from icecream import ic
import numpy as np
from sklearn.datasets import load_iris

datasets = load_iris()
print(datasets.DESCR)
print(datasets.feature_names)

x = datasets.data
y = datasets.target

from tensorflow.keras.utils import to_categorical
y = to_categorical(y)


from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y,
                                                    train_size=0.7,
                                                    shuffle=True,
                                                    random_state=66)

from sklearn.preprocessing import MinMaxScaler, StandardScaler
# scaler = MinMaxScaler()
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
e19f020bd055cbc35106a6bbc21efbe5b3aab327
<mask token>

<mask token>
print(datasets.DESCR)
print(datasets.feature_names)
<mask token>
scaler.fit(x_train)
<mask token>

<mask token>
datasets = load_iris()
print(datasets.DESCR)
print(datasets.feature_names)
x = datasets.data
y = datasets.target
<mask token>
y = to_categorical(y)
<mask token>
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7,
    shuffle=True, random_state=66)
<mask token>
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)

from icecream import ic
import numpy as np
from sklearn.datasets import load_iris
datasets = load_iris()
print(datasets.DESCR)
print(datasets.feature_names)
x = datasets.data
y = datasets.target
from tensorflow.keras.utils import to_categorical
y = to_categorical(y)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.7,
    shuffle=True, random_state=66)
from sklearn.preprocessing import MinMaxScaler, StandardScaler
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)

from icecream import ic
import numpy as np
from sklearn.datasets import load_iris

datasets = load_iris()
print(datasets.DESCR)
print(datasets.feature_names)

x = datasets.data
y = datasets.target

from tensorflow.keras.utils import to_categorical
y = to_categorical(y)


from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y,
                                                    train_size=0.7,
                                                    shuffle=True,
                                                    random_state=66)

from sklearn.preprocessing import MinMaxScaler, StandardScaler
# scaler = MinMaxScaler()
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)