Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.

Performing comparative sentiment analysis to determine public reaction on newly introduced Farm Laws of 2020, India by collecting data using Twitter Tweepy API

4 machine learning models applied to 2 seperate binary classification problems each. These models include decision trees, neural networks, random forest bagging, and k-nearest neighbor.

Using Collaborative Filtering predicting Movie Rating and K-nearest Neighbours & SVM algorithms for Number ClassificationNumber Classification

The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems.

k-Nearest Neighbors (KNN) used for an Etherium Blockchain classification problem

A Python project that categorizes Spotify tracks into four moods based on their respective features.

Classify car safety as safe or unsafe Using k-nearest neighbors classifier on Car Evaluation Data Set

Simpsons Members Recognizer Supervised Machine Learning Algorithm.

This project was developed for the CSC-481: Artificial Intelligence class at Southern Connecticut State University. The purpose of this assignment was to use the K-Nearest Neighbor classifier, as well as Decision Tree classifier, to create AI models that could identify the gender of an individual from the provided face dataset.

Unsupervised Learning Algorithms being implemented to detect a liar.

This repository contains a Python implementation of a K-Nearest Neighbors (KNN) classifier from scratch. It's applied to the "BankNote_Authentication" dataset, which consists of four features (variance, skew, curtosis, and entropy) and a class attribute indicating whether a banknote is real or forged.

Aplicação web onde você consegue treinar um modelo de Machine Learning para classificar uma pessoa como do sexo masculino ou feminino com base em seu nome.

Raw Coding Implementation Of Different Sorts Of Machine Learning Algorithms Without Using Library

Differentiates between single and binary star systems using photometric constraints and a k-NN classification algorithm.

This model predicts the class of the flower from the input data of sepal length, sepal width, petal length and petal width. The data used for training this model is from the iris dataset present in 'sklearn' package in Python. The classification model used is K- Nearest Neighbors where number of neighbors or 'n_neighbors' is 3.

Emotion-based music player created for data science capstone project

k-Nearest Neighbors Algorithm with p-adic Distance

user-drawn digit recognition program