House Price Prediction in Python - Full Machine Learning Project
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Top Comments (10)
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)
12:19 - plt.figure(figsize=(15,8)) sns.heatmap(train_data.corr(numeric_only=True), annot=True)
Hi. What I would recommend doing in the hyperparameter tunning phase on the RFR model. Is to use np.range() instead of a list with hard values the model has to use and which are limited to two options or three. Yes this might take a lot of time to run but using randomizedsearchCV would be okay as a starter then if you see the model improving you can use gridsearchcv instead.
Amazing work man
explained better than my instructor xD thanks man
Keep it up bro! Pls do more videos with predictions
Oh my!! Just amazing!! Make more such videos. Thank you so much.
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Top Comments (10)
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)
12:19 - plt.figure(figsize=(15,8)) sns.heatmap(train_data.corr(numeric_only=True), annot=True)
Hi. What I would recommend doing in the hyperparameter tunning phase on the RFR model. Is to use np.range() instead of a list with hard values the model has to use and which are limited to two options or three. Yes this might take a lot of time to run but using randomizedsearchCV would be okay as a starter then if you see the model improving you can use gridsearchcv instead.
Amazing work man
explained better than my instructor xD thanks man
Keep it up bro! Pls do more videos with predictions
Oh my!! Just amazing!! Make more such videos. Thank you so much.