This project is simply a symptom checker and i believe we tried to accomplish that successfully. This project can be scaled more and we are continuously working upon it.

This project focuses on predicting the likelihood of diabetes in individuals based on various health metrics using a Support Vector Machine.

Machine Learning Models trained on Scikit-learn datasets. This repository contains the code files and saved models trained on Toy datasets (Classification & Regression), and Real World dataset.

The goal of this study is to classify microalgae of different species such as Chlorella vulgaris FSP-E, Chlamydomonas reinhardtii, and Spirulina platensis, using machine learning (ML) and deep learning (DL) methods

Early Detection of Depression Using AI/ML Algorithms @ DataLogs Research Labs

This project explores the optimal combination of Bag-of-Words and TF-IDF vectorization with Naive Bayes and SVM for sentiment analysis. It evaluates performance using accuracy, precision, recall, and F1-score, addressing ethical concerns like data privacy and bias to improve sentiment classification in real-world applications.

Mobile Price Classification System with Java SpringBoot API

The objective is to ascertain the probability of an individual being susceptible to a severe heart problem based on some features.

An experimental machine learning project 🤖📖

Build classification models to predict whether the cancer type is Malignant or Benign.

Diabetes Health Indicator Dataset analysis with R

Explore a broad range of machine learning algorithms, including ML, RF, SVM, LR, NB, PCA, LogReg, DT, KMeans, SVMC, GD, HClust, DBSCAN, ICA, KNN, and more, within this repository. Gain practical insights and apply these diverse ML concepts effectively.

This project uses machine learning algorithms like SVM, Decision Tree, and Random Forest to detect lung cancer from patient data. The models are trained, evaluated, and compared to identify the most accurate method for early lung cancer detection, aiming to improve diagnosis and treatment outcomes.

Machine learning models for travel mode prediction, using logistic regression and SVM on real-world transportation data.

A repository contains more than 12 common statistical machine learning algorithm implementations. 常见机器学习算法原理与实现

This project implements machine learning models to detect fraudulent credit card transactions. It includes data preprocessing, feature encoding, class balancing, and evaluation of various models like Logistic Regression, Decision Trees, Random Forest, SVM, Naive Bayes, and XGBoost. The final results are compared through accuracy visualizations.

This project is focused on the automated classification of lung sounds using machine learning techniques, aimed at improving the diagnosis of pulmonary diseases such as asthma, pneumonia, and COPD.

Generación de Playlists Basadas en el Reconocimiento de Emociones