Naive Bayes Classifier in Python (from scratch!)

I was importing a mysql dataframe, I was importing string elements and it resolved them into objects,
data = pd.read_sql_table("ai_learning", engine)

columns_to_convert = ["Products", "feedback", "blog", "diagnosis"]

data[columns_to_convert] = data[columns_to_convert].apply(pd.to_numeric, errors='coerce')
data = data[["Products", "feedback", "blog", "diagnosis"]]

This is how I fixed it if anybody was getting the same outputs.

this gave a lot of clarity , thanks

When you are checking for gaussian curve, shouldn't you have filtered for different diagnosis and then check if the curve fits?
Because now, we see the data fits gaussian. But we then change the data and only take a subset and then fitting the curve
Thanks for the great video.

perfect!

😃Bro thx for the nice explanation. Are you using a theme for vs code, cuz all the colours in your systems are looking damn good

@normalized Nerd How do you make a prediction with this using specific values?

You're a legend my dude, thanks so much for explaining this

can someone explain me the guassian distribution part

I did not understand the output, we were detecting the cancer patient, but in out put there are two matrix and accuracy data so which is which.

Damn, I was hoping for a SKlearn tutorial!

Thank you for opening up new horizons for me <3

Man, u save my life ty very much.
Use sklearn is too easy, justify why u decide to use Naive and why u can use it is the very important thing, keep it up man .
( excuse me for my bad english )

Thanks man,your effort to make algorithms from scratch is just on another level.Your effort is much appreciated👍

Hello! Is it possible to add the multinomial in the code? Thank you.

Awesome video.

the number of subscribers to your channel does not do justice to your content. This is such quality educational content. Keep it up, man.

Hi, I am getting error as "index 29 is out of bounds for axis 0 with size 29" for this statement likelihood[j] *= cal_gaussianLikelihood(df,features[i],x[i],Y,labels[j]), any solution?

Fantastic video, very well explained!

Sorry, again I do understand now, and also I apply in my work with excellent results, Thanks!

Sorry, but I do not understand who is "df" when you def a function because you have never defined. I will appreciate your explanation

SVMs,Random Forest and gradient boosting left in the playlist

likelihood = [1] * len(labels), post_prob = [1] * len(labels)
what this above code actually do?

And also how can I work this code on tennis.csv dataset?

amazing content and fantastic explanations

Excellent video. Keep up the good work 🙂

Super high quality videos! I'm surprised you have 8K and not 800K... Keep it up!

The from scratch series in this channel is the best !!

Nice video