Decision Tree Algorithm in Machine Learning Python - Predicting Churn Example
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- เผยแพร่เมื่อ 12 ม.ค. 2021
- Learn how to run Decisions Trees, Random Forest and Extreme Gradient Boost (XGB) machine learning models in Pythons. This is a full tutorial in machine learning. We start with the problem formulation phase, then EDA phase, then running and evaluating different models. We also show how to adjust the hyperparameters of the models. At the end, we show how to deploy the model predictions in Power BI. Hope you enjoy this video!
Data Analytics Course Link:
ipidata.teachable.com/
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Git Repo:
github.com/Pitsillides91/Pyth...
Tutorial Overview:
- What is the machine learning process
- How to load raw data from excel to python and run ML
- How to clean the raw data for machine learning (EDA)
- How to split the raw data for machine learning
- How to Run decision trees machine learning model
- How to evaluate machine learning models
- How to visualize the decision tree in Python
- How to create a confusion matrix in Python
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python decision tree tutorial sklearn - แนวปฏิบัติและการใช้ชีวิต
![Random Forest Python Example from Scratch using SKLearn - [Deployment Included]](http://i.ytimg.com/vi/aXw12xFVxwg/mqdefault.jpg)
![Random Forest Python Example from Scratch using SKLearn - [Deployment Included]](/img/tr.png)
3 more models added in our library! Let me know what you think about these ones! Thanks!
Great work and resource don't know why it took me this long to find your channel
how we can plot learning curve and compare the test data and the learning data
Thank you so much for this tutorial! It was so helpful to understand and apply for my master's final project
Amazing tutorial! Thank you!
This is excellent wrok ,
Note :
we can plot the DT with this simple code
from sklearn import tree
plt.figure(figsize=(15,10))
tree.plot_tree(dt_model ,filled=True)
Thank you Yiannis , am following your episodes one by one , your really helping me develop myself ,🙏
Same here - thank
You Yiannis
Happy to hear that! Thanks!
Impressively explained....and paced correctly
Glad you liked it!
Very good video! Keep it up!
Hey Yiannis, another brief and concise tutorial, very well explained thus further enhancing the knowledge in the computer science world!!! Keep it up!
Glad it was helpful!
Very nice, thank you
Hi Yiannis,
Thank you soooo much for your videos, no kidding it's so clever. We need other tutorial bro ! You are giving such good contents thought.
I just have one remark : in this tutorial, the training data are the same as the one for testing witch is a bit confusing... Anyway, thank's a lot
This is excellent, thank you very much!
Glad it was helpful!
Hey Yiannis, nice to meet you! I just found your channel and subscribed, love what you're doing!
I like how clear and detailed your explanations are as well as the depth of knowledge you have surrounding the topic! Since I run a tech education channel as well, I love to see fellow Content Creators sharing, educating, and inspiring a large global audience. I wish you the best of luck on your TH-cam Journey, can't wait to see you succeed! Your content really stands out and you've put so much thought into your videos!
Cheers, happy holidays, and keep up the great work ;)
Hey Rohak, nice to e-meet you too! Just had a look in your channel! Great work! Keep it up :)
@@Data360YP Nice to e-meet you too! Thanks for taking a look at my channel too, I really appreciate the kind gesture!
Hello, nice work...I have a suggestion...Maybe you should use a dataset that is very dirty, so you can teach and show us how to wrangle it, and also show us how to do feature engineering also
Great tutorial, thank you.
My understanding is that scaling would not be required for tree-based algorithms, as they are not skewed by not scaled/normalised data. Not that it would hurt the prediction, just that would save time by skipping it.
Best Tutorial, plz make some videos about Feature Engineering.
Cool! Thanks!
Welcome!
Hello Sir, Why you are not making any videos, it was so great and helpful
Thank you for providing this! Super helpful. Just a quick question, can you use the random forest model to predict the accuracy of certain thing? For example, if we are doing a count for inventory, and I want to use All the parameters to predict how accuracy each scenarios would be the count ? In this case the output is not a 1 or 0 but a range of number between 0 and 1
thank you
YOU ROCK! :)
Hi Yiannis
I wrote the code step by step according to your guide
In the tree I got an entropy greater than 1, how can that be? what did I do wrong?
Great video! I have a question though: does anybody know why you need to split HasCrCard in _1 and _0? Isnt the original column already binary? I mean the _1 is the same as the original HasCrCard, why add the reverse in _0? Is this some kind of balancing method?
Cheers
Hi Yiannis, I love your videos and am finding them very captivating to listen to and follow along! I have a problem I am working on right now using decision trees and RFA and was wondering if I could get your advice on it? would you be available for a quick chat?
I have a dataset I have that I need to predict on groundwater potential and I can't figure it all out. May you please 🥺 help.?
Can anyone help me out ,I'm getting error like couldn't able to convert int value into float from datasheet