A Shiny R web application to estimate differences in diagnostic efficiency based on differences between single-condition cases.

Three skin diseases images classified with support vector machine. The dataset is collected from kaggle and some other sources. This code is used in a research work in one of the IEEE conferences.

Prediction of Heart Disease using machine learning models

Build a model which will predict whether an individual will hire an attorney or not.

TakenMind Global Internship Program is recognized under United Nations Sustainable Development and Growth (SDG) and is a highly recognized International Certification Program. - Reference Link to the United Nations SDG #26437 TakenMind Program. TakenMind (powered by United Nations SDG Program) is offering a Global Internship in Data Analytics an…

It is a machine learning project that predict smart watch price. Dataset is created manually from ecommerce site.

Voting Ensemble & Stock Prediction - Project Submission for Data Mining & Machine Learning Module

Evaluation of the performance of classification models can be facilitated through a combination of calculating certain types of performance metrics and generating model performance evaluation graphics. The purpose of this exercise is to calculate a suite of classification model performance metrics via Python code functions.

This project uses a few different classification algorithms to try and predict customer loan default status. The following algorithms are tested: (1) K-Nearest-Neighbors (2) Decision Trees (3) Support Vector Machines (4) Logistic Regression (5) Gradient Boosting Classifier

The folliwing ML project involves EDA analysis of Election Dataset, Data preparation for modelling, and prediction using ML models. Also Text Analysis on the inaugral corpora from nltk to analyse the most frequently used words in Presidents' Speeches.

Experience predictive healthcare with our Streamlit app. Utilizing Random Forest, our tool analyzes medical data to assess diabetes risk swiftly. Ideal for healthcare professionals and researchers, this user-friendly app simplifies risk evaluation. Join us in the fight against diabetes.

What is "accuracy"? The effect of changing the decision threshold on a model's accuracy.

Prediction Analysis on TSR using CNN with 99% accuracy

Get insights over your prediction's accuracy regardless of how it was obtained

ABCRaster stands for Accuracy assessment of Binary Classified Raster. It is a package for performing validation, accuracy assessment, or comparing binary classified rasters (.tiff) versus a reference (.shp). Primary use case is to compare flood maps encoded as (1,0) in tiff file format against a reference vector from CEMS.

Machine learning project

This is a Python notebook giving an overview and implementing the accuracy assessments in *Good practices for estimating area and assessing accuracy of land change* by Olfosson et al. (2014).

With the help of Logistic Regression, Support Vector Machine and K-Nearest Neighbors models you can predict a person's income that it will be below or above 50K.

Datasets for the Forvision project

XLM-Roberta is a transformer model that categorized bengali news headline into six different categories. This code is used in research work which is published in one of the IEEE conferences.