The most advanced and powerful native open-source data factory for Salesforce.

PyVista example data - These are used for examples in both PyVista and PVGeo

Classification Analysis with supervised algorithms KNN, RandomForest and SVM. Use of PCA and K-means to recognize the importance of features.

Splitting the advertising data (advertising.csv) into training and testing data sets, then choosing and training a classification machine learning algorithm; Getting the accuracy of the ML model; Using feature engineering skills to create new features and improve my ML model;

Selenium is the first thing that comes to mind when one is planning to automate the testing of web applications. Selenium Webdriver with Java, one needs to bring the different components together, to start coding.

The Bike Sharing Company wants to understand the independent variables on their past data to analyze and create a machine learning model to understand the demand of the bike and accordingly plan a business strategy.

Random data generator which saves you the pain of having to manually generate testing data for your test case with a single mouse click

Leverage data analytics to identify "Hot Leads" and sculpt personalized strategies for maximum conversion potential, propelling X Education to new heights of success.

Sample Data Files for the open source NASA Common Metadata Repository (CMR)

it generates random data based on xpath passed by user

some-test

From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually. Use R or Python to perform this task

Machine Learning Practice and Exercises Welcome to our repository dedicated to the practice and mastery of machine learning (ML) concepts and techniques. This repository serves as a comprehensive resource for learners and enthusiasts looking to enhance their ML skills through hands-on exercises and practical applications.

This repository consists of implementation of some of the advanced algorithms in classification of dataset as well as training and testing of personalized dataset and decreasing the error.

Simple Python CLI tool to create JSON files with dummy data to use for testing.

Classification with ensemble learning & resampling techniques.