Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

An implementation of the K Nearest Neighbors Algorithm from scratch in python (using the Iris dataset)

This repository is dedicated to the detection of faults in electric motors through machine learning techniques, specifically utilizing the K-Nearest Neighbors (KNN) algorithm. It contains datasets, Python scripts, and accompanying documentation necessary for implementing and understanding the fault detection process.

Predicting Credit Card Defaults This repository provides a step-by-step tutorial on predicting credit card defaults using machine learning algorithms in Python with scikit-learn. Learn to implement SVM, Random Forest, Decision Trees, k-Nearest Neighbors, and Artificial Neural Networks to forecast default payments for credit card clients.

Implementation in Python of the K-Nearest Neighbors algorithm for machine learning. Working with the Iris CSV.

A k-nearest neighbors algorithm is implemented in Python from scratch to perform a classification or regression analysis.

A parallel K-Nearest Neighbors algorithm implementation in python.

K-nearest neighbors algorithm implemented from scratch in Python, tested on iris dataset.

This project demonstrates the implementation of the k-Nearest Neighbors (k-NN) algorithm entirely from scratch in Python. This project does not use any machine learning imports nor basic libraries like math, numpy, or pandas.

K-Nearest-Neighbors-with-Python This repository contains projects related to KNN algorithm using Python. Introduction: K Nearest Neighbors - Classification K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970’s as a non-parametric technique. Knn uses follwoing as a distance function : knn 1)Euclidean distance is calculated as the square root of the sum of the squared differences between a new point (x) and an existing point (xi) across all input attributes j. EuclideanDistance(x, xi) = sqrt( sum( (xj – xij)^2 ) ) 2)Manhattan Distance: Calculate the distance between real vectors using the sum of their absolute difference. Also called City Block Distance. 3)Minkowski Distance: Generalization of Euclidean and Manhattan distance. Different disciplines in KNN 1) Instance-Based Learning: The raw training instances are used to make predictions. As such KNN is often referred to as instance-based learning or a case-based learning (where each training instance is a case from the problem domain). 2) Lazy Learning: No learning of the model is required and all of the work happens at the time a prediction is requested. As such, KNN is often referred to as a lazy learning algorithm. 3)Non-Parametric: KNN makes no assumptions about the functional form of the problem being solved. As such KNN is referred to as a non-parametric machine learning algorithm. How to Prepare Data for KNN 1) Rescale Data: KNN performs much better if all of the data has the same scale. Normalizing your data to the range [0, 1] is a good idea. It may also be a good idea to standardize your data if it has a Gaussian distribution. 2)Address Missing Data: Missing data will mean that the distance between samples can not be calculated. These samples could be excluded or the missing values could be imputed. 3)Lower Dimensionality: KNN is suited for lower dimensional data. You can try it on high dimensional data (hundreds or thousands of input variables) but be aware that it may not perform as well as other techniques. KNN can benefit from feature selection that reduces the dimensionality of the input feature space. Source: https://tinyurl.com/y8fh9fgn

K-Nearest Neighbors Classifier with Normalized Compression Distance (KNN_NCD) is a machine learning module implemented in Python. It provides a classifier that uses the Normalized Compression Distance (NCD) to measure similarity between instances. Supports major compression algorithms.

Implementation of classification algorithms: K-Nearest Neighbors and Centroid Classification method in Python (From scratch :p). Implemented algorithms are used to classify handwritten-characters and ATNT Face-Dataset.

k-nearest neighbors (KNN) classifier algorithm implementation in python.

Implementation of k-Nearest Neighbors Algorithm from Scratch in Python.

Fuzzy K-Nearest Neighbors (FkNN) Classification Algorithm Implemented in Python

K-Nearest Neighbor(KNN) Algorithm for Machine Learning implemented in Python

Solved a very Popular Machine learning problem Iris flower classification using K nearest Neighbor algorithm implementation in python.

This repository contains a simple implementation of the k-Nearest Neighbors (k-NN) algorithm in Python, a popular machine learning method for classification and regression tasks.

A implementation of KNN(k-Nearest Neighbors) Algorithm in Python, to classication of flowers using sepal length, sepal width, petal length and petal width.

A collection of machine learning algorithms implemented from scratch in Python to provide a deeper understanding of their inner workings. Includes K-Nearest Neighbors (KNN), Neural Networks, K-Means Clustering, and Support Vector Machines (SVM).

A content-based Book Recommendation System built using the K-Nearest Neighbors (KNN) algorithm. This project uses the Book-Crossings dataset to suggest books based on user preferences. Implemented in Python with exploratory data analysis, preprocessing, and recommendation logic.

This project is about the implementation of two learning algorithms namely, 1)Artificial neural networks (ANN) 2)K Nearest Neighbors on SGEMM GPU kernel performance Data Set.s. The ANN algorithms is performed with the help of Keras library and TensorFlow. The KNN algorithm is performed with the help of Sckitlearn library in Python language on Jupyter notebook.

DOS Detector ML-KNN is a powerful machine learning tool implemented in Python for detecting and mitigating Denial-of-Service (DOS) attacks. Leveraging the K-Nearest Neighbors (KNN) algorithm, this tool offers an intelligent and proactive approach to identifying various types of DOS attacks in real-time.

This python project explains on how to implement a supervised machine learning algorithm on the famous iris dataset. The different classifiers used in this project includes decision tree and k-nearest neighbors. For a complete explanation of this project, check out my video on this topic.

This repository includes python codes, pdf files and dataset in order to solve the binary classification problem using 3 different machine learning algorithms i.e. Neural Network (FFNN), Logistic Regression and K Nearest Neighbors (k-NN) which are implemented from scratch without using SciKit Learn Library. It also includes the project report describing the project goals, data set and results in great detail.

Support Vector Machine(SVM), Logistic Regression, K-Nearest Neighbors(KNN) Algorithms are used for this Project to check computational performance (training time), predictive performance (accuracy and F-measure. To check the performance of accuracy, f-measure and computational performance of 3 algorithms I performed Run stratified ten-fold cross-validation test, Friedman test and report the results, Average ranks as a whole display significant differences on the 0.05 alpha level and, I implemented Nemeyi test to calculate critical difference to determine which algorithms perform significantly different from each other. I implemented the code in python.

Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy! We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with Python NumPy with Python Using pandas Data Frames to solve complex tasks Use pandas to handle Excel Files Web scraping with python Connect Python to SQL Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations Machine Learning with SciKit Learn, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Natural Language Processing Neural Nets and Deep Learning Support Vector Machines and much, much more! Enroll in the course and become a data scientist today! Wat zijn de vereisten? Some programming experience Admin permissions to download files Wat leer ik in deze cursus? Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms Learn to use NumPy for Numerical Data Learn to use Pandas for Data Analysis Learn to use Matplotlib for Python Plotting Learn to use Seaborn for statistical plots Use Plotly for interactive dynamic visualizations Use SciKit-Learn for Machine Learning Tasks K-Means Clustering Logistic Regression Linear Regression Random Forest and Decision Trees Natural Language Processing and Spam Filters Neural Networks Support Vector Machines Wie is het doelpubliek? This course is meant for people with at least some programming experience

Analysis of medical cost and related factors in medical insurance industry Designed the algorithm and identified the important factors that affect healthcare costs through Python and R Preprocessed Dataset by data cleaning, classification, standardization and feature transformation. Trained supervised machine learning models including logistic regression, random forest, k-nearest neighbors, and XGBoost, and applied regularization with optimal parameters to overcome overfitting Implemented logistic regression and decision tree model to judge the impact of different living habits on medical expenses Optimized model by GridSearchCV, achieved the accuracy of 0.986 and the AUC score of 0.963

K-Nearest Neighbors Algorithm Implementation in Python

I will implement some classical machine learning algorithms using raw python in this tutorial, including decision tree (ID3, C4.5, CART), gradient boosting decision tree (GBDT), support vector machine, logistic regression, navie bayes, k nearest neighbors, expectation maximation and adaboost.