Logistic Regression with Python | Titanic Data | Your First Kaggle Project |

Welcome to the world of machine learning. Learn to code with Python for Machine Learning and build a model to predict whether or not a passenger survived in titanic disaster. You can download the source code and file from the below link and can do some more feature engineering to improve precision and can submit this as your first Kaggle Project.
Link to download source file and code:
github.com/sumitprakashdubey/...
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Please feel free to comment with any questions or suggestions.
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[@EliasIoannou]

I have no idea how this video has only 7k views. You explain things so clearly and you throw in useful commands that make life easier. Mad respect brother.

[@getchertified]

Thank you for being sooooo clear with your explanation! You deserve more subscribers. I just subscribed!

[@satyajeetrai6529]

Honestly one of the best project explanation for beginners on KZbin

[@pronoybiswas3810]

I knew all the things. I know how to find but I was very confused about what to do what to find with Logistic Regression. Now this video gives me a hope that I can do any kind of Logistic Regression with ease

[@abdulrehman5128]

looking for ur upcoming videos..U are doing a great work..

[@call-me-potato.]

Great work , many thanks. you explained with clearity. i am surprised to see your video only got 700 views & its one of best video of data science.

[@atanunayak6637]

Amazing beautifully explained each and everything, perfect ✨

[@aldux]

Great video my friend. You explain it very well. Regards from Brazil.

[@merajinamdar8505]

keep uploading the good the work. Only data science people understand the important of simple and effective teaching.

[@milliekim5072]

Thank you so much, sir! It helped me a lot.

[@vidyullatharavikanth9753]

Very clear explanation.... great work sir

[@ouriel91]

Great video, it was my first experiment in ML and it helped me so much

[@ranjithags7925]

Very clearly and Well explained sir .. Thank you for the video

[@programsolve3053]

Very well explained 🎉🎉🎉 Thanks for your support 🎉🎉🎉

[@mamatasingh7069]

Very nicely explained

[@sharda6686]

Great explanation. Thanks❤️

[@meetsaurabhtiwari]

Plz upload more video Ur concept is very clear

[@StoniAthi]

Very clearly explained.

[@oceanview3165]

This video needs 1M likes!!!

[@maly2512]

Well explained 👏

[@subhajitparamanik1603]

Should not the index=['actual yes', ' actual no'] same for the columns . As the positive comes first so actual yes should come first and not actua no. Same for the column too

[@harithachamath6572]

thank you. it was a great lesson. can you make a video for signal classification on CSV file input too?

[@skillfullbog7923]

Many Thanks for this great Video! Question: How can i actually use this Modell? For Example to predict whether a spezific Passenger will die?

[@dsudheer1660]

Nice explain

[@mounaelgarb5501]

thank you so much Sir

[@lastmukandi5031]

Many thanks for this great video! How can i use this model to predict whether forecast?

[@abdulrehman5128]

Great

[@danishmaxable9661]

Very good video

[@mahmoudchiboub8213]

THANKS

[@reshmab7001]

Can we take first cloumn as predicted yes, then predicted no? Also index in the similar way. In this case true positive is 151. In the video true negative is 151.

[@call-me-potato.]

any idea what we achieve with this? i mean where is the conclucion suppose i wanted to know what are the variables behind those who surrived ? for example if it is age or a gender how do i know ?

[@avtejsingh4947]

In line 74, shown at 37:01 I am getting an error that "ValueError: Input contains NaN, infinity or a value too large for dtype('float64')." What can be done about this?

[@rajmalla5310]

use chat gpt

[@01-sangramsinhbhusnar34]

Please send source code

[@globaldatascience]

Your vocal quality is not good can you uoload again with increased volume