There are 1 repository under machine-learning-classification topic.
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 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 techniques implemented including big data analytics and AI technology.
Build visual machine learning models with multidimensional general line coordinate visualizations by interactive classification and synthetic data generation tools.
This repository contains two classification projects: credit card default prediction and mobile price classification along with streamlit deployment .
Review Classification using NLP
This 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).
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.
Decision Tree Classification was explored on Breast Cancer Data.
Supervised Machine Learning
Framework to build, evaluate, select, and compare ML classification and regression models using high-dimensional biological data and other covariates
Identify the various sections (bridge, chorus, etc) of a song.
Machine Learning classifier comparison GUI application. Choose 21 classifiers, evaluation data (optional for evaluation of synthetic data), hyperparameters, cross-validation splits, and rng seed; tabulates, and visualizes in Parallel Coordinates: best, worst, average, and standard deviation of Accuracy/F1/Recall.
This repository contains a web application associated with a collection of a few classification algorithms using machine learning in Python to determine the sentiments behind internet memes based on image and text data extracted from 6,992 different internet memes, as part of the final project for the Introduction to Data Science (DS2001) course.
Machine learning model trained on 520K social media comments generating probability distribution of emojis based on textual context.
Logistic Regression Implementations - ML, Shallow NN and Enhanced Deep Neural Network for Structured and Unstructured Data Classification
🚀 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
Notebooks for Machine Learning Classification
:ambulance: Get Instant Result from your Test Reports analyzed over a huge data-set using machine learning classification
Cancerous Tumor Classifier based on RNA-Seq gene expressions dataset
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.
Exploratory data analysis and machine learning classification models to predict employee attrition.
This repository contains code designed to reproduce the results of my thesis by first generating a data set from query logs (Data), extracting features from that data set (FeatureExtraction), performing experiments based on those features (Experiments), and showing those results (Results).
Machine Learning Projects
In this Machine Learning Classification project we are going to predict who is going to survive the titanic crash and who isn't !!
This ML project predicts oil spills using various machine learning algorithms like XGBoost and Random Forest. This project also contains saving and load of the model to make predictions on a sample dataset.
ml regression analysis