K-Nearest Neighbor Classification (K-NN) Using Scikit-learn in Python – Tutorial 25

///K-Nearest Neighbor Classification (K-NN) Using Scikit-learn in Python – Tutorial 25

K-Nearest Neighbor Classification (K-NN) Using Scikit-learn in Python – Tutorial 25

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In this tutorial, you will learn, how to do Instance based learning and K-Nearest Neighbor Classification using Scikit-learn and pandas in python using jupyter notebook. K-Nearest Neighbor Classification is a supervised classification method.

This is the 25th 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:

Download Link for Enrollment Forecast:

Download Link for Iris Data Set:

Download Link for Snow Inventory:

Download Link for Super Store Sales:

Download Link for States:

Download Link for Spam-base Data Base:

Download Link for Parsed Data:

Download Link for HTML File:

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By |2020-02-16T01:50:53+00:00February 16th, 2020|Python Video Tutorials|14 Comments

14 Comments

  1. S February 16, 2020 at 1:50 am - Reply

    Amazing amazing very useful and well made tutorial, thank you sir!

  2. Hamza Benkhalil February 16, 2020 at 1:50 am - Reply

    Hi sir, can you please make a video about Image segmentation with KNN in python from scratch please, it would help me a lot with a project

  3. HAMZA QUAID JOHER February 16, 2020 at 1:50 am - Reply

    You can improve this by explaining more about hyper parameters and about tweaking the model according to the problem

  4. CH D February 16, 2020 at 1:50 am - Reply

    No module named 'sklearn.cross_validation'

  5. CH D February 16, 2020 at 1:50 am - Reply

    can I get the code please?

  6. Hasan E Rezwan February 16, 2020 at 1:50 am - Reply

    ( Download Link for Cars Data Set: ) this link is not working properly. will you please provide us the data set ( for all data set) via another medium. … Thanks in advance 🙂

  7. Yiying Zhang February 16, 2020 at 1:50 am - Reply

    content is good but allow way to many advertisement.. which makes it annoying

  8. JEETESH GARSUND February 16, 2020 at 1:50 am - Reply

    how plot the dataset in clusters?

  9. Neha Sheth February 16, 2020 at 1:50 am - Reply

    Can you please share this dataset and code?

  10. Bill Windsor February 16, 2020 at 1:50 am - Reply

    @TheEngineeringWorld – excellent video here on K-Nearest Neighbors concepts and application code. Your data science videos are excellent for developing to an intermediate and advanced level of data science skills, working from a foundation that one already has an introductory level skill set. I am sure your approach builds your own professional standing in the data science community, as well as enhancing our skill set in the field. Many thanks.

  11. Muhammad Arief Hidayat February 16, 2020 at 1:50 am - Reply

    how to import reParams from sklearn pelase?

  12. Josefina Marin February 16, 2020 at 1:50 am - Reply

    Thank you so much for this! You saved my life

  13. Narendra Rana February 16, 2020 at 1:50 am - Reply

    Thank you for this introductory video to kNN, I would have liked to understand how the model got k=5 and how is the prediction actually returning 0 or 1 instead of outputting the number of nearest neighbors it found. Finally, you mentioned that this approach is only good for small datasets can you elaborate why also any ideas on what to use for large datasets instead of knn?

  14. Joshua Fancher February 16, 2020 at 1:50 am - Reply

    Thanks for the tutorial!

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