Naive Bayes Classifier – Multinomial Bernoulli Gaussian Using Sklearn in Python – Tutorial 32




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In this Python for Data Science tutorial, You will learn about Naive Bayes classifier (Multinomial Bernoulli Gaussian) using scikit learn and Urllib in Python to how to detect Spam using Jupyter Notebook.
Multinomial Naive Bayes Classifier
Bernoulli Naive Bayes Classifier
Gaussian Naive Bayes Classifier

This is the 32th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist “the sexiest job of the 21st century.” Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We’ll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.

Download Link for Cars Data Set:
https://www.4shared.com/s/fWRwKoPDaei

Download Link for Enrollment Forecast:
https://www.4shared.com/s/fz7QqHUivca

Download Link for Iris Data Set:
https://www.4shared.com/s/f2LIihSMUei
https://www.4shared.com/s/fpnGCDSl0ei

Download Link for Snow Inventory:
https://www.4shared.com/s/fjUlUogqqei

Download Link for Super Store Sales:
https://www.4shared.com/s/f58VakVuFca

Download Link for States:
https://www.4shared.com/s/fvepo3gOAei

Download Link for Spam-base Data Base:
https://www.4shared.com/s/fq6ImfShUca

Download Link for Parsed Data:
https://www.4shared.com/s/fFVxFjzm_ca

Download Link for HTML File:
https://www.4shared.com/s/ftPVgKp2Lca

Source


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Comment List

  • TheEngineeringWorld
    November 24, 2020

    Terimakasih. Untuk teman" lainya saya juga ada nih video tutorial ngoding Naive Bayes python 3 bisa di cek barangkali cocok.
    https://youtu.be/m0HVDfe0k90

  • TheEngineeringWorld
    November 24, 2020

    can you please share a tutorial for apriori algorithem of assosiation rule mining using sklearn in python

  • TheEngineeringWorld
    November 24, 2020

    thanx a lot for this tutorial it's really usefull

  • TheEngineeringWorld
    November 24, 2020

    Why did you use only 47 columns instead of 57 for predictive features? Sorry for my bad english

  • TheEngineeringWorld
    November 24, 2020

    thanks alot sir

  • TheEngineeringWorld
    November 24, 2020

    thankyou soo much …..u saved my day

  • TheEngineeringWorld
    November 24, 2020

    hey u know parameters prior_fit in naive bayes for what? i dont understand in documentation thx

  • TheEngineeringWorld
    November 24, 2020

    It helped me in completing my final year project. Thanks and keep making more such videos.

  • TheEngineeringWorld
    November 24, 2020

    Hello sir, Thanks for your video. Could you please explain the python code on how to implement the laplace smoothing for zero frequency issue in naive bayes.

  • TheEngineeringWorld
    November 24, 2020

    There are more than 48 features since on printing dataset[0] there are more than 48 values as featuers. Let me know if I am wrong.

  • TheEngineeringWorld
    November 24, 2020

    Thank you for making this video 🙂 It was very helpful..

  • TheEngineeringWorld
    November 24, 2020

    " module 'urllib' has no attribute 'urlopen' " ——- how to solve this error ???????????

  • TheEngineeringWorld
    November 24, 2020

    My multinomial accuracy is lower than the bernoulli accuracy..what is the reason?

  • TheEngineeringWorld
    November 24, 2020

    I got a virus when I tried to get data from his link for Spam-base data. Don't go to the 4shared.com link.

  • TheEngineeringWorld
    November 24, 2020

    Good tutorial! Keep up the good work! By the way, you could loop over the BernoulliNB binarize number. If you do that, then you will find that binarize = 0.35 gives a slightly better result (0.8986175115207373).

  • TheEngineeringWorld
    November 24, 2020

    I am getting an error message

    y_expect = y_test

    y_pred = BernNB.predict(X_test)

    print(accuracy_score(y_expect , y_pred))

    throwing error as:
    NotFittedError: This BernoulliNB instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

    Please suggest how to resolve it

  • TheEngineeringWorld
    November 24, 2020

    good video

  • TheEngineeringWorld
    November 24, 2020

    How to get a specific label i.e Spam or not spam as output ??? Accuracy is good but if I want to find a label for a custom text How to do?

  • TheEngineeringWorld
    November 24, 2020

    what format is X in? my format is [[0.1,0.2],[0.2,0.3]] and it only outputs 0.0 as accuracy

  • TheEngineeringWorld
    November 24, 2020

    where is url

  • TheEngineeringWorld
    November 24, 2020

    x= dataset [: , 0:48] and y= dataset[:, -1] how is it working can someone explain ?

  • TheEngineeringWorld
    November 24, 2020

    Thanks. It was very helpful to me.

  • TheEngineeringWorld
    November 24, 2020

    Predictors are not 'independent', they are 'conditionally' independent given the class

  • TheEngineeringWorld
    November 24, 2020

    Good. But I don't think so for the "binarize=True". Actually the binarize is a floating point number being the threshold of judging 0 or 1.

  • TheEngineeringWorld
    November 24, 2020

    very nice explanation – it would be great if you can share the source code on Github

  • TheEngineeringWorld
    November 24, 2020

    Excellent video sir!.Could you kindly make a video on how to plot the confusion matrix for the algos used in this video.

  • TheEngineeringWorld
    November 24, 2020

    I like the way you explain ..Expecting more videos to come …Thanks alot

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