I'm super excited to introduce the new panel data forecasting functionality in modeltime. It's perfect for making many forecasts at once without for-loops.
easystats: Quickly investigate model performance
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.
R is for Research, Python is for Production
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.
sklearn: Make your first linear regression model in Python [Video]
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.
Gentle Introduction to Forecasting with Modeltime [Video Tutorial]
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.
plotnine: Make great-looking correlation plots in Python
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.
Hyperparameter Tuning Forecasts in Parallel with Modeltime
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.
grafify: Make 5 powerful ggplot2 graphs quickly with R
Siuba: Data wrangling with dplyr in Python
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: Make Exploratory Data Analysis Reports
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!
gghalves: Make Half Boxplot | Half Dotplot Visualizations with ggplot2
Course Launch: Python for Data Science Automation in 7 Days!
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.