A machine-learning project to determine if a certain mushroom is edible or poisonous.

Automatic method for the recognition of hand gestures for the categorization of vowels and numbers in Colombian sign language based on Neural Networks (Perceptrons), Support Vector Machine and K-Nearest Neighbor for classifier /// Método automático para el reconocimiento de gestos de mano para la categorización de vocales y números en lenguaje de señas colombiano basado en redes neuronales (perceptrones), soporte de máquina vectorial y K-vecino más cercano para clasificador

The Ruby DataMining Gem, is a little collection of several Data-Mining-Algorithms

An implementation of the K-Nearest Neighbors algorithm from scratch using the Python programming language.

Using Supervised Machine Learning Techniques for Chronic Kidney Disease Detection

Detection (Prediction) of the possibility of a stroke in a person

The Iris flower classification project uses the Iris dataset to demonstrate a simple machine-learning workflow. It covers data loading, exploration, preprocessing, model building, evaluation, and data visualization.

All solved lab of FAST NUCES Lahore campus _ 2022 Spring

Train SVM & KNN model for face recognition with the help of "The world's simplest facial recognition api for Python and the command line"

Build and evaluate various machine learning classification models using Python.

The aim of this study is to predict how likely individuals are to receive their H1N1 flu vaccine. We believe the prediction outputs (model and analysis) of this study will give public health professionals and policy makers, as an end user, a clear understanding of factors associated with low vaccination rates. This in turn, enables end users to systematically act on those features hindering people to get vaccinated.

Used 5 different supervised machine learning algorithms and trained them with real data of people with and without liver disease. Then evaluated the results of each of them using different parameters to choose the best one.

Implementing an Image classification neural network to classify Street House View Numbers

Webapp para classificar comentários (positivos, negativos e neutros) advindos do Facebook usando Natural Language Toolkit (NLTK) + Django e Bootstrap na interface Web.

Bank GoodCredit wants to predict cred score for current credit card customers. The cred score will denote a customer’s credit worthiness and help the bank in reducing credit default risk.

CSE 575 Statistical Machine Learning

Eigenfaces is an approach to facial recognition based on the overall appearance of a face, not on its particular details. By means of technique that can intercept and reshape the variance present in the image, the reshaped information is treated like the DNA of a face, thus allowing recovery of similar faces (because they have similar variances) in a host of facial images.

A python script that classifies iris flower species based on their various dimensions.

My learning outcomes and followup of a well instructed Coursera guided project by Ari Anastassiou.

Predict a Pulsar Star using Stochastic Gradient Descent, K nearest neighbors, Support Vector Machine and Decision Tree classifiers.

Personalized Medicine: Redefining Cancer Treatment

Here we detect diabetes based on some attribute values related to body

This repository contains the main.py file that performs different Classification algorithms on popular datasets like the Iris dataset, Breast Cancer dataset, and Wine dataset from UCI Machine Learning Repository and shows its results in a simple UI. Also, the visualization of the data is done using Matplotlib. The datasets are multidimensional, thus I have applied PCA first to reduce the dataset to two-dimension and then have plotted it.

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In this project we predict credit card defaults using classification models.

The fraud identification models were build using Python Scikit-learn machine-learning module.

A Supervised machine learning classifier using K Nearest Neighbour (KNN) and Logistic Regression.

This is GEM repo, as it has all the Hands-On ML notebooks

K-NEAREST NEIGHBOR and HyperParameter Optimization using GridSearch.

These Codes are written as part of Neural Networks and Deep learning course at UCLA.