Machine Learning Project in Python: Predicting California Housing Prices

Thank you for watching the video! Here is the notebook: colab.research.google.com/drive/1cF0ZrFM1qj7XSvUsWPE4ku7JWKsq-JW0?usp=sharing
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[@GregHogg]

Take my courses at mlnow.ai/!

[@andreiardelean9324]

The video was very informative but I have what might be a silly question, however I am new to ML. How can we interpret the predicition values? I have seen you during the entire video mentioning here and there "oh this is overfitting" or "there is no overfitting here". I would like to know how can we differentiate overfitting by looking at the prediction values. Unless I am missing something?

[@yanhu2011]

Amazing video! Do you currently have a video that talks different AI models ? Linear regression, neural networks etc? And when and why one should be used for a particular data set?

[@michaelle1229]

Thank you, Greg, for this amazing tutorial, helped me finish how to predict house prices in Melbourne.

[@michaelle1229]

I have a question, does neural networks can be helpful or useful to reduce any noise/interference or overfitting on the final dataset? If so, what conclusion do you make in this case?

[@rednax25]

Please let us know how we can encourage you to do more videos like this......Thanks for the wonderful explanations. I really love your style of teaching.

[@GregHogg]

Thanks so much Alexander - which is my middle name by the way!

[@agnesdemarco9386]

You're the best! Brilliant and clear explanation!!!! Thank youuu!!!

[@GregHogg]

Very welcome 😁😁

[@denysshushpanov7630]

Great video! I have a question though, how would you approach data preprocessing while making predictions on trained model with the new data (let’s say user input or streaming)?

[@GregHogg]

As long as you have some predefined processing function that doesn't depend on anything, you would just apply that to every input

[@ayamouallem3619]

Great explanation !! but i have a question , how can i know the correct split for train , test and validation , is it ok if test and val cross in some data ??

[@GregHogg]

Roughly 70, 15, 15, at random

[@sgpleasure]

Thanks for the new video. Would using a PIPELINE be cleaner and recommended by you?

[@GregHogg]

You're welcome! Pipelines are great. I've never personally used them, but I know how they work and they're pretty awesome.

[@user-up6wd6em5u]

16:00 , i find a easy way to do: median_house_value = 'median_house_value' all_columns = list(data.columns) all_columns.remove(median_house_value) new_column_list = all_columns + [median_house_value] data_new = data[new_column_list] data_new

[@user-up6wd6em5u]

also 19:50 it can use another easy and auto way to split train_test from sklearn.model_selection import train_test_split X = data_new.drop(['median_house_value'], axis=1) y = data['median_house_value'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

[@mathhack8647]

@39:00 the validate MSE is worse then the training. The model did not generalize but does this mean that's an overfitting issue.?

[@GregHogg]

Yes, we overfit to the training set :)

[@anitube7360]

can we have a project tutorial on real-time facial emotion detector in python......... plz tell the different approaches/methods that we can take to complete this project.

[@GregHogg]

Okay this will be added to the to-do list!

[@MohdIrshad-hv4vw]

That is good It will really helpful for me

[@GregHogg]

I'm very glad!

[@thummapalasuresh292]

Could you please provide the dataset....

[@bnneit777]

Hi everyone! MSE and RMSE are too large, whether they are reasonable? All the predicting house price tasks I had done had the big MSE and RMSE like that.

[@arsheyajain7055]

Great project!!

[@GregHogg]

Thank you ☺️

[@andrewsnell2309]

I am doing this in google collab and and it keeps saying Import "tensorflow.keras..." could not be resol

[@GregHogg]

Are you importing exactly the same way?

[@mdashik2291]

Could u make a video for roadmap of machine learning & deep learning with Coursera in 2023 plz❤️

[@GregHogg]

I'd say my roadmap is still valid

[@germanshepherd6638]

Why didn't you show us how to create the API and upload it?

[@longtruong9935]

could you provide the notebook link?

[@GregHogg]

Thank you very much for reminding me - it's there now.

[@norbertishimwe228]

On 39:14 why did you predict on the train data instead of the test data?

[@rohitsali2603]

Because we have to calculate the mse of training data not testing data Because we want to know how much accurate our model is by learning from training data

[@__________________________6910]

U are best

[@GregHogg]

No u

[@__________________________6910]

@@GregHogg No U. Today I watched the full video 😃

[@GregHogg]

@@__________________________6910 Oh great to hear!

[@__________________________6910]

@@GregHogg 😅