Random Forest Python Example from Scratch using SKLearn - [Deployment Included]

Learn how to run Decision Trees, Random Forest and eXtream Gradient Boost Trees in Python using SKLearn in Jupyter Notebooks. All the code is provided. 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/
Support the channel on Patreon:
/ data360yp
Git Repo:
github.com/Pitsillides91/Pyth...
Video Part 1:
• Decision Tree Algorith...
Tutorial Overview:
- How to run Random Forest Machine learning in Python SKLearn
- How to run eXtream Gradient Boost (XGB) machine learning model in Python
- How to optimize random forest; hyperparameters tuning
- How to optimize XGB; hyperparameters tuning
- How to create a confusion matrix in Python
- How to deploy a machine learning model in python
Yiannis Pitsillides on Social Media:
/ pitsillides91
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Tags:
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Random Forest Python Example from Scratch

[@Data360YP]

Let me know what you think about this video! Which model shall we do next?

[@Thanos-gg5ru]

Hey Giannis thanks for the video. Could you please make a video of applying sklearn models on time-series data(especially univariate) ? I think its interesting because its a bit different and there are not many examples about this on the internet.

[@balakrishna5711]

Hi, could you please let me know how to read 60 k files of 3 TB data into jupyter notebook, please help me or refer any source, it's very urgent for my work.
Could you please reply for the same.
Thanks...

[@hristo8835]

Great Video. As to model ideas for the future, how about a recommender model to select items based on similar attributes rather than the one everyone does to recommend movies via ratings.

[@21121990jay]

Can you please make a video on multilinear Regression model with deployment.

[@treyhannam3806]

This is great! I am wondering if you could use GridSearchCV instead of writing out a loop when tuning the random forest model? If you can, is the method equally as useful?

[@konstantinoskrit]

Εύχομαι μέσα από την καρδιά μου τη βοήθεια που μας προσφέρεις να την απολαύσεις κ' με το παραπάνω...

[@Data360YP]

Nase kala Konstantino!

[@leandrop.7963]

Mate, I need to say, You are a legend, seriously this is what everyone look around and can't find!
So happy to have found this that I have to comment.
Such a great work, deserve to be said. Congrats, I'm huge fan after that.

[@andreasp.189]

Another excellent, well structured and informative video added to the Data science world by Yiannis who doesn't stop to impress us! Keep it rolling!!!

[@Data360YP]

Thanks again!

[@chrysis7989]

Thanks Yiannis!
Keep up the good work!

[@abidhossain4412]

This channel is the best. Please do more videos on other machine learning models.

[@MahmoudNasrCPA]

Thank you for this great tutorial

[@Downesy230]

Great video. If I have no 'new' data, so should I fit the XGBoost model on the training set (.fit(X_train,y_train)) only and then predict y with only X_test (.predict(X_test))?

[@hristo8835]

Thank you Yiannis. Great content!

[@Data360YP]

Glad you liked it!

[@tomjohnas9304]

Nice!! Been waiting for this!

[@Data360YP]

Enjoy

[@shadrackdarku8613]

great content keep the good work up .Bravo

[@abbeynguyen8396]

Thank you so much for your videos. Please make more videos about data visualization.

[@Rodaportal]

Well detailed, keep it up!

[@Data360YP]

That's the plan!

[@johnsonb90436]

Hello,
For the calf.best_estimator_, my output cell is not displaying all of the prams and putting …) at the end. How do i get the output to show everything?

[@alasternyaude2582]

amazing work!

[@Data360YP]

Thank you! Cheers!

[@paulntalo1425]

Thank you for this great video

[@Data360YP]

Glad you enjoyed it!

[@jerrerock]

Why do you take all of the data in the XG Boost clf.fit(X,Y), I have been learned to always split test, train, validation cause otherwise the model is overfitting and can't be tested??

[@jongcheulkim7284]

Thank you.

[@tarigahmed8637]

Thank you Yiannis, great as always,
I have little suggestion about previous episodes,
If you can make more projects regarding sql,and joins, (project that can link excel with sql again)
Also if you can make some projects with tableau, like sales insights or any projects.
I didn't reach these today video yet to give my opinion 😅
Thank you

[@Data360YP]

Thanks for the idea!

[@cowardlyanonymous2446]

thank youuuu!!

[@lukeshlarik8421]

Hello Sir, Why you are not making any videos, it was so great and helpful

[@tristanhughes6409]

I've tried to follow this, but having problems with GraphViz. It says: AttributeError: module 'graphviz.backend' has no attribute 'ENCODING' . Does anyone have any ideas?
The video is very informative, thank you

[@louatisofiene9114]

is it okay to work with 49% of misclassified
i have same problem BTW

[@ajithkurianphotography6181]

How to give a single input for prediction

[@ifeanyiokwuazu3225]

Wow

[@josephmart7528]

I think you made a mistake in your confusion matrix plot for the final_model when you used (classes=rf.classes_)
It should be like this ==>> plot_confusion_matrix(cm_norm, classes=final_model .classes_)
You should have used (final_model .classes_) not plot_confusion_matrix(cm_norm, classes=rf.classes_)
My final model gave 0.79 TP when I used [classes=final_model .classes_]