krishnaik06 / advanced-house-price-prediction-

Advanced-House-Price-Prediction-

advanced-house-price-prediction-'s Issues

Feature Selection

Features can be selected based on multiple criteria. A model cannot only select valuable features. As we can see the drop of too much information as 61 columns (features) are dropped. This will affect the model in prediction.

Not able to understand one block of Code

Mr Krish has not explained this block of Code in his Video can someone help me understand what is being done in this block of Code

for feature in categorical_features:
labels_ordered=dataset.groupby([feature])['SalePrice'].mean().sort_values().index
labels_ordered={k:i for i,k in enumerate(labels_ordered,0)}
dataset[feature]=dataset[feature].map(labels_ordered)

Missing Values

Missing values can be replaced with mode or as per me if the distribution of the column contains top two values like 30% and 29% then some of the missing values can be replaced with 1st top value and others with 2nd top value.