{"repo":"saidsef/ml-classifier","free":true,"listed":false,"github":"https://github.com/saidsef/ml-classifier","clone":"git clone https://github.com/saidsef/ml-classifier.git","description":"ML-powered news classifier: categorise articles via REST API. Built with scikit-learn, model comparison with VotingClassifier, and a REST API service for real-time predictions with Docker and Kubernetes deployment","language":"Jupyter Notebook","stars":14,"topics":["machine-learning","data-visualization","classification","serverless-functions","openfaas-function","classify-news-articles","sklearn-classify","scikit-learn","sklearn","notebook-jupyter"],"license":"MIT","category":"machine-learning","readme_excerpt":"Machine Learning - News Articles classification with sklearn Classify news articles into different categories using Machine Learning. The dataset consists of 6000 documents and 47 categories. My goal is to show you how to create a predictive model(s) that will classification labels for news articles. Objective - To classify news articles - Learn the basics of natural language processing - Build models using sklearn and choose the best one - Use sklearn's make pipeline class - Learn how to turn it into a service - Learn how to make it composable and portable - ... - Profit? Prerequisite - Python = v3.12 - Jupyter Notebook - Some knowledge of Machine Learning Python Libs - NumPy - Pandas - SciPy - Matplotlib - Jupyter - Scikit-learn (the library that we will use later in this post when creating the classifier model(s)) We Will - Apply some preprocessing steps to prepare the data. - We will perform a descriptive analysis of the data to better understand the main characteristics that they have - We will continue by practicing how to train different machine learning models using scikit-learn. It is one of the most popular python libraries for machine learning - We will also use a subset of the dataset for training purposes - We will iterate and evaluate the learned models by using unseen data. Later, we will compare them until we find a good models that meets our expectations, and use a VotingClassifier soft voting for unfitted estimators. - Once we have chosen the candidate model","default_branch":null,"files":null,"tree":[],"storefront":"/r/saidsef","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/saidsef/ml-classifier/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."}