svm-learning
There are 53 repositories under svm-learning topic.
jakczo/Registration-Number-Recognizer
C++ language program that searches for a car license plate on a photo and then recognises its registration number. [ENG]
luisafernandarj/electronic_nose_for_triatomines
Smelling bugs with an Electronic Nose
nataljunior/Classifyingr-in-R
Classifying in R Identifying Categories for Customer Complaint’s Mediation Automation
nustalgic/Crypto-Algorithmic-Trading-Bot
We will be creating an algorithmic crypto trading bot that will use the Kraken API to get crypto prices. We will use machine learning to determine the trend of the market from historical data and determine the best strategies/indicators to use.
sabaaaaaaaaaa/MNIST
SVM and Deep on MNIST Dataset.
singhgaurav2323/classifier
supervised machine learning classifier model
avichaudhary29/Twitter-sentiment-analysis
Twitter sentiment analysis allows you to keep track of what's being said about your product or service on social media, and can help you detect angry customers or negative mentions before they they escalate.
Azizimj/Hyperparameter-robust-simulation-optimization
Hyperparameter tuning using a robust simulation optimization framework
Kovenda/Driver-s-Race-Classifer-traffic-stops
To see if drivers were being profiled. I built a Support Vector Machine (SVM) classifier and a randomForest classifier to predict a driver's race given the traffic's stop's details. Successful classification will indicate the existence of bais in the traffic stops' data.
Necl0/Scikit-SVM-example
A model I used (breast cancer dataset) to teach myself SVMs and learning more Scikit-learn w/ notes
noernimat/modeling_dataset_using_supervised_learning
Machine Learning Models for Absenteeism at Work Dataset
noernimat/sentiment_analysis_using_supervised_learning
Sentiment Analyst using Supervised Learning using Women's E-Commerce Clothing Reviews Dataset
Ohara124c41/MLND-Finding_Donors
Investigated factors that affect the likelihood of charity donations being made based on real census data. Developed a naive classifier to compare testing results to. Trained and tested several supervised machine learning models on preprocessed census data to predict the likelihood of donations. Selected the best model based on accuracy, a modified F-scoring metric, and algorithm efficiency.