The easystats performance R package makes it easy to investigate the relevant assumptions for regression models. Simply use the check_model() function to produce a visualization that combines 6 tests for model performance.
Both R and Python are great. We’ll showcase some of the strengths of each language in this article by showcasing where the major development efforts are within each ecosystem.
Scikit Learn is a powerful package for making machine learning models. In this Python Tip, we cover how to make your first Linear Regression Model that adds a trendline to a plot.
A gentle introduction to our forecasting package, Modeltime. Modeltime extends the Tidymodels ecosystem for time series forecasting. Learn how to forecast with ARIMA, Prophet, and linear regression time series models.
The plotnine library is a powerful python visualization library based on R's ggplot2 package. In this tutorial, we show you how to make a great-looking correlation plot.
I'm super excited to introduce the new parallel processing functionality in modeltime. It's perfect for speeding up hyperparameter tuning of forecast models using parallel processing.
The siuba python library brings the power of R's dplyr and the tidyverse to Python. Gain access to functions like group_by(), mutate(), summarize(), and more!
Pandas Profiling is an awesome python package for Exploratory Data Analysis (EDA). It extends pandas for statistical summaries including correlations, missing values, distributions, and descriptive statistics. It's great for understanding Data Quality too!
Python for Data Science Automation is an innovative course designed to teach data analysts how to convert business processes to python-based data science automations.