Diretório com os algoritmos de pré-processamento e modelos para análise de dados espectrais da uva de mesa Cotton Candy.

cancer cell class prediction

ML based Smart Crop Recommendation System with Disease Identification, utilizing CNNs. It aids farmers in selecting crops, managing diseases, and boosts productivity by integrating weather and geolocation APIs.

Active Learning Method for Virtual Support Vector Machine with self-learning constraints

This repository contains machine learning algorithms implemented from scratch and using scikit-learn, covering classification, regression, and clustering. Each algorithm is well-documented, with clear code and explanations. To use K-Medoids, install sklearn_extra via pip install scikit-learn-extra. Contributions are welcome!

Efficient Brain Tumor Classificataion using Filter-Based Deep Feature Selection Methodology

Artificial Neural Networks university subject

This is a machine learning project that uses various machine learning algorithms to predict whether a patient is diabetic or not. Here various machine learning algorithms like SVM, RF Classifier, DT Classifier, KNN, LR , LRwith CV, NB Classifier, and XGB are used. For this work, a website is made with Python Streamlit library. Paper is ongoing.

Wrapper on top of libsvm-tools

Lung Cancer Prediction achieved using sklearn for the CSE422 course at BRAC University,

The Learning From Data - Assignment in my MSc Business Analytics course

Tuberculosis (TB) remains a significant global health concern, ranking among the top ten causes of mortality worldwide. Timely and accurate detection of TB is pivotal for effective management and containment of the disease. In this study, we developed a robust TB detection system utilizing state-of-the-art methodologies including image preprocessor

SVM module

ML Classification application built using Flask

Implemented EDA, feature engineering, cross-validation, and visualization to create and test three models (Logistic Regression, Random Forest Classifier, and Support Vector Machine), building model with the highest accuracy

This project can be really helpful in the detection of sources that spread fake news. If there is a slight chance that the news might be fake or it might not give out the right information to the audience, you can get to know about its authenticity through machine learning models which take the help of tools such as Naive Bayes, (SVMs) etc.

A curated list of Best Artificial Intelligence Resources

This project investigates the effects of image augmentation on the accuracy of various models and their loss types.

Sentiment analysis of customer reviews for various Amazon Alexa products.