no clue how to interoperate the decision tree, I understand that 1 means buy and 2 means sell, but I dont understand where to look for the stock price? meaning if a high is EX: 112 then buy, where do I look in the decision tree for that?
One question that boggles me is how to use this to predict if I should buy tomorrow or in the coming week. I saved the model using pickle, but then when I load the model using pickle.load, how to I get predictions using loaded_model.predict for a future date, like tomorrow or next week?
I'm currently learning Decision Trees and it was really ambiguous , this has helped a lot, I also do not need to understand the equations explained here because it is all intuitions. Thanks a million Professor Bazzi 👏🏻
For those asking about decision trees. According to wikipedia, a decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
no clue how to interoperate the decision tree, I understand that 1 means buy and 2 means sell, but I dont understand where to look for the stock price? meaning if a high is EX: 112 then buy, where do I look in the decision tree for that?
Thanks for this tutorial. I like your simplistic explanation. One question that boggles me is how to use this to predict if I should buy tomorrow or in the coming week. I saved the model using pickle, but then when I load the model using pickle.load, how to I get predictions using loaded_model.predict for a future date, like tomorrow or next week?
@@danmcgloven8169 What does 'haters' have to do with it anyway? Why just throw away silly words, I tried to help in my experience and show that there's an internal mistake when you make a prediction about a few days ahead and then mix the data so some leaks to the test. Ask yourself why it doesn't give good results to the forecast for one day ahead, but excellent results for 60 days ahead. All I'm saying is that in practice this model is not equal and the test data is incorrect.
This model is wrong -- you have used close, high, low prices, and volume as features but it does not make sense because in real-time prediction if u have these values anyone can make a buy/sell decision.....if open
00:00:00 Introduction
00:01:11 Decision Tree Classifiers
00:02:31 The CART Algorithm
00:05:39 Gini Impurity
00:10:43 CART Sub-optimality
00:13:31 Entropy
00:21:52 scikit-learn: Decision Tree Classifiers
00:25:38 Viewing decision trees using graphicviz
00:30:41 Plotting Decision Boundaries on Python
00:38:08 Soft Decision Tree Classifiers
00:40:45 Decision Tree Classifiers & Rotation Sensitivity
00:46:42 Decision Tree Regression
00:49:57 scikit-learn: Decision Tree Regressor
00:56:11 Stock Market Analysis: Decision Trees Predicting Buy & Sell Signals
Love this part so much 00:56:11 Stock Market Analysis: Decision Trees Predicting Buy & Sell Signals
Very useful
no clue how to interoperate the decision tree, I understand that 1 means buy and 2 means sell, but I dont understand where to look for the stock price? meaning if a high is EX: 112 then buy, where do I look in the decision tree for that?
Machine Learning is so much is intuitive, fun and easy. Thanks to Ahmad Bazzi !
Very good lecture Prof. Ahmad Bazzi !
DecisionTreeClassifier() actually works for trading signals 👍🏻
Hey there lil mama
i can apply DecisionTreeClassifier on other stuff if you know what i mean 😀😜
I tried it for cryptocurrency. Good results. Lost 200 made 300 USD tether.
One question that boggles me is how to use this to predict if I should buy tomorrow or in the coming week. I saved the model using pickle, but then when I load the model using pickle.load, how to I get predictions using loaded_model.predict for a future date, like tomorrow or next week?
I tried this yesterday on some stocks in my portfolio and worked pretty fine :) Thanks a lot Ahmad !
I'm currently learning Decision Trees and it was really ambiguous , this has helped a lot, I also do not need to understand the equations explained here because it is all intuitions. Thanks a million Professor Bazzi 👏🏻
Gini index has been introduced by Breiman and Entropy by Claude Shannon.
Ahmad has been consistent since 2018, video after video, energy on top of energy. He's charged up. Enough said.
Very good lecture @Ahmad Bazzi 👍
For those asking about decision trees. According to wikipedia, a decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
Thank you Ahmad
no clue how to interoperate the decision tree, I understand that 1 means buy and 2 means sell, but I dont understand where to look for the stock price? meaning if a high is EX: 112 then buy, where do I look in the decision tree for that?
You're awesome , never learned anything better than this 🥺
Best Explanation Ever!!!
Thanks for this tutorial. I like your simplistic explanation. One question that boggles me is how to use this to predict if I should buy tomorrow or in the coming week. I saved the model using pickle, but then when I load the model using pickle.load, how to I get predictions using loaded_model.predict for a future date, like tomorrow or next week?
Liked and subbed
@peppemelia We should all support like u
👍🏻
perfetto
Me too I've subscribed !
subbed !
Thanks a lot Ahmad 👌🏻
entendible
👍
Simply Fantastic!
Very precious sir !
Perfect !!
Mantap mamang
Thanks for an awesome lecture !
Perfect place to revise my :)
I agree.
Please can I have the code for this ?
Thanks sir.
#underrated
SAFEMOON crew, where are you ? 🚀
I'm all in on safemoon @Mustafa 💰
I LOVE YOU AHMAD BAZZI ❤️
I'm sorry to inform you that your model suffers from a data leak between training and testing, so the results are not actually right.
Some haters commenting here 😅
I literally tried it and it gave me satisfactory results
@@danmcgloven8169 What does 'haters' have to do with it anyway? Why just throw away silly words, I tried to help in my experience and show that there's an internal mistake when you make a prediction about a few days ahead and then mix the data so some leaks to the test. Ask yourself why it doesn't give good results to the forecast for one day ahead, but excellent results for 60 days ahead. All I'm saying is that in practice this model is not equal and the test data is incorrect.
Hey! thanks for pointing it out. Can you please also tell how to get rid of the leakage?
Again thanks!
Works perfectly for data leakage.
Can you pinpoint where is the leakage ?
Appreciate it bro <3 :) :D
Hey you, if you read this, you are awesome.
This model is wrong --
you have used close, high, low prices, and volume as features but it does not make sense because in real-time prediction if u have these values anyone can make a buy/sell decision.....if open
T.H.A.N.K. Y.O.U. S.O. M.U.U.U.U.U.C.H!!