There are 14 repositories under uci-machine-learning topic.
This repository contains the collection of UCI (real-life) datasets and Synthetic (artificial) datasets (with cluster labels and MATLAB files) ready to use with clustering algorithms.
Simple API for UCI Machine Learning Dataset Repository (search, download, analyze)
We currently maintain 488 data sets as a service to the machine learning community. You may view all data sets through our searchable interface. For a general overview of the Repository, please visit our About page. For information about citing data sets in publications, please read our citation policy. If you wish to donate a data set, please consult our donation policy. For any other questions, feel free to contact the Repository librarians.
Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring
Heart Disease Analysis repository
Diabetes predictions application with gui
Classifying malignant and benign tumors using Neural Networks 🔬
The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit.
R data package containing data sets on UCI's ML repo
𝗙𝗶𝗿𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻🔥using Machine Learning Algorithm with python🐍, GoogleColab & database taken from 𝗨𝗖𝗜 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆
Machine Learning Algorithms Python [In Active Development]
An SVM model for multi-class classification of Thyroid data.
Computer Intelligence subject final project at UPC.
Clustering similar tweets using K-means clustering algorithm and Jaccard distance metric
Machine learning project on Distinguish between the presence and absence of cardiac arrhythmia and its classification in one of the 16 groups.
Marketing refers to activities undertaken by a company to promote the buying or selling of a product or service. Marketing includes advertising, selling, and delivering products to consumers or other businesses. Our data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls.
Applying Machine Learning techniques to identify randomly distorted capital letters in the English alphabet.
Basic Support for Final Projects in UCI CS 273A: Machine Learning
This project was part of the MIS6v99 Applied Machine Learning course that I took in Spring 2019. The goal of this project is to help the auditors by building a classification model that can predict the fraudulent firm on the basis the present and historical risk factors. The dataset is collected from the Auditor Office of India to build a predictor for classifying suspicious firms and is publicly available on UCI's Machine Learning Repository.
:bar_chart: UCI Machine Learning Repository in R
implementation of spam classification using python
UCI Thyroid Classification - Python, Keras, scikit-learn, ANN
Prediction of Benign or Malignant Cancer Tumors
A case study on using Logistic Regression. The dataset is from UCI machine learning repository. This ML algorithm is optimized by using K-fold and grid search and comparison is shown in notebook
Comparison of clustering methods for determining the operational states of a wastewater treatment plant (BSc project in Statistics) :wrench: :potable_water: :arrows_counterclockwise: :recycle: :sweat_drops:
repository for easy generation of tabulated dataset from the MIMIC-III database
Course Project for CS273A: Machine Learning at UCI
Smart Encryption of data based on best choices using Pandas and PySpark
Customer Segementation is used in marketing to better understand customers of a business and target them accordingly. Segmentation of customer can take many forms, based on demographic, geographic, interest, behavior or a combination of these characteristics. Segmentation for this analysis was conducted based on their purchase behavior, the features to be analyzed were Recency, Frequency and Monetary Value, (RFM) for short.
Predict the type of arrhythmia based on Electro-cardiogram (ECG) tool using machine learning models and algorithms.