<jupyter_start><jupyter_text>Manual<jupyter_code>x['Marketing Spend'] = x['Marketing Spend'] / x['Marketing Spend'].max()
x.head()<jupyter_output><empty_output><jupyter_text>Min Max Scaler<jupyter_code>from sklearn.preprocessing import MinMaxScaler
minmax = MinMaxScaler(feature_range=(0, 3)) #default range 0,1
minmax
df['Marketing Spend'] = minmax.fit_transform(x[['Marketing Spend']])
df.head()
df['Administration'] = minmax.fit_transform(x[['Administration']])
df['Transport'] = minmax.fit_transform(x[['Transport']])
df.head()<jupyter_output><empty_output><jupyter_text>Standard Scaler<jupyter_code>df2 = pd.read_csv('agora.csv')
df2.head()
from sklearn.preprocessing import StandardScaler
std = StandardScaler()<jupyter_output><empty_output><jupyter_text>Robust Scaler<jupyter_code>df3 = pd.read_csv('agora.csv')
df3.head()
from sklearn.preprocessing import RobustScaler
rob = RobustScaler()<jupyter_output><empty_output><jupyter_text>Max Absolute Scaler<jupyter_code>df4 = pd.read_csv('agora.csv')
df4.head()
from sklearn.preprocessing import MaxAbsScaler
rob = MaxAbsScaler()<jupyter_output><empty_output>
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