train.head()

test.head()

sample.head()

train.describe()

train = train.drop(columns=["id"])

train["Year_Factor"].value_counts()

train["State_Factor"].value_counts()

train["building_class"].value_counts()

train["facility_type"].value_counts()

from sklearn.preprocessing import StandardScaler scaler = StandardScaler()

nominal = ["Year_Factor", "State_Factor"] ordinal = ["building_class", "facility_type"]

train_val = train.drop(columns=nominal+ordinal) train_val.head()

scaled = scaler.fit(train_val)

from sklearn.model_selection improt train_test_split X, y = train_test_split()

from sklearn.linear_model import LinearRegression model = LinearRegression()