{"repo":"RubixML/Credit","free":true,"listed":false,"github":"https://github.com/RubixML/Credit","clone":"git clone https://github.com/RubixML/Credit.git","description":"An example project that predicts risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset.","language":"PHP","stars":36,"topics":["php","machine-learning","logistic-regression","data-science","classification","credit-card","rubix-ml","dataset","analytics","t-sne"],"license":"MIT","category":"machine-learning","readme_excerpt":"Rubix ML - Credit Card Default Predictor An example Rubix ML project that predicts the probability of a customer defaulting on their credit card bill next month using a Logistic Regression estimator and a 30,000 sample dataset of credit card customers. We'll also describe the dataset using statistics and visualize it using a manifold learning technique called t-SNE. - Difficulty: Medium - Training time: Minutes Installation Clone the project locally using Composer: Requirements - PHP 7.4 or above Recommended - Tensor extension for faster training and inference - 1G of system memory or more Tutorial Introduction The dataset provided to us contains 30,000 labeled samples from customers of a Taiwanese credit card issuer. Our objective is to train an estimator that predicts the probability of a customer defaulting on their credit card bill the next month. Since this is a binary classification problem ( will default or won't default) we can use the binary classifier Logistic Regression which implements the Probabilistic interface to make our predictions. Logistic Regression is a supervised learner that trains a linear model using an algorithm called Gradient Descent under the hood. Note: The source code for this example can be found in the train.php file in project root. Extracting the Data In Rubix ML, data are passed in specialized containers called Dataset objects. We'll start by extracting the data provided in the dataset.csv file using the built-in CSV extractor and then inst","default_branch":null,"files":null,"tree":[],"storefront":"/r/RubixML","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/RubixML/Credit/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}