Titanic Survival Prediction in Python - Machine Learning Project 

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

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! :)

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

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

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!

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

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

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

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!

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.

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

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

Great Video, thank you!

Sir my updated sklearn version doesn't have fit_transform.. Please guide what should I do!

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

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.

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

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.

Thanks, it is a great tutorial

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?

Awesome bro

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

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.

Is there a difference between hit encoding in pandas and sklearn? The process is so much easier with pandas, is there a particular reason why he used sklearn?

Why are you scaling the variables when using a tree-based model? Scaling is done to Normalize data so that priority is not given to a particular feature. Scaling is mostly important in algorithms that are distance based and require Euclidean Distance. Random Forest is a tree-based model and hence does not require feature scaling.

Is there a video in which you have a deep explanation of how to understand 'Class' __init__ and everything related to this methods?

sns.heatmap(titanic_data.corr(), cmap="YlGnBu") plt.show() This gives error: could not convert string to float: 'Braund, Mr. Owen Harris' shouldn't the titanic_data.corr() drop the string columns automatically?

Nice video.

Cn't import BaseEstimator, anyone can help?

Can we download your jupyter notebook from somewher?

i am having strat data error after that everywhere its an error anyone can explain why

Interesting where at 15:10 you said you don't want to look too much at your training set so you don't get biased. It seems everyone else I hear says to examine it as much as possible....is there something I'm misinterpreting from you or them?

I followed the code as said in the video and came across an error when we fit_transform with the strat_test_set. The error was that the 'Embarked' column was missing. I think it is because we drop it in featuredropper function, but in the pipeline as we process it all over again , I guess we get this error. Can you help me fix it asap???

Thanks, very interesntin video, new susbcribe.

I just used logistic regression and got 0.7655 taking only gender & Pclass. Thanks for your clarification about 100% accuracy though.

at 32:00, how is he calling stat_train_set in the pipeline.fit_transform function when the variable doesnt exist yet?

sparse matrix length is ambiguous; use getnnz() or shape[0] showing error message as shown above.(How to slove this) column_names = ["C", "S", "Q", "N"] ---> 13 for i in range(len(matrix.T)): 14 X[column_names[i]] = matrix.T[i]

A little bit fast(especially typing xD), but good tutorial; I got 79,42%, thanks!

Am i the only one Stuck at 32:31. i keep getting this error: AttributeError: 'FeatureEncoder' object has no attribute 'transform'

Thank you for you teach video, it is very good for noob

nice

Correlation doesn't work for string values hw u did it ? 🤔

11:45 You can't use Pearson correlation coefficient for nominal/ordinal data. 12:49 you need to create dummy variables for each class.

This is strange but, if you add the name length as a column it helps. The name length has 0.332350 correlation with the Survived column :)

Hello thanks for your this video , but strat_train_set = pipeline.fit(strat_train_Set) give attribute error that DataFrame object has no attribute "toarray"

I got this error ValueError: Input contains NaN after this line strat_train_set = pipeline.fit_transform(strat_train_set),I was following your tutorial.

had a problem here 42:05 I solved only selecting numeric: X_test_numeric = X_test.select_dtypes(include=[np.number])

I just submitted mine today and I got a score of 0.78229 but then I saw all those 1s and I was like "just how did they do that"😂

decent vid

Give the notebook

I see they probably cheating I lost confidence when I say some 100% while I only got 0.76 which I think is not bad

lol

Lol

Very interesting. But please translate your video in Russian

I'm actually tired of worrying about stocks. it's driving me nuts these days,I think crypto investment is far better than stock made over $39k in a week..