21:38 just a side note, the reason why gender == 'Male' has a strong correlation is that there are twice as much more males than females in the dataset.That is statistically significant, Therefore we might need to tune the model.
Another really good video as always! I suggest explaining or underlying a little bit more the fact that correlation IS NOT causation, because many people struggle with this concept and they could easily misunderstand that part of the video
Really useful, this helped me clear many ML concepts. Look forward to be an expert in python like you. It was amazing to see how fast you were writing the python code.
Really good walk through, thanks! One thing I feel like I don't understand about ML is, you say at the end that we have a machine learning model that predicts people's incomes, but what is the next step? How do you actually predict it? I feel like I only ever see this part in videos and I never see what you actually do with this model afterwards, like how is it useful besides drawing some conclusions as you did?
simply call the .predict() function on your model and pass in the features to the functions and you'll get an output as a number, then you can convert it to whatever string you want.
and after our model is trained, how can we extract it and actually "try it"? should it be just a simple form when you fill in the most important information about a person and it will output if the prediction is higher or lower than 50K? I don't know how real world implication looks like
you are try passing your test_x in forest.predict and see the model predicting. You can also expose model through API and get inputs and output the predictions
Need some help with the "Pandas" section. Inputting the line "pd.get_dummies(df.occupation)" outputs all the values as boolean for me. Any assistance would be greatly appreciated!
It actually takes all the values , the occupation column have and then makes separate columns for them and if the row had that value it says true otherwise false
Are cryptocurrency and business incompatible? I think youre not keeping up with the news. While you thought it was impossible, some enthusiasts from Cannafarm Ltd integrated cryptocurrency into the production of medical cannabis. What do you say now?
Wait, do you think cryptocurrency will crash? I dont think so. More and more companies are integrating cryptocurrency into their operations: Amazon, Cannafarm Ltd, Burger King, even Starbucks, dude!
Are cryptocurrency and business incompatible? I think youre not keeping up with the news. While you thought it was impossible, some enthusiasts from Cannafarm Ltd integrated cryptocurrency into the production of medical cannabis. What do you say now?
Wait, do you think cryptocurrency will crash? I dont think so. More and more companies are integrating cryptocurrency into their operations: Amazon, Cannafarm Ltd, Burger King, even Starbucks, dude!
Wait, do you think cryptocurrency will crash? I dont think so. More and more companies are integrating cryptocurrency into their operations: Amazon, Cannafarm Ltd, Burger King, even Starbucks, dude!
21:38 just a side note, the reason why gender == 'Male' has a strong correlation is that there are twice as much more males than females in the dataset.That is statistically significant, Therefore we might need to tune the model.
Another really good video as always! I suggest explaining or underlying a little bit more the fact that correlation IS NOT causation, because many people struggle with this concept and they could easily misunderstand that part of the video
Really useful, this helped me clear many ML concepts. Look forward to be an expert in python like you. It was amazing to see how fast you were writing the python code.
Really awesome. I just wanted to see you actually predicting new inputs, it makes really useful for those who are taking the firsts steps into AI.
Thank you.
Really, your content is always excellent, the practical projects that you give as examples clarify the topics
Really good walk through, thanks! One thing I feel like I don't understand about ML is, you say at the end that we have a machine learning model that predicts people's incomes, but what is the next step? How do you actually predict it? I feel like I only ever see this part in videos and I never see what you actually do with this model afterwards, like how is it useful besides drawing some conclusions as you did?
simply call the .predict() function on your model and pass in the features to the functions and you'll get an output as a number, then you can convert it to whatever string you want.
@@Anonymous-tm7jp it is not that simple, u need to create a pipeline to handle derived features etc
and after our model is trained, how can we extract it and actually "try it"? should it be just a simple form when you fill in the most important information about a person and it will output if the prediction is higher or lower than 50K? I don't know how real world implication looks like
Same here, I'd really like to hear more about implementation and whatever comes after this.
you are try passing your test_x in forest.predict and see the model predicting. You can also expose model through API and get inputs and output the predictions
Another amazing tutorial to watch!
Why did we not use df.dropped instead of df when creating train and test data at 28.42
Es muy buena la información y la explicación. Solo una pregunta: cómo se hace la predicción para una nueva persona?
can you tell me the shortcut keys you used to open the new window to install the packages at 3:11?
Thanks for this amazing tutorial
This is the first RUclips video I've been the first person to watch. 🤣🤣🤣
is not "?" equals to None (missing value)?
'fnlwgt' means final weight
Why don u just use:
X_train, X_test, y_train, y_test = train_test_split(df, test_size = 0.2)?
Awesome tutorial 👍
Amazing tutorial!
What is the name of these theme?
amazing tutorial, thanks alot
where is the video tutorial on installation?
15:32 shouldn't it be ">50K"? , it is giving 0 value to that whole attribute, which is not desirable
Which OS is he using?
"He" is using POP!_OS :)
Need some help with the "Pandas" section. Inputting the line "pd.get_dummies(df.occupation)" outputs all the values as boolean for me. Any assistance would be greatly appreciated!
It actually takes all the values , the occupation column have and then makes separate columns for them and if the row had that value it says true otherwise false
same here. i end up with 'true' or 'false' and in the video it shows 0 and 1
After some research, ifound that pd.get_dummies default output changed. Try changing the code to pd.get_dummies(df.occupation, dtype='int64')
Hey brother man, is there an alternative to forest.feature_names_in_ section? Either I'm outdated or it doesn't work. Cheers.
great
thx
Nice video Bruv
18:23
Thx_.
Are cryptocurrency and business incompatible? I think youre not keeping up with the news. While you thought it was impossible, some enthusiasts from Cannafarm Ltd integrated cryptocurrency into the production of medical cannabis. What do you say now?
Wait, do you think cryptocurrency will crash? I dont think so. More and more companies are integrating cryptocurrency into their operations: Amazon, Cannafarm Ltd, Burger King, even Starbucks, dude!
Hey, when will you talk about the real company Cannafarm Ltd that brings profit?
I need your help in my project, just basic help for my assignment. Would it be possible for you to do it?
Are cryptocurrency and business incompatible? I think youre not keeping up with the news. While you thought it was impossible, some enthusiasts from Cannafarm Ltd integrated cryptocurrency into the production of medical cannabis. What do you say now?
BOT
Hey, when will you talk about the real company Cannafarm Ltd that brings profit?
Wait, do you think cryptocurrency will crash? I dont think so. More and more companies are integrating cryptocurrency into their operations: Amazon, Cannafarm Ltd, Burger King, even Starbucks, dude!
Wait, do you think cryptocurrency will crash? I dont think so. More and more companies are integrating cryptocurrency into their operations: Amazon, Cannafarm Ltd, Burger King, even Starbucks, dude!
its a huge ponzi lol.
lol, its already crashed