Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

Fast-API base StockSeer-API uses different machine learning alogs to forecast closing stock prices.

Heart Disease Prediction using KNeighborsClassifier and RandomForestClassifier

A modern GUI Based Face Recognition and Emotion Predictor using Machine Learning

This is a Smart Attendance System designed using a pre-trained model called Haar-Cascade Classifier, OpenCV and other various Dependencies to mark the attendance in a smarter way and saves the lecture time.

Predict the best classifier for the given data.

Document Classification using Python and scikit-learn and nltk

Learning with sklearn diabetes and iris flower dataset, single and multiple linear regression, classification with multi-layer perceptron, kneighbors and support vector machines.

Classification Methods in Machine Learning

Gender Classification based on height, weight and shoe size using different Machine Learning Algorithms

Esse pequeno projeto tem como objetivo fazer testes de acurácia com rede neural com apenas um neurônio sem classificadores e com 2 (dois) classificadores, sendo eles KNN e 1R.

A simple machine learning program that use Scikit to predict gender based on the given data

AttendIQ is a Face Recognition Attendance System designed to automate and streamline the attendance process with precision and ease. By leveraging real-time face detection and recognition technology, AttendIQ eliminates the need for manual roll calls or ID-based check-ins. The system captures facial data during a quick registration process .

Repositório para meus estudos de Machine Learning seguindo alguns cursos e livros da internet.

KNeighborsClassifier for audio files

Project for AI. Model to check password security with five classes: "Too weak", "Weak", "Moderate", "Strong", "Very strong". Developed in 2024 with @sasyxk

🌸 Compare multiple classification models on the Iris dataset to evaluate accuracy, precision, recall, and F1-score with clear visualizations.

The objective of this DLM (Deep Learning Model) is to recognize the emotions from speech.

This example application predicts the iris flowers classification based on the IRIS data set.