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

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.

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.

The goal of this project was used advanced preprocessing and feature engineering. Achieved high accuracy with XGBoost and LightGBM. Deployed via a Django web application and visualization was presented using Dash and Plotly.

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

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

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

KNeighborsClassifier for audio files

A Machine Learning (ML) model that every ML beginner develops. This model is used to predict type of Iris flower based on the input.

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

ETL workflow and data analysis. ETL-workflow using prefect and pygrametl (SCD, slow changing dimension). Product classification based on product name.

This module is limited to making use of the classifier KNeighborsClassifier from sklearn to apply it to the recognition of handwritten digits in the MNIST file, with a hit rate about 98,65% in test and extraordinary simplicity, especially compared to the complexity of classifiers based on neural networks. It also shows full sensitivity.

Python application using artificial intelligence using LaberEnconder, KNeighborsClassifier and RandomForestClassifier

Fruits Classification.