136 skills found · Page 4 of 5
tufts-ml / False Alarm ControlCode for training binary classifiers to maximize recall subject to a minimum precision constraint. Paper published at AISTATS '22.
jaiprasadreddy / Binaryclassifier RandomforestBinary classification based on cost sensitive random forest classifier for tumor classification using gene expression data
James-sjt / Vision Mamba Skin Cancer ClassifierThis Mamba-based model is used to classify skin cancer. After 20 epochs training, this model can achieve 99% accuracy in binary-classification task.
ChanithaAbey / Breast Cancer Detection MLThis personal project incorporates a machine learning model to detect breast cancer using a dataset by scikit-learn. By using Logistic Regression the model is trained to classify tumors to either a malignant (cancerous) class or a benign (non-cancerous) class, offering reliable predictions for simple binary medical classification tasks.
neurlang / ClassifierNeurlang binary classifier (Hashtron)
PtPrashantTripathi / IPL 2020 PredictionPredictive Analysis of an IPL Match using SVM Binary Classifier
aseveryn / SVMTK Multiclass ClassifierPython wrapper around SVM-TK binary classifier to perform multiclass classification
ayushjain19 / Naive Bayes ClassifierNaive Bayes Classifier with stop words | Naive Bayes Classifier without stop words | Binary Naive Bayes Classifier
plissonf / BBB ModelsDeveloped binary ensemble classifiers to predict the Blood-Brain Barrier (BBB) permeability of small organic compounds. Applied our best models to natural products of marine origin, able to inhibit kinases associated with neurodegenerative disorders.
mathurpulkit / MemesvsNotesA binary classifier of Memes vs Notes.
rosarioscavo / Instagram Hashtags Generator"Instagram Hashtag Generator" analyzes an image and gives back some hashtags based on a prediction of binary classifiers. It is possible to use two different classifiers, a classifier based on Logistic Regression and the other one on k-nearest neighbors.
bhuyanamit986 / Spam ClassifierSpam Classifier Spam detection is one of the major applications of Machine Learning in the interwebs today. Pretty much all of the major email service providers have spam detection systems built in and automatically classify such mail as 'Junk Mail'. In this mission we will be using the Naive Bayes algorithm to create a model that can classify dataset SMS messages as spam or not spam, based on the training we give to the model. It is important to have some level of intuition as to what a spammy text message might look like. Usually they have words like 'free', 'win', 'winner', 'cash', 'prize' and the like in them as these texts are designed to catch your eye and in some sense tempt you to open them. Also, spam messages tend to have words written in all capitals and also tend to use a lot of exclamation marks. To the recipient, it is usually pretty straightforward to identify a spam text and our objective here is to train a model to do that for us! Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam' and nothing else. Also, this is a supervised learning problem, as we will be feeding a labelled dataset into the model, that it can learn from, to make future predictions.
Tech-with-Vidhya / Productionized Docker ML Model Application Into Kubernetes Cluster Using AWS EKS CloudFormation EMRThis project covers the end to end implementation of deploying and productionizing a dockerized/containerized machine learning python flask application into Kubernetes Cluster using the AWS Elastic Kubernetes Service (EKS), AWS Serverless Fargate Instances, AWS CloudFormation Cloud Stack and AWS Elastic Container Registry (ECR) Service. The machine learning business case implemented in this project includes a bank note authentication binary classifier model using Random Forest Classifier; which predicts and classifies a bank note either as a Fake Bank Note (Label 0) or a Genuine Bank Note (Label 1). Implementation Steps: 1. Creation of an end to end machine learning solution covering all the ML life-cycle steps of Data Exploration, Feature Selection, Model Training, Model Validation and Model Testing on the unseen production data. 2. Saved the finalised model as a pickle file. 3. Creation of a Python Flask based API; in order to render the ML model solution and inferences to the end-users. 4. Verified and tested the working status of the Python Flask API in the localhost set-up. 5. Creation of a Docker File (containing the steps/instructions to create a docker image) for the Python Flask based Bank Note Authentication Machine Learning Application embedded with Random Forest ML Classifier Model. 6. Creation of IAM Service Roles with appropriate policies to access the AWS Elastic Container Registry (ECR) Service and AWS Elastic Kubernetes Service (EKS) and AWS CloudFormation Service. 7. Created a new EC2 Linux Server Instance in AWS and copied the web application project’s directories and its files into the AWS Linux Server using SFTP linux commands. 8. Installed the Docker software and the supporting python libraries in the AWS EC2 Linux Server Instance; as per the “requirements.txt” file. 9. Transformation of the Docker File into a Docker Image and Docker Container representing the application; using docker build and run commands. 10. Creation of a Docker Repository within the AWS ECR Service and pushed the application docker image into the repository using AWS Command Line Interface (CLI) commands. 11. Creation of the Cloud Stack with private and public subnets using the AWS CloudFormation Service with appropriate IAM roles and policies. 12. Creation of the Kubernetes Cluster using the AWS EKS Service with appropriate IAM roles and policies and linked the cloud stack created using the AWS CloudFormation Service. 13. Creation of the AWS Serverless Fargate Profile and Fargate instances/nodes. 14. Creation and configured the “Deployment.yaml” and “Service.yaml” files using the Kubernetes kubectl commands. 15. Applied the “Deployment.yaml” with pods replica configuration to the AWS EKS Cluster Fargate Nodes; using the Kubernetes kubectl commands. 16. Applied the “Service.yaml” using the Kubernetes kubectl commands; to render and service the machine learning application to the end-users for public access with the creation of the production end-point. 17. Verified and tested the inferences of the productionized ML Application using the AWS Fargate end-point created in the AWS Kubernetes EKS Cluster. Tools & Technologies: Python, Flask, AWS, AWS EC2, Linux Server, Linux Commands, Command Line Interface (CLI), Docker, Docker Commands, AWS ECR, AWS IAM, AWS CloudFormation, AWS EKS, Kubernetes, Kubernetes kubectl Commands.
abichat / EvabicEvaluation of Binary Classifiers
Nikoletos-K / Quantum ML Classification With PennyLanePython (PennyLane) simulation for a quantum classifier using a variational quantum circuit for classifying data for the binary classification parity problem with 3 inputs
RaoulHeese / QtreeQuantum decision tree classifiers for binary data.
LaviniaChen / Segment And Erase NetworkSegment-and-Erase Network: Cascaded Binary Classifiers for Multi-label Brain Tumor Segmentation
dev-xero / Titaniatitania: a binary classifier that estimates the likelihood of hypothetically surving the titanic incident.
alsulke / Social Media Comments ClassificationThe multi label classification of comments using binary relevance and classifier chain transformation methods.
VL97 / Sand Dune Detection And Delineation On MARSThis project utilises ML based classifier and segmentation (U-NET) techniques to work upon texture information extracted through Local Binary Pattern(LBP) of extraterrestrial dune field images. The aim is to detect and delineate such sand dunes in a given dune field.
