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the challenge : write a KNN Algorithm that 1.Must be able to accept both numeric and categorical features. 2.Must at least perform classification, regression is optional. 3-Use Gower distance (Minkowski’s for continuous and Jaccard for categorical 4.Use Titanic data to predict survival and IRIS to predict type
Exploring Recommender Systems using various Machine Learning Models like scikit-learn, Surprise, NLP and collaborative filtering using KNN and Tensorflow.
The task is to build a machine learning regression model will predict the number of absent hours. As Employee absenteeism is a major problem faced by every employer which eventually lead to the backlogs, piling of the work, delay in deploying the project and can have a major effect on company finances. The aim of this project is to find an issue…
Problems Identification: This project involves the implementation of efficient and effective KNN classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.
The goal of this report is: -predicting a transformation of the player `Position` variable, -predicting player's market `Value` by predictive machine learning modeling in R with "tidymodels" package.