If the p-value had been greater than 0.5 then the prediction would have been class 1 = female. The raw p-value output is 0.0776 and because the p-value is less than 0.5 the prediction is class 0 = male. The new person is age 33, from Nebraska, has an income of $50,000 and is a political moderate. The demo concludes by using the trained model to make a gender prediction for a new, previously unseen person. The model scores 86.00 percent accuracy (172 out of 200 correct) on the training data, and 77.50 percent accuracy (31 out of 40 correct) on the test data. Hope to see you next time.Figure 1: Logistic Regression in ActionĪfter training, the model is applied to the training data and the test data. And for Python tips, check out the Dev with Serdar series in the same channel. You can also find the Do More With R playlist on YouTube’s IDG Tech Talk channel - where you can subscribe so you never miss an episode. That’s it for this episode! Thanks, Serdar, for your Python tips and thank you for watching! For more R tips, head to the Do More With R page at bit-dot-l-y slash do more with R, all lowercase except for the R. There’s a separate Do More With R video on running Python within RStudio you can watch after this one. Or you can run Python the conventional way from a console – including an RStudio console. You can library directly in your R code with reticulate’s import() function, or source a Python script from R with py_run_file(). You can run Python code in your R script with reticulate’s py_run_string() function. You can add Python chunks to an R Markdown document. There are several ways to run Python code within RStudio. I’ll need to restart my R Session for this to take effect. We also need to set an R environment variable so reticulate knows where python is. reticulate was designed to help Python and R interoperate, and it allows for easy data transfer between the two. We need to install the reticulate package if it’s not already on our system, and then load the reticulate package. Serdar, any other must-have packages you’d recommend for an R user doing data analysis or other common work? So instead of install.packages() in our R console, we need to run pip install in a terminal window to install Python libraries. Step 4 is a familiar one: Install packages we want. Step 3 is to activate my virtual environment with the source command. Serdar, why should we use one virtual environment per project? Again, notice that I’m running that virtualenv command in a terminal window, and not the R console. ![]() ![]() I’ll open an R project in RStudio and then create my virtual environment. Next, step 2, is to create a Python virtual environment for an RStudio project. While I run pip install, Serdar, can you tell us why we need virtual environments? ![]() That requires Python’s pip install command, which I’ll run in a terminal window. RStudio says we need the virtualenv Python package. But we’ve got choices! Serdar, would you recommend downloading from or Anaconda? Another question I often run into for Python in general: Should I use Python version two-dot-X or three-dot-X? Step 1, not surprisingly, is to install Python. Instead, since we’ve got Serdar here to help, I’d like to go through a manual version: RStudio’s suggested workflow, step by step. But it can be hard to understand what’s going on here. R is running commands to install Python, install some Python packages, and create a virtual environment. If you run that you should see a response something like this. If you are working locally, the reticulate R package has an easy Python install command: install_miniconda(). He’s here to help answer questions we R users might have when installing and configuring Python for RStudio. ![]() It’s “special” because I’ve got a guest today: Serdar Yegulalp, InfoWorld’s Python expert and host of the InfoWorld Dev with Serdar video series. I’m Sharon Machlis at IDG Communications, here with a special episode of Do More With R: How to set up your system for Python.
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