simple model build

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Tue Jul 13 2021 13:40:19 GMT+0000 (Coordinated Universal Time)

Saved by @CaoimhedeFrein #python

#define the target
y = home_data.SalePrice

#Create the list of features below
feature_names = ['LotArea','YearBuilt','1stFlrSF','2ndFlrSF','FullBath','BedroomAbvGr','TotRmsAbvGrd']

# Select data corresponding to features in feature_names
X = home_data[feature_names]

from sklearn.model_selection import train_test_split

# split data into training and validation data, for both features and target
# The split is based on a random number generator. Supplying a numeric value to
# the random_state argument guarantees we get the same split every time we
# run this script.
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 1)

from sklearn.tree import DecisionTreeRegressor
#specify the model
#For model reproducibility, set a numeric value for random_state when specifying the model
iowa_model = DecisionTreeRegressor(random_state=1)

# Fit the model
iowa_model.fit(train_X, train_y)

# get predicted prices on validation data
val_predictions = iowa_model.predict(val_X)

from sklearn.metrics import mean_absolute_error

print(mean_absolute_error(val_y, val_predictions))

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