A package for MD, Docking and Machine learning drug discovery pipeline
-
Updated
Aug 30, 2020 - Python
A package for MD, Docking and Machine learning drug discovery pipeline
Classification is the process of predicting the class of given data points. Classes are sometimes called as targets/ labels or categories.
Map of Artificial Intelligence: Classifications, Approaches, Algorithms, Libraries, Tools, State of Art Studies, Awesome Repos, etc..
This repository contains two classification projects: credit card default prediction and mobile price classification along with streamlit deployment .
This project is Master thesis research conducted at ENEA Portici Research Center, Italy. The data is obtained from the HPC CRESCO6 cluster at ENEA Portici Research Center. The aim is to identify energy consuming areas within the data center. In this project, real-time dataset from ENEA Portici Research Center is used. There are several technique…
Review Classification using NLP
Supervised Machine Learning
Decision Tree Classification was explored on Breast Cancer Data.
his project involves the classification of ECG (Electrocardiogram) readings to determine whether they are normal or abnormal. The dataset consists of rows, each representing a complete ECG of a patient with 140 data points (readings).
Framework to build, evaluate, select, and compare ML classification and regression models using high-dimensional biological data and other covariates
Notebooks for Machine Learning Classification
Logistic Regression Implementations - ML, Shallow NN and Enhanced Deep Neural Network for Structured and Unstructured Data Classification
Project aim is to use machine learning for predicting the winner of Pokémon among player choices. Potential audiences are the general public interested in playing the game and company management.
🚀 Revolutionize customer targeting with a predictive ML model that optimizes insurance subscription. 🎯📊
2023년 11월 대한산업공학회(UNIST) : 다중 역할 경험을 고려한 게임 유저 이탈 예측: 롤 게임을 중심으로, 1저자
🚑 Get Instant Result from your Test Reports analyzed over a huge data-set using machine learning classification
This project focuses on utilising machine learning techniques to predict the effectiveness of bank marketing campaign. Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Machine, XGBoost, K Nearest Neighbor, Naive Bayes, Support Vector Machine, and Artificial Neaural Networks algorithms are used to build a model for prediction.
Add a description, image, and links to the machine-learning-classification topic page so that developers can more easily learn about it.
To associate your repository with the machine-learning-classification topic, visit your repo's landing page and select "manage topics."