. Then, I utilize the SMOTEENN method to oversample and undersample the data. Finally, I used ensemble models.
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.
Credit_Risk_Analysis using Machine Learning
Train and evaluate models to determine credit card risk using a credit card dataset
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