MultiClass Logistic Regression, Python Machine Learning, Detecting multi outcome logistic regression

In this #PythonMachineLearning series, #MultiClassLogisticRegression is explained step by step using #IRISDataset. Logistic regression is applied on iris dataset and the species of iris flower is detected using its #PetalLength, #PetalWidth, #SepalLength, #SepalWidth.
Link to the code:
github.com/kjsk22/MultiClass-...
Link to Iris dataset used:
www.kaggle.com/saurabh00007/i...
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[@sohailshaikh8571]

Very good approach... One thing could have be applied here for converting Species into numeric data is, LabelEncoder. It will be flexible for any number of species.

[@sebastiendupre7098]

lovely content

[@jw1984mouse]

Thank you for this video. It helps me a lot with my project.

[@lucillewiid5476]

The best tutorial thanks!

[@raj345to]

very nicely explained.

[@ifyifemanima3972]

Thank you!

[@eko_zaubar]

ty, sir.

[@yangfarhana3660]

Thank you for this tutorial, it's very clear! I have a question on the final part of the video, for the same model, with a test size of 0.3, how we improve the score of 0.933, what do we have to tweak?

[@raj345to]

i think normalizing/scaling the data and hyper parameter tuning may help

[@gokulgupta1021]

Can you make video without using sklearn. Using sklearn a baby would make a model. A video should have a proper knowledge that gradient with batch etc.

[@abdra4724]

How to extract sigmoid function from it

[@tiagosilva856]

Use label encoder.

[@tiagosilva856]

And BTW you should not label y as 1,2,3 but 0,1,2 to proper analyze the confusion matriz

[@tiagosilva856]

Wrong. U don't use score in logistic regressions. U use accuracy.

[@tiagosilva856]

Don´t use replace its a bad praticse. Use catergory instead: df['Species']=df['Species'].astype('category') df['Species']=df['Species'].cat.codes df