Scikit Learn Tutorial | Scikit-Learn Workflow | Data Preprocessing In Machine Learning | Intellipaat

Really good session✨

Love the way you generalise the concept🫡

Students like us are very obliged to have masters like you ❤️

This is so good. You got a new subscriber. Please continue doing the great work

Professionally Explained!

How can I access the code that you were writing during the video?

Bro you didn't mention IDE name please tell me which IDE it is?

I want create jarvis like model can anyone help me out what things should I learn and roadmap for development

get_parameters(df) return error ?????????????
run the function is ok when executing
get_parameters(df)
you get this
KeyError: '[211.3375, 151.55, 26.55, 77.9583, 51.4792, 49.5042, 227.525, 69.3, 78.85, 25.925, 247.5208, 76.2917, 75.2417, 52.5542, 221.7792, 91.0792, 135.6333, 35.5, 164.8667, 262.375, 30.5, 50.4958, 39.6, 27.7208, 134.5, 26.2875, 27.4458, 512.3292, 47.1, 61.175, 53.1, 86.5, 29.7, 136.7792, 25.5875, 83.1583, 25.7, 71.2833, 81.8583, 106.425, 56.9292, 78.2667, 31.6792, 31.6833, 110.8833, 26.3875, 27.75, 133.65, 49.5, 79.2, 38.5, 211.5, 59.4, 89.1042, 34.6542, 28.5, 153.4625, 63.3583, 55.4417, 76.7292, 42.4, 83.475, 93.5, 42.5, 51.8625, 57.9792, 30.6958, 28.7125, 25.9292, 39.4, 45.5, 146.5208, 82.1708, 57.75, 113.275, 26.2833, 108.9, 25.7417, 61.9792, 66.6, 40.125, 55.9, 82.2667, 32.3208, 79.65, 28.5375, 33.5, 34.0208, 75.25, 77.2875, 61.3792, 11.5, 10.5, 12.525, 13.5, 26.25, 36.75, 73.5, 31.5, 32.5, 13.8583, 14.5, 12.275, 13.7917, 12.35, 10.7083, 41.5792, 12.875, 15.0458, 37.0042, 15.5792, 19.5, 9.6875, 30.0708, 13.8625, 15.05, 12.7375, 15.0333, 18.75, 12.65, 15.75, 7.55, 20.25, 7.65, 7.925, 7.2292, 7.25, 8.05, 9.475, 9.35, 18.7875, 7.8875, 7.05, 8.3, 22.525, 7.8542, 31.275, 7.775, 7.7958, 7.8958, 17.8, 31.3875, 7.225, 14.4583, 15.85, 19.2583, 14.4542, 7.8792, 4.0125, 56.4958, 7.75, 15.2458, 15.5, 16.1, 7.725, 7.0458, 7.2833, 7.8208, 6.75, 8.6625, 7.7333, 7.4958, 7.6292, 15.9, 8.1583, 10.5167, 10.1708, 6.95, 14.4, 24.15, 17.4, 9.5, 20.575, 12.475, 13.9, 6.975, 15.1, 34.375, 7.7417, 20.525, 7.85, 46.9, 8.3625, 9.8458, 8.85, 19.9667, 14.1083, 6.8583, 8.9625, 12.2875, 6.45, 7.0542, 8.1125, 6.4958, 8.6542, 11.1333, 23.45, 9.825, 7.125, 8.4333, 7.5208, 13.4167, 7.8292, 7.7375, 22.025, 12.1833, 9.5875, 9.4833, 25.4667, 6.4375, 15.55, 7.5792, 7.1417, 23.25, 7.7875, 8.0292, 8.4583, 15.7417, 11.2417, 7.8, 6.2375, 9.225, 3.1708, 8.4042, 7.3125, 9.2167, 8.6833, 21.075, 39.6875, 8.7125, 13.775, 22.3583, 8.1375, 29.125, 7.7208, 20.2125, 7.7292, 7.575, 69.55, 9.325, 21.6792, 16.7, 7.7792, 27.9, nan, 9.8375, 10.4625, 8.5167, 9.8417, 7.875] not in index'
Output is truncated. View as a scrollable element or open in a text editor. Adjust cell output settings...

if you kindly post the full function syntyx of def get_parameters(df):

amazing session, thanks

Thanks

thanks a lot amazing session

Excellent explanation

I am new to machine learning and wanted to learn more about scikit learn. Will watching this playlist make me good enough to procedd with machine learning?

So nice explanation 🎉🎉
Thank you so much 🎉🎉🎉🎉🎉

am here on 2024

please provide the dataset asap

Great content.Thanks!

Thank you !!, but how do I remember and implement. Please suggest.

Write (parse = ‘auto’ ) when calling fetch_openml to silent the warning (its cz of the new version)

Please share the colab link on discription for every video to practice it. Thank you.

nice tutorial very thanks 2u , please could you pass the github code for this video

for col, param in parameters.items():
missing_values = np.nan
strategy = param['strategy']
imp = SimpleImputer(missing_values = missing_values, strategy = strategy)
df[col]=imp.fit_transform(df[[col]]).ravel()
Use this

Thank you so much great explanation

Enjoy !, I am your new subscriber

missing_values = param['missing_values'] Key Error coming 'missing_values' in this line

Is machine learning the pathway to Data science? Is Data science able to be automated? Is there AI components in this? This is very nice course I am learning a lot! 😃

Thank you Sir😊

thank you so much ... really excellent ...

Great instruction and video presentation. Thank you Sr.