How to normalize the Train and Test data using MinMaxScaler sklearn

Question

So, I have this doubt and have been looking for answers. So the question is when I use,

from sklearn import preprocessing
min_max_scaler = preprocessing.MinM         


        

Answer 1

Best way is train and save MinMaxScaler model and load the same when it's required.

Saving model:

df = pd.DataFrame({'A':[1,2,3,7,9,15,16,1,5,6,2,4,8,9],'B':[15,12,10,11,8,14,17,20,4,12,4,5,17,19],'C':['Y','Y','Y','Y','N','N','N','Y','N','Y','N','N','Y','Y']})
df[['A','B']] = min_max_scaler.fit_transform(df[['A','B']])  
pickle.dump(min_max_scaler, open("scaler.pkl", 'wb'))

Loading saved model:

scalerObj = pickle.load(open("scaler.pkl", 'rb'))
df_test = pd.DataFrame({'A':[25,67,24,76,23],'B':[2,54,22,75,19]})
df_test[['A','B']] = scalerObj.transform(df_test[['A','B']])

Answer 2

You should fit the MinMaxScaler using the training data and then apply the scaler on the testing data before the prediction.

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In summary:

• Step 1: fit the scaler on the TRAINING data
• Step 2: use the scaler to transform the TRAINING data
• Step 3: use the transformed training data to fit the predictive model
• Step 4: use the scaler to transform the TEST data
• Step 5: predict using the trained model (step 3) and the transformed TEST data (step 4).

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Example using your data:

from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
#training data
df = pd.DataFrame({'A':[1,2,3,7,9,15,16,1,5,6,2,4,8,9],'B':[15,12,10,11,8,14,17,20,4,12,4,5,17,19],'C':['Y','Y','Y','Y','N','N','N','Y','N','Y','N','N','Y','Y']})
#fit and transform the training data and use them for the model training
df[['A','B']] = min_max_scaler.fit_transform(df[['A','B']])
df['C'] = df['C'].apply(lambda x: 0 if x.strip()=='N' else 1)

#fit the model
model.fit(df['A','B'])

#after the model training on the transformed training data define the testing data df_test
df_test = pd.DataFrame({'A':[25,67,24,76,23],'B':[2,54,22,75,19]})

#before the prediction of the test data, ONLY APPLY the scaler on them
df_test[['A','B']] = min_max_scaler.transform(df_test[['A','B']])

#test the model
y_predicted_from_model = model.predict(df_test['A','B'])

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Example using iris data:

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import SVC

data = datasets.load_iris()
X = data.data
y = data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)

model = SVC()
model.fit(X_train_scaled, y_train)

X_test_scaled = scaler.transform(X_test)
y_pred = model.predict(X_test_scaled)

Hope this helps.

See also by post here: https://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79