Sentiment Analysis using Natural Language Processing (NLP) Multi-Classification Support Vector Modeling for & Clustering / Segmentation using Latent Dirichlet Allocation (LDA):

This project uses Support Vector Machine algorithms to predict whether participants of a data science training program would be looking for a job change.

Titanic rescue prediction using Decision Tree, SVM, Logistic Regression, Random Forest and KNN. The best accuracy score was from Random Forest: 84.35%

Implementation of different types of machine learning algorithm and there performance comparison on a same dataset

ML Topics include KNN. Naive Bayes and Support vectors both in Theory and Python Code. KNN Imputation technique is also explained in this branch.

Heart Disease classification, Accuracy-85.25% (4models)

Demo_Projects_Benbhk_machine_learning_scikit-learn

Kernel-Methods on a Red-Wine Dataset

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.

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.

Uses Cuckoo Sandbox and a trained SV classifier to accurately detect ransomware samples.

Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.

AXA Data Science Challenge