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The Ruby DataMining Gem, is a little collection of several Data-Mining-Algorithms
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
A machine-learning project to determine if a certain mushroom is edible or poisonous.
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
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"
Detection (Prediction) of the possibility of a stroke in a person
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.
Webapp para classificar comentários (positivos, negativos e neutros) advindos do Facebook usando Natural Language Toolkit (NLTK) + Django e Bootstrap na interface Web.
Implementing an Image classification neural network to classify Street House View Numbers
My learning outcomes and followup of a well instructed Coursera guided project by Ari Anastassiou.
A python script that classifies iris flower species based on their various dimensions.
CSE 575 Statistical Machine Learning
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.
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.
In this project we predict credit card defaults using classification models.
Build and evaluate various machine learning classification models using Python.
Here we detect diabetes based on some attribute values related to body
In this repository, I worked on Mental Health in Tech Survey which includes: Data Cleaning, Data Analysis, Machine Learning, Natural Language Processing and Word Cloud.
The fraud identification models were build using Python Scikit-learn machine-learning module.
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.
Who is a Liver Patient?
K-NEAREST NEIGHBOR and HyperParameter Optimization using GridSearch.
Personalized Medicine: Redefining Cancer Treatment
These Codes are written as part of Neural Networks and Deep learning course at UCLA.
Deep learning based recommender system made up of CNN for feature extraction and k-nearest-neighbors for recommendation
A Supervised machine learning classifier using K Nearest Neighbour (KNN) and Logistic Regression.
Estimation of vital status of patients with ovarian cancer using Machine Learning models (K-Nearest-Neighbors, Support Vector Machine, Logistic Regression, Random Forest and XGBoost)
This is a machine learning project that uses various machine learning alogorithms to predict whether a patient is suffering from heart disease or not. Here I am using variour machine learning algorithms like Random Forest classifier, XGBClassifier, GaussianNB, Decision Tree Classifier, K-Nearest Neighbours and Logistic Regression.
This project aims to build a convolutional neural network (CNN) model that can classify handwritten digits from the MNIST dataset.
This is a Classification problem in which we need to classify whether the Loan will be approved or not.
You just finished a book! Now what? Using KNN and NLP, I can give you the next five books on your reading list based on the one you just finished.
Predictive problems requires three main challenges to overcome. First, choosing the right classification algorithm. Second, building a robust building and testing environment for algorithm to learn and thirdly, picking the appropriate performance metric for evaluation. Here it is explained how these challenges can be addressed.
Explore hands-on machine learning projects, resources, and collaborative opportunities in this GitHub repository.