In this project, we build and train a model to predict if a customer will defer on a particular loan on an imbalanced dataset. We'll build a layered ANN for this and try to make our model better using Hyperparamater Optimization, before exploring Oversampling to make it more accurate.

Using Python to build and evaluate several machine learning models to predict credit risk.

Sprint 6, Task 1

Improving Machine Learning Performance by: feature engineering, oversampling and hyperparameter tuning

🔍 Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset.

Mostly in Banking domains or credit card use cases, the data for predicting a transaction as fraudulent is extremely low due to less evidence for fraud cases resulting in an Imbalanced Dataset for ML use cases. This notebook deals with 3 techniques of handling such cases.

Oversample 44.1kHz WAV file to 352.8kHz

Supervised machine learning model to classify loan applicants into high and low risk categories

Class imbalance correction algorithm for multiple-instance data

Experiments with imbalanced data using undersampling and oversampling techniques.

This is an end-to-end machine learning model in which I implement random-forest and decision tree classifiers to predict heart disease. I utilized cross-validation, and oversampling to deal with an imbalanced dataset.

Build and evaluate several machine learning algorithms to predict credit risk

Supervised Learning..Build/Evaluate Machine algorithms to predict credit risk

This dataset was used to learn more about how some machine learning models work: KNN, Naive Bayes, and Decision Tree. It also includes some model evaluation metrics: Precision, Recall, Accuracy, and F1-Score. These metrics were derived from the confusion matrix.

Built and evaluated several machine learning algorithms to predict credit risk.

Tugas akhir saat saya melakukan virtual internship ID/X pada laman rakamin

Financial distress prediction from Kaggle

Prediction of stroke in patients using machine learning algorithms.

Columbia FinTech Boot Camp Homework - Programs to utilize resampling and ensemble machine learning models to predict credit risk for retail loans.