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

The goal of this project is to develop a machine learning model that can help banks to identify customers who are likely to churn and take appropriate measures to retain them

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…

Dance Forms Identification: A Deep Learning Classification Problem.

Multiple Object Tracking in video Using Deep learning

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

The project is an integral cog in Computer Vision and Artificial Intelligence and Machine learning. It aims to determine the activity of a human from a video provided to the machine. It is a step forward in solving various problems like surveillance, fall detection for elderly or sick people, robotics and computer interaction, security among man…

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.

learning python day 6

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

Prediction Analysis on TSR using CNN with 99% accuracy

Supervised Machine Learning project with KNN, decision tree, random forest and adaboost algorithms

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

Breast Cancer Detection using Machine Learning

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.