scatter | IoC container and out-of-the-box extensibility | Dependency Injection library

My problem is that the intercept for my second regression is wrong, in fact I do not even get an intercept when looking at the summary, unlike with the first regression.

That is because your second model specifies no intercept, since you use ... ~ 0 + ...

Also, your first model doesn't make sense because it includes spending twice. The second entry for spending will be ignored by lm

There is no one such resource from AWS that provides the comprehensive view of how to use SageMaker SDK Estimator to train and save models.

I put a diagram and brief explanation to get the overview on how SageMaker Estimator runs a training.

1. SageMaker sets up a docker container for a training job where:

   • Environment variables are set as in SageMaker Docker Container. Environment Variables.
   • Training data is setup under /opt/ml/input/data.
   • Training script codes are setup under /opt/ml/code.
   • /opt/ml/model and /opt/ml/output directories are setup to store training outputs.

This may not be what you want but you can do this by adding a runtime of shiny in your yaml

Found the problem.
I was installing pandas_profiling, and this package updated pyyaml to version 6.0 which is not compatible with the current way Google Colab imports packages.
So just reverting back to pyyaml version 5.4.1 solved the problem.

For more information check versions of pyyaml here.
See this issue and formal answers in GitHub

##################################################################
For reverting back to pyyaml version 5.4.1 in your code, add the next line at the end of your packages installations:

I adapted an example from the plotly community to your example and created the code. The point of creation is to create the data for each subplot and then switch between them by means of buttons. The sample data is created using representative companies of US stocks. one issue is that the title is set but not displayed. We are currently investigating this issue.

You'll need to use Cost-Explorer API for that.

This was a good opportunity to check out the new geomtextpath, which looks really cool. It's got a bunch of geoms to place text along different types of paths, so you can project your labels onto the lines.

However, I couldn't figure out a good way to set the hjust parameter the way you wanted: the text is aligned based on the range of the plot rather than the path the text sits along. In this case, the default hjust = 0.5 means the labels are at x = 0.5 (because the x-range is 0 to 1; different range would have a different position). You can make some adjustments but I pretty quickly had labels leaving the range of the plot. If being in or around the middle is okay, then this is an option that looks pretty nice.

You can use a 2D FFT to find the general direction in which the crop is aligned (as proposed by mozway in the comments). The idea is that the general direction can be easily extracted from centred beaming rays appearing in the magnitude spectrum when the input contains many lines in the same direction. You can find more information about how it works in this previous post. It works directly with the input image, but it is better to apply the Gaussian + Canny filters.

Here is the interesting part of the magnitude spectrum of the filtered gray image:

The main beaming ray can be easily seen. You can extract its angle by iterating over many lines with an increasing angle and sum the magnitude values on each line as in the following figure:

Here is the magnitude sum of each line plotted against the angle (in radian) of the line:

Based on that, you just need to find the angle that maximize the computed sum.

Here is the resulting code:

using OneHotEncoder is not the way to go here, it's better to extract the features from the column time as separate features like year, month, day, hour, minutes etc... and give these columns as input to your model.