import pandas as pd
from sklearn.preprocessing import StandardScaler


df = pd.read_csv('https://bit.ly/3flZJSR', delimiter=",")

# Extract input variables (all rows, all columns but last column)
X = df.values[:, :-1]

# Extract output column (all rows, last column)
Y = df.values[:, -1]

# Rescale all the input variables to be between 0 and 1
scaler = StandardScaler().fit(X)
rescaled_X = scaler.fit_transform(X)

print(rescaled_X)

1dc0a3bf08e8f1fd7081583aaaab02b2a005e5e9
<mask token>

<mask token>
print(rescaled_X)

<mask token>
df = pd.read_csv('https://bit.ly/3flZJSR', delimiter=',')
X = df.values[:, :-1]
Y = df.values[:, -1]
scaler = StandardScaler().fit(X)
rescaled_X = scaler.fit_transform(X)
print(rescaled_X)

import pandas as pd
from sklearn.preprocessing import StandardScaler
df = pd.read_csv('https://bit.ly/3flZJSR', delimiter=',')
X = df.values[:, :-1]
Y = df.values[:, -1]
scaler = StandardScaler().fit(X)
rescaled_X = scaler.fit_transform(X)
print(rescaled_X)

import pandas as pd
from sklearn.preprocessing import StandardScaler


df = pd.read_csv('https://bit.ly/3flZJSR', delimiter=",")

# Extract input variables (all rows, all columns but last column)
X = df.values[:, :-1]

# Extract output column (all rows, last column)
Y = df.values[:, -1]

# Rescale all the input variables to be between 0 and 1
scaler = StandardScaler().fit(X)
rescaled_X = scaler.fit_transform(X)

print(rescaled_X)
