Kaggle Competition – House Prices; Advanced Regression Techniques Walkthrough



Selected Algorithm: Linear Regression
Used Technologies:
– Python 3
– PyCharm

Kaggle link:

Source Code – Github Link –

Correction:
At 17:50,
It should be test[‘enc_street’] = pd.get_dummies(test.Street, drop_first=True) not test[‘enc_street’] = pd.get_dummies(train.Street, drop_first=True) .

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17 thoughts on “Kaggle Competition – House Prices; Advanced Regression Techniques Walkthrough

  1. You just take account of numerical data what about categorical data?
    Do we have to deal with each and every categorical column and convert it into numerical data column one by one?
    Anyone?

  2. I don't get you here you assume that there is no difference exist between other type of sale condition that is not Partial but when it comes to Street you did not which I does not see the difference in there average SalePrice

  3. Shouldn’t you be predicting the prices for the test.csv data instead of using the train test split on the train data. They gave the test.csv file to predict the prices for those houses. ?

  4. @Nimeshika Ranashinghe At 17:50, you run, train['enc_street'] = pd.get_dummies(train.Street, drop_first=True) on the training data, then you run test['enc_street'] = pd.get_dummies(train.Street, drop_first=True). Did you mean to set test['enc_street'] = pd.get_dummies(train.Street, drop_first=True) or test['enc_street'] = pd.get_dummies(test.Street, drop_first=True)?

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