This is a Malware Detection ML model made using Random Forest Algorithm

Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning

I developed Machine Learning Software with multiple models that predict and classify AID362 biology lab data. Accuracy values are 99% and above, and F1, Recall and Precision scores are average (average of 3) 78.33%. The purpose of this study is to prove that we can establish an artificial intelligence (machine learning) system in health. With my…

The objective of this project is to determine the risk of default that a client presents and assign a risk rating to each client. The risk rating will determine if the company will approve (or reject) the loan application

Diabetes mellitus, commonly known as diabetes is a metabolic disease that causes high blood sugar. The hormone insulin moves sugar from the blood into your cells to be stored or used for energy. With diabetes, your body either doesn’t make enough insulin or can’t effectively use its insulin.

Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

Predict whether a person will default on a loan or not.

Early prediction of Mortality Risk among Covid -19 Patients in early stages when patients gets admitted into the hospital.

ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/

Halo! Selamat datang di repository ku. Ini adalah model klasifikasi gagal jantung yang mempunyai akurasi sebesar 89% dengan algoritma Bagging! -Final Project H8

Machine Learning models for helping BNP Paribas Cardif accelerate its claims process

Used different types of machine learning classifiers such as Passive Aggressive, Extra Trees, Dummy Classifier to detect the DDos attack and compared the accuracies of the classifiers to determine the best out of the three.

This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.

Before training a model or feed a model, first priority is on data,not in model. The more data is preprocessed and engineered the more model will learn. Feature selectio one of the methods processing data before feeding the model. Various feature selection techniques is shown here.

Predicting sales of Walmart stores by cleaning the data, processing it. Then creating different models to predict the sales.

This project is about statistically analyzing risk factors for heart disease and performing A/B testing, descriptive and inferential statistics to provide health care plans and strategies to better understand the risk factors assocaited with heart disease and give key insights into what factors contribute most heavily and least heavily to the de…

Autoencoder & Variational Autoencoder for data augmentation and checking data authenticity with ML models.

This repository contains all the Machine learning [RTA] project | implimentation part done by The Bright Kid

In this project, we design a robust activity recognition system based on a smartphone.