An ensemble of machine learning models for detecting fraudulent credit card transactions, utilizing advanced techniques for feature selection, data imbalance handling, and hyperparameter tuning.

Over- and under-sampled data using four algorithms and compared two machine learning models that reduce bias to identify the most reliable credit risk prediction model.

Using skills in data preparation, statistical reasoning, and machine learning, real-world challenges of credit card risk are assessed and solved.

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.

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.

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

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.

An analysis on credit risk

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).

To evaluate the performance of supervised machine learning models to make a written recommendation on whether they should be used to predict credit risk.

This project applies supervised machine learning models to predict credit risk, and compare algorithm effectiveness in an unbalanced classification problem

Developed Machine Learning Models to Predict Credit Risk

Train and evaluate models to determine credit card risk using a credit card dataset

Analysis using RandomOverSampler, SMOTE algorithm, ClusterCentroids algorithm, SMOTEENN algorithm, and machine learning models BalancedRandomForestClassifier and EasyEnsembleClassifier.

Apply machine learning to solve the challenge of credit risk

Supervised Machine Learning Project: imbalanced-learn; scikit-learn; RandomOverSampler; SMOTE; ClusterCentroids; SMOTEENN; BalancedRandomForestClassifier; EasyEnsembleClassifier.

Built several supervised machine learning models to predict the credit risk of candidates seeking loans.

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

Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of c…