House Price Prediction in Python - Full Machine Learning Project

11:47 train_data.corr(numeric_only=True)

a small summary : for those who are gonna start , he preprocessed the dataset a bit ( removing NaN values, adding features and splitting the catogerical value column to binary columns ) and then scaled,splitted and trained & tested on linear , random forest ..finding best estimator at last ( no explaination on what estimators are, so read forest ahead of doing this )

Mate you explain everything so concisely and keep it so interesting! Really enjoyed this video

16:48, pd.get_dummies(data['ocean_proximity'], dtype=int)

If you could brief explain what linear regression did ? Were all the variable taken into account and develop a slop to predict the value based on existing data? What if we removed some negatively correlated data and the response? I fail to understand what we did apart from cool images, if you can make a brief lectures on regression random decision tree cluster with some situation analysis- it would help us Thanks

Every thing was great but the fact that ive to debugg my entire code because we split earlier and had to pre process the test data again was so painfull speacially in jupyter lab

your tutorials are the best thing i found on the internet

Am impressed,your explanation is so smooth and i can keep tyrack and understand every step or code you input💯

your tutorials are on another level! The way you explain complex topics, makes it feel so achievable. Absolute respect for your teaching skills!

Excellent walkthrough. Meaningful predictions rarely come from raw data alone. They emerge from the process of cleaning, structuring and enriching information before the model is ever trained.