Prepare and refine your dataset effectively by mastering column selection, label encoding, and feature scaling. Gain practical insights into solving common Pandas data transformation issues to ensure seamless model compatibility.
Key Insights
- Selecting and preparing consistent columns such as
pclass,embarked,sex,fare, as well as columns representing family relations, ensures uniformity across training and testing datasets for successful model predictions. - Applying label encoding correctly using the
.locmethod in Pandas eliminates 'copy of a slice' warnings and ensures accurate transformation of categorical features likeembarkedandsex. - Scaling numerical features such as
ageandfareusing StandardScaler'sfit_transformmethod helps standardize data distributions, enabling efficient performance and accurate predictions in models ready for Kaggle submissions.
Note: These materials offer prospective students a preview of how our classes are structured. Students enrolled in this course will receive access to the full set of materials, including video lectures, project-based assignments, and instructor feedback.
Let's label, encode, and scale our columns. So, let's first pick out which columns we want in this actual final-ish version of xtest. We want the columns pclass, embarked, sex, fair.
I want to make sure I'm consistent with the other one because we're going to be running in through the same model. Siblings and spouses and parents and children, let me make sure I haven't missed one. If you have a different value, yep, got it.
If you have different values, different columns, then the model won't be able to work with it the same way. All right, now we're going to set xtest embarked to equal the le.fit transform version of xtest embarked. Let's run this.
I want to make sure I'm doing this right. Yeah, this is an interesting panda's problem where we're trying to work on a copy of a slice of a data frame. More properly, we should be using .loc here, and it's not hard to do.
We just say all rows in the column embarked. Okay, and we'll do this. Let me make sure I've got that right.
Yep, no warning this time. And let's do the same thing for sex. Those are two things that needed label encoding.
And age and fare are ones that need scaling. And so we'll use s standard scalers, fit transform, and fit transform those same values. This way we can do it all in one nice line.
And there we go. Yep, that's all good. And let's just check our xtest because we just wrote a lot of code.
Did it work? No. I'm afraid I've made some kind of dreadful error. I remember the .loc. When I make an error like this, this is pretty common for me to do.
An invalid key. Let's check this out. Let's see if we can figure out what I did wrong here.
Oh, I missed the .loc here. I was saying I got all the .locs, but nope, I missed this one here. Yep.
All right. So I still have an issue. Age not an index.
Oh, yep, I just straight up forgot to include age up here. All right. I'm going to reset things by running every code block before.
Make sure that I'm running this from the start. And let's try this again. There we go.
Some intricate code, but we got there eventually. I missed putting age in to begin with, and I missed a method call here, but we got it. All right.
Let's take a look at this. Age and fare appear to be scaled. Embarked and sex appear to be label encoded.
I think in the next step, we're ready to get everything set to submit to Kaggle.