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

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

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.

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

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

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

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

Demo_Projects_Benbhk_machine_learning_scikit-learn

AXA Data Science Challenge

Kernel-Methods on a Red-Wine Dataset

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

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