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In this repository i performed a support vector classfication on real life data in order to make a machine learning classfication model , initially i performed some data preprocessing technique in order to filter out the data flaws then undergoes the process of model building i.e SVM Classification
R package and C++ library that allows training SVM models in a GPU using CUDA and predicting out-of-sample data. A support vector machine (SVM) is a type of machine learning model that is trained using supervised data to classify samples.
The "Diabetes Prediction" project focuses on predicting the likelihood of an individual having diabetes using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), and Support Vector Machines (SVM), this project offers a comprehensive solution for accurate classification.
Easy to understand classification problem from a highly skewed kaggle dataset. Solved using logistic regression and SVM, code inspired from top contributor.
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.