Topic: support-vector-classification Goto Github
Some thing interesting about support-vector-classification
Some thing interesting about support-vector-classification
support-vector-classification,Titanic rescue prediction using Decision Tree, SVM, Logistic Regression, Random Forest and KNN. The best accuracy score was from Random Forest: 84.35%
User: alicevillar
support-vector-classification,
User: amatov
support-vector-classification,Sentiment Analysis using Natural Language Processing (NLP) Multi-Classification Support Vector Modeling for & Clustering / Segmentation using Latent Dirichlet Allocation (LDA):
User: andiosika
support-vector-classification,Demo_Projects_Benbhk_machine_learning_scikit-learn
User: benjaminbhk
support-vector-classification,Kernel-Methods on a Red-Wine Dataset
User: benjaminrueling
support-vector-classification,Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.
Organization: brainhack-school2020
support-vector-classification,This project uses Support Vector Machine algorithms to predict whether participants of a data science training program would be looking for a job change.
User: chloe-s-wang
support-vector-classification,This project implements the Support Vector Machine (SVM) algorithm for predicting user purchase classification. The goal is to train an SVM classifier to predict whether a user will purchase a particular product or not.
User: kshitizrohilla
support-vector-classification,Implementation of different types of machine learning algorithm and there performance comparison on a same dataset
User: prabhatk579
support-vector-classification,Heart Disease classification, Accuracy-85.25% (4models)
User: saanvi-tayal
support-vector-classification,ML Topics include KNN. Naive Bayes and Support vectors both in Theory and Python Code. KNN Imputation technique is also explained in this branch.
User: sandipanpaul21
support-vector-classification,This Getting Started Tutorial systematically demonstrates the typical ML work process step-by-step using the powerful and performant Support Vector Classifier (SVC) and the beginner-friendly Iris Dataset. Furthermore, the selection of the correct SVC kernel and its parameters are described and their effects on the classification result are shown.
User: urmel79
support-vector-classification,AXA Data Science Challenge
User: wizzx7
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