hse | HSE : Heterogeneous-memory storage engine | Database library

I am using STM32 cubeMX for configuration and Keil for programming. Have set onboard led pin pc13 pin as an output pin and default in push-pull mode. Set debugger to the serial wire as I am using ST-link V2 as a debugger. RCC set HSE to crystal/ceramic resonator. and clock configuration set to default and generated project.

Now I started with a simple LED blink program. As below

I am trying to learn React Hooks with a Bootstrap Modal example. I have the following Modal (from the react bootstrap website).

I'm trying to get the STM32F446 running at full speed, following this tutorial: https://www.youtube.com/watch?v=GJ_LFAlOlSk&t=826s i did everything he does, but the clock speed of my timers is DEADLY slow, like literally, when blinking an LED with pre-scalar of 9 and ARR of 20, it is easily visible by eye.. Wt* am i doing wrong ?

I wanted to show the confidence interval on the plot which I have made for the cubic spline of the data, but I have no idea how it should be done. From theory, I know that the CI should diverge from the fitted line when we get closer to the edges, but the only solution I came up with is this janky addition, which does not show the correct CI.

Here is the code:

I wanted to create a Logit plot for a natural cubic spline function with four degrees of freedom for P(wage > 250) but the error occurs for some reason. I don't understand why, because OLS works fine.

Here is the code (it should be fully working without any tweaks, except, of course, the part in question):

I am trying to make a pairplot using sns, but for some reason, it refuses to plot the first one. What may cause this issue?

Here is the fully working code:

I am trying to implement sklearn's lasso in my code. To test it out, I have decided to make a test with alpha = 0. This should by definition yield the same result as LinearRegression, but this is not the case.
Here is the code:

What I wanted to do is go over some OLS fits with different degrees of polynomials to see which degree performs better at predicting mpg given horsepower (while using LOOCV and KFold). I wrote the code, but I couldn't figure out how to apply the PolynomialFeatures function to every iteration using GridSearchCv, so I ended up writing this:

I have been trying to make a cubic regression for a few days now, but I encounter the same problem: my result does not coincide with the code I wrote in R to check. The databases are completely the same, so this is not the problem. The code that I have right now is something like this: