121 skills found · Page 4 of 5
abythomas / Improved K Means Clustering AlgorithmAn improved k-means clustering algorithm with improved centroid selection and clustering functions
SamanKhamesian / Simple Kmeans Clustering AlgorithmThis project is an implementation of a simple K-means algorithm
Gaurav-2001 / Machine Learning And Cyber SecurityUsing Kmean cluster algorithm to stop the DOS attack and making the webserver Secure
ciniro / A Hybrid Algorithm For Multiobjective OptimizationA hybrid algorithm for multiobjective optimization based on Diferenttial Evolution, Kmeans and NSGA II developed by me and presented on XIII Brazilian Congress on Computational Intelligence.
Amirbeek / TintTroveTintTrove is a simple and elegant image color picker tool that extracts dominant colors from uploaded images. The project is powered by a KMeans clustering algorithm and deployed on Heroku.
pooja1909 / Bigdata ProjectPerformed data cleaning, data analysis on Kaggle Crime dataset and implemented various machine learning algorithms like Kmeans, Knn techniques using Python. Developed map based visualizations to depict top five crimes in a particular area using d3.js and leaflet.js
ipang-dwi / Spk KmeansSPK mempergunakan clustering dengan metode KMeans - www.firstplato.com
angeloskath / Py KmeansA flexible python implementation of the simple kmeans clustering algorithm
MNoorFawi / Text Kmeans Clustering With Pythonsimple text clustering using kmeans algorithm
skandavivek / Geospatial ClusteringEvaluating clustering algorithms KMeans, DBSCAN, Hierarchical Agglomerative performance on geospatial data
ryokugyu / One Pass KMeans AlgorithmsImplementation of An Efficient Clustering Method for k-Anonymization in Python 2.7
MNoorFawi / Clustering With RExploring Hierarchical and Kmeans Clustering algorithms in R trying to segment wholesale customers ...
orico / KernelAlgorithmsA comparison of non vs kernel algorithms, such as KDE, PCA, SVM, Kmeans
adrian-haldenby / KmeansplusplusKmeans++ algorithm in Julia
ctaylor389 / K Means Yinyang GpuYinyang kmeans algorithm implemented in CUDA
Wenping-Du / Machine LearningMy implementation of SVM, AdaBoost and PCA & Kmeans Algorithms
ki-ljl / ClusterPython implements three clustering algorithms: kmeans, dbscan and agnes.
Parsa33033 / Kmeans ClusteringClustering point on Graph using Kmeans Algorithm(GUI with javafx)
zakariamejdoul / Customer Geolocation Data ClusteringWe use our customer geolocation data to perform a clustering algorithm to get several clusters in which the member data of each cluster are closest to each other using KMeans and Constrained-KMeans Algorithms.
deypadma / Prediction Of Stroke EventsStroke is the second leading cause of death worldwide and remains an important health burden both for individuals and for the national healthcare systems. Potentially modifiable risk factors for stroke include hypertension, cardiac disease, diabetes, dysregulation of glucose metabolism, atrial fibrillation, and lifestyle factors. Therefore, the goal of our project is to apply principles of machine learning over large existing data sets to effectively predict stroke based on potentially modifiable risk factors. Then it intended to develop the application to provide a personalized warning based on each user’s level of stroke risk and a lifestyle correction message about the stroke risk factors. In this article, we discuss the symptoms and causes of a stroke and also a machine learning model that predicts the likelihood of a patient having a stroke based on age, BMI, and glucose level for a group of patients. To proceed with the implementation, different datasets were considered from Kaggle. Out of all the existing datasets, an appropriate dataset was collected for model building. After collecting the dataset, the next step lies in preparing the dataset to make the data clearer and easily understood by the machine. This step is called Data pre-processing. This step includes handling missing values, handling imbalanced data and performing label encoding that is specific to this particular dataset. Now that the data is pre-processed, it is ready for model building. For model building, pre-processed datasets along with machine learning algorithms are required. Logistic Regression, Decision Tree Classification algorithm, Random Forest Classification algorithm, K-Nearest Neighbour algorithm, Support Vector Classification, KMeans Clustering Classification and Naïve Bayes Classification algorithm are used. After building seven different models, they are compared using four accuracy metrics namely Accuracy Score, Precision Score, Recall Score, and F1 Score.
