Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! If you want to watch a full course on Machine Learning check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at ryannolandata@gmail.com *Both Datacamp and Stratascratch are affiliate links.

this needs more views. was so in depth and perfect for a beginner!

Hope you enjoyed this video, it took so long to produce. If you enjoyed it, please subscribe to the channel. I just uploaded the 2nd part of this video where I improve the model (linked down below) Below are a few links that you should check out: Part 2: kzfaq.info/get/bejne/geB7ZNOay8eWhp8.html&ab_channel=RyanNolanData Kaggle Code: www.kaggle.com/code/ryannolan1/titanic-wip-9-12 Twitter: twitter.com/RyanNolanData LinkedIn: www.linkedin.com/in/ryan-p-nolan/ SciKit-Learn Tutorials: kzfaq.info/get/bejne/idB_mcWTy6-WoY0.html&ab_channel=RyanNolanData Practice SQL & Python Interview Questions: stratascratch.com/?via=ryan

I loved the walkthrough, honestly the last about 35 mins I had no idea what was going on but it's really cool that people like you are giving free tutorials on such complex work. Thanks!

So helpful !! Ideal demonstration for my first projects, going forwards

Thank you so much for the video!!

Thank you for this, your videos are so helpful. Keep it up!

It was a super useful video and I am happy to have done my first Data Science project. Thank you very much.

Excellent video. So much in it, thought process, code tips etc.

at least you explain in detail what you are typing for after copy your line of code. Nice video btw

yes man, much appriciated for your efforts

Extremely interesting tutorial, learnt a lot of new functions in pandas and different ways to analyze the date. Thanks Ryan !!😀

I got 78% result using forest. Thanks for the brilliant explanation!

Very cool video! Would love to see some of this type of content.

Thanks... I really enjoyed and you explain so well.... Bless you.

@Ryan Nolan Data: Excellent vdo.

keep up your good work

Amazing content!

Hi, great video. One idea - instead of writing out so many loc statements, it might be easier to just use labels=False when using qcut.

It really helped a lot, thank you, keep going

Hi Ryan, I am new to data science. I am a bit lost on what the point of analyzing the ticket number and passenger name. What is the goal of doing that? Same with qcuts, are we doing them to help with a decision tree model? Do we need to do any of this if we just build a regression model?

Thank you so much. Keep it up:)

Awesomely awesome...i had to sub

you explained it in fantastic way just one request will you please provide the valid link for notebook actually its not working

Hi @Ryan Thanks for making this amazing video. I just want to understand why did use "Plus one for yourself" @25:05? Thank you!

Just a suggestion your next video should be on using chatgbt for this project

great tutorial, can't wait to check out part 2! question on correlation map; how did you use it to narrow down your parameters/features?

1:17:00 i don't understand the usage of .transform('count'). Can someone explain with an example?

The length of the name is dominated by married ladies who have their married name AND their maiden names in brackets. Here is the top few: 'Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)', 'Phillips, Miss. Kate Florence ("Mrs Kate Louise Phillips Marshall")', 'Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan")', 'Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)', 'Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)' So I think when noting that survival is related to name length you are actually picking up that name length is a predictor of being female who of course have a higher chance of surviving. Analysing this dataset is addictive - I must give it up!

What's the point in splitting the dataset into train and validation if then at the end you are using only the training to do the grid search with cross validation? doesn't the grid search directly create the validation set on the training set you give it?

can you make more project every month? i want learn about project more.

Why on 40:57 my output of age is 0 every row

This was really helpful 🥳🥳🥳🥳

hello! thank you for your video, i am trying to follow you and repeat all the steps. i have found a better way to assign labels for age groups : df['Age_Lebel'] = pd.qcut( df['Age'], 8, labels = np.arange(8) + 1 ) hope it can be helpful!

I do not think you meant to make the young French girls ‘noble’ which you did. I am just starting to learn pandas with your help but some of the complicated string editing you did would have been so much simpler and more understandable if done in an old fashioned ‘for loop’. I know it is frowned upon by ‘experts’ but the whole point of Python is that the code is readable.

can we add this project to our resume ?

Am I the only one that can't see anything he types or clicks in the beginning of "Starting the Project" (from 9:00 for approx a minute)? :/

nice video tutorial chief! What kind of extension do you use ?

Great work brother! I have subscribed you and waiting for next Kaggle projects also

on 48:37 there is a shorter way of extracting Title: train_df['Title'] = train_df['Name'].str.extract(' ([A-Za-z]+)\.', expand=False)