The data complexity library, DCoL, is a machine learning software that implements all metrics to characterize the apparent complexity of classification problems. The code is implemented in C++ and can be run on multiple platforms.
banter is a package for creating hierarchical acoustic event classifiers out of multiple call type detectors.
Reviews can make or break a product; as a result, many companies take drastic measures to ensure that their product receives good reviews. When it comes to board games, reviews and word-of-mouth are everything. In this project, we will be using a linear regression model to predict the average review a board game will receive based on characteris…
BayesCOOP is a scalable Bayesian framework for supervised multimodal data integration. It combines the Bayesian bootstrap with a jittered group spike-and-slab Laplace prior to enable cooperative learning across heterogeneous data modalities, with principled uncertainty quantification.
Predict donors using supervised learning, ensemble methods and data science
Interactive visualizations for protein complexes from hu.MAP 1.0 and hu.MAP 2.0 by Super.Complex and CORUM
Finding out the donors who earn more than $50K annually using Machine Learning
Applied data analysis and supervised learning projects focused on regression modeling, statistical inference, and model validation.
Udacity Nanodegree - Machine Learning - Supervised Learning, Unsupervised Learning, and Reinforcement Learning
Finding doners for charityML
The goal of the project is to build a predictive model using machine learning concepts to predict customer attrition for a telecom service company.
Modelo para predecir polaridad en mensajes de Twitter durante el plebiscito en Colombia
This repo contains the projects I have built for Udacity's ML Nanodegree program that I'm currently pursuing
IST 707 Data Analytics Assignments
A machine learning web application that predicts an employee's salary based on demographic and professional attributes such as age, gender, education, job title, and years of experience.
Data collected for housing prices in the Boston, Massachusetts area to predict the selling price of a new home.
Implementing multiple Supervised Algorithms to predict the Donors.
First project Of machine learning Nanodegree (Supervised Learning)
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