Titanic Survival Prediction in Python - Machine Learning Project

In this video we build a model, which predicts titanic survivors with a decent accuracy.

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[timvielhauer]

The latest pandas version is not ignoring string values in the .corr function anymore. just add "numeric_only=True" and it will work again

[saya]

Great tutorial video! helped me to understand how pipeline in ML works, hope there will be more Kaggle competition walkthrough like this from you soon! :)

[benjamindeporte]

Nice "real life" example of the scikit pipeline. Helped me a lot, thanks.

[jaymney_]

This is a great video, I’ve been trying to find a good place that would show the code behind creating a basic ML pipeline, or show some beginner feature engineering and whatnot, but I haven’t found anything as straightforward as this. A lot of other people have a lot of fluff in their tutorials, but you just show it straight up, which I really appreciate. Do you have any recommendations for textbooks/articles for a beginner wanting to get into Machine Learning? I have a strong math/programming background, so that’s not an issue, I just need something that will comprehensively explain all the main components of making an ML project. Thanks in advance and keep up the good work!

[muratsahin]

I was pretty confused when I saw %100 accuracy lol, thanks for the explaining.

[cryptigo]

This is actually such a good idea. A lot of python program / resume ideas are boring. Thanks!

[shashvatsinghal]

This is the best video i have ever watch on datascience and ml till date

[jeremyheng]

Thank you for great tutorial! Do you have more Kaggle competition walkthrough?

[vivekthumu]

Thank u so much for providing this video helped me to understand a lot

[wasgeht]

Thank you... I have one question, why u pick this models ? On which KPI based you choice your models for any kinds of problems. That will be a very interesting for me

[paralogyX]

Good video, but: 1) What was a purpose of test set? You didn't use for your model estimation and you used cross-validation. 2) You shouldn't fit StandardScaler on Kaggle Test Set, but only transform on the same scaler you used for training data, because if features distributed a bit different, then scaling will be different and your model will get different numbers for exactly similar passenger. Would be nice if you pay attention to these details, because they are really important. But generally, video is nice and useful.

[littlevb-yt]

Your videos are awesome I like them too much that's great job. Love from India....

[jomp]

Man your video was awesome. Easy to follow and replicate, plus you explain the key insights for those of us who have only a little knowledge of data analysis. Thanks a lot!

[RivinduBRO]

thankyou very much for this tutorial cuz i was like mentally down as i got 0.75 accuracy at my first try and also there were many people with 1.0 accuracy. so i was thinking why i can't. but now i understood the thing. thankyou soo much for this lesson.

[AzureCz]

I'm curious, how do I know the accuracy percentage inside the notebook, comparing the prediction with the dataset that we have, and not just uploading to kaggle.

[pravachanpatra]

Can you make a tutorial on an AI that plays a game using the NEAT module in python and pygame???

[juanmariomorenochaparro]

Thanks, very interesntin video, new susbcribe.

[novagamings]

I am new in the field of data science in terms of experience. I have completed paid skill course from IBM though. In my first attempt of this project which is my first project i got an accuracy of 78%. Is it good enough and should i move on to next project or try to refine my model for better accuracy. Please suggest someone with experience

[aflahalabri]

I don't think there was a need for creating the AgeImputer class at least in the latest versions, probably using the SimpleImpute class directly is sufficient. But it's good learning tip on how to create a custom class.

[ChristianA.Bradna]

I am confused as to when I should use fit_transform and when I should use transform only. Previously, I understood that when you sing the former, you are calibrating, so to speak, to the estimator to a particular set of data, so that if you wanted to use that estimator subsequently and have it perform in the exact same way you should not refit it, but you should only use it with its transform method. In this video however you used fit transform every time and still got it to perform the same in every data set. Could you tell me a little bit about how that works?