# Rescaling Data for Machine Learning in Python with Scikit-Learn

Your data must be prepared before you can build models. The data preparation process can involve three steps: data selection, data preprocessing and data transformation.

In this post you will discover two simple data transformation methods you can apply to your data in Python using scikit-learn.

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Data Rescaling
Photo by Quinn Dombrowski, some rights reserved.

## Data Rescaling

Your preprocessed data may contain attributes with a mixtures of scales for various quantities such as dollars, kilograms and sales volume.

Many machine learning methods expect or are more effective if the data attributes have the same scale. Two popular data scaling methods are normalization and standardization.

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## Data Normalization

Normalization refers to rescaling real valued numeric attributes into the range 0 and 1.

It is useful to scale the input attributes for a model that relies on the magnitude of values, such as distance measures used in k-nearest neighbors and in the preparation of coefficients in regression.

The example below demonstrate data normalization of the Iris flowers dataset.

For more information see the normalize function in the APIÂ documentation.

## Data Standardization

Standardization refers to shifting the distribution of each attribute to have a mean of zero and a standard deviation of one (unit variance).

It is useful to standardize attributes for a model that relies on the distribution of attributes such as Gaussian processes.

The example below demonstrate data standardizationÂ of the Iris flowers dataset.

For more information see the scale function in the API documentation.

## Tip: Which Method To Use

It is hard to know whether rescaling your data will improve the performance of your algorithms before you apply them. If often can, but not always.

A good tip is to create rescaled copies of your dataset and race them against each other using your test harness and a handful of algorithms you want to spot check. This can quickly highlight the benefits (or lack there of) of rescaling your data with given models, and which rescaling method may be worthy of further investigation.

## Summary

Data rescaling is an important part of data preparation before applying machine learning algorithms.

In this post you discovered where data rescaling fits into the process of applied machine learning and two methods: Normalization and Standardization that you can use to rescale your data in Python using the scikit-learn library.

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### 100 Responses to Rescaling Data for Machine Learning in Python with Scikit-Learn

1. Matt July 10, 2016 at 7:13 pm #

Hi thank you for this nice article ! I have a quick question (maybe long to explain but I think the answer is short đź™‚ )
I am using data standardization for a k-NN algorithm. I will have new instances and I’ll have to determine their class, their data won’t be standardized.
Should I :
1) Standardize my training set with the scale function. Then manually calculate the means and the std of my training set to standardize my new vector.
2) Add the new data to the training set and then standardize the set with the mean function.
3) Neither
Thank you !

2. Tzu-Yen June 16, 2017 at 6:50 am #

I came across your website which is extremely helpful for studying machine learning. Thanks for the great effort.

Just a friendly reminder. The normalization function has an axis parameter with a default value equals to 1, so it will run on rows/data by default. For feature normalization, you need to set axis = 0.

• Jason Brownlee June 16, 2017 at 8:12 am #

Thanks Tzu-Yen!

• Abel August 17, 2017 at 7:09 am #

Hey Tzu-Yen, you saved my day…. Thanks a lot

3. Shud November 1, 2017 at 4:00 pm #

Hi Jason,

May i know how to bring the data back to original scale? I need my predictions in original scale. I normalised by data and tried .inverse_transform(data) to get back my original data. But it gave me an error – AttributeError: ‘Normalizer’ object has no attribute ‘inverse_transform’
Any kind of help would be appreciated.

4. Sumit November 17, 2017 at 3:37 pm #

Hi Jason,
I have one question.
Suppose today my data range is 5000 to 10000 which will be scaled between 0 to 1
And tomorrow if new data entry comes with 10500. if I use same 5000 to 10000 range for fitting then it produce output X1 and and If i specify 5000 to 10500 range then it produce output X2 which is not equal to X1.

How to over come this? How to handle new data with old range?

Thanks & Regards
Sumit

• Jason Brownlee November 18, 2017 at 10:12 am #

You can estimate the expected range of data in the future and those min/max to scale.

Or, you can estimate a mean/stdev and standardize instead, if the data is Gaussian.

5. Rizwan Mian January 1, 2018 at 10:11 am #

Hi Jason, when I use standardization as suggested in the post, I see mean and standard deviation very close to zero and one, respectively…but not exactly. Wonder if such close-enough values are acceptable in the community?

count 7.68E+02
mean -6.48E-17
std 1.00E+00
min -1.14E+00
25% -8.45E-01
50% -2.51E-01
75% 6.40E-01
max 3.91E+00

6. Suranga April 8, 2018 at 11:25 pm #

Hi , can you tell me the formula behind preprocessing.scale()?

7. neethu May 16, 2018 at 9:05 pm #

While am training the data set am getting an accuracy around 100%.But when am testing am not getting the proper answer.what could be the reason?can you please help me with it

• Jason Brownlee May 17, 2018 at 6:31 am #

Sounds like you are overfitting the training dataset.

8. Hagais June 27, 2018 at 6:01 pm #

hai Mr. Jason Brownlee, I wanna ask about how to prints the values back in their original scale using normalizer

• Jason Brownlee June 28, 2018 at 6:14 am #

You can invert the scaling transforms, call scaler.invert_transform()

9. Nitin vij July 18, 2018 at 8:27 pm #

Hi Jason,

I tried to use standardized_X = preprocessing.scale(X).
I checked with print(np.mean(standardized_X[:,0])) but mean is not zero (for any of the four columns) although standard deviation is one. Am I doing something incorrectly.

• Jason Brownlee July 19, 2018 at 7:49 am #

I’m surprised. I would expect it to be zero, perhaps confirm that you have not introduced a bug or typo?

10. vivek September 13, 2018 at 11:29 pm #

If i have target values in different range for prediction using regression with deep neural network will it be helpful to get better accuracy by doing normalization of target values?If yes, then which technique i should use for that.

• Jason Brownlee September 14, 2018 at 6:37 am #

Yes, perhaps try normalize and standardize and see which results in better skill compared to no rescaling.

11. Abhishek Shankar September 19, 2018 at 4:43 am #

When to normalize and when to standardize ??

• Jason Brownlee September 19, 2018 at 6:27 am #

Normalize when the data variables have different units.

Standardize when a variable has a gaussian distribution.

12. olufemi george January 1, 2019 at 2:01 am #

i have a dummy question;

there were 2 lines in your code.

X = iris.data
y = iris.target

how does X and Y know what the dependent and independent variables are? Will this work on my own dataset, without me having to tell it ( doubt it).

Thanks

13. hannah February 19, 2019 at 7:52 pm #

Hi Jason,

First, I would like to say that I really appreciate your blog posts, they’re helping me a lot!

I have a problem with reproducing a normalization/standardization from the article. The authors wrote that “(…)Magnitudes are scaled logarithmically. The features are normalized per frequency band to zero mean and unit variance.” – do they mean standardize, so is it enought to simply use preprocessing.scale ?

To make it more confusing, I found another group which is reproducing the results from the first one. They wrote that “The logarithm of the normalized sum magnitude of the
filter bank energies is computed for each window. These features were normalized to range between 0 and 1 before feeding to the network input.” And for me that looks like normal normalization.

Is it the same process described? I doubt…

Thanks a lot for your help!

Hannah

• Jason Brownlee February 20, 2019 at 8:00 am #

Perhaps contact the authors directly and ask exactly what they did?

Unless academics release code used to produce the results, their papers are a waste of time in the best case or fraud in the worst case.

14. Murali krishna March 25, 2019 at 1:16 pm #

Hi Jason,

A quick question on standardization, lets say I have built a model on a selected sample data from entire population and standardized the values before running through a model. So, can I directly use these beta coefficients on the entire population or since I have found beta coefficients by standardizing the values should I standardize all the values in the entire population.

• Jason Brownlee March 25, 2019 at 2:18 pm #

I recommend estimating the coefficients from the training set and using them on all data going forward.

15. Murali March 25, 2019 at 5:21 pm #

Hey thanks for the reply, can you please elaborate on why should we be using estimates coefficients from training set and use them on all the data

• Jason Brownlee March 26, 2019 at 8:02 am #

If we estimate the distribution from all data, then evaluate the performance of the model on a subset of that data, we will be subject to data leakage and the results will be optimistic – we are using knowledge out side of the scope of the test.

16. Anna May 20, 2019 at 1:03 pm #

I am trying to code LSTM for household_power_consumption_days.csv data by using Pytorch. So, should I normalize or rescale data?

• Jason Brownlee May 20, 2019 at 2:37 pm #

Sorry, I don’t have any examples for Pytorch.

Scaling data prior to modeling is a good practice.

17. Liten May 29, 2019 at 12:49 am #

Hello Jason,

In order to train an SVM classifier, should the data be scaled to [0,1 ] or [-1, 1]?

• Jason Brownlee May 29, 2019 at 8:45 am #

The targets need to be {-1, 1} I believe. But sklearn will do this for you – from memory

18. MWh August 15, 2019 at 9:46 pm #

Thanks Jason,

Do i need normalising/scaling if i only have 1 feature?
My data has x and y only, where y is the dependent variable. I am working on Random forest regression.

19. jeff August 15, 2019 at 10:53 pm #

How can I normalize a dataset with text values to numbers properly in sklearn? and my dataset have train and test part and Im getting different number of columns after normalization. How can I normalize them eqally?

20. Bilal August 27, 2019 at 7:17 pm #

is normalization required for Decision Tree Algorithm

21. Amit Yadav September 24, 2019 at 7:12 pm #

Thank you very much for writing this article. When to use normalisation and when to use Standardisation ?, I went through an article they said “We can use Normalisation if we want to rescale every observation of dataset, We can use standardisation if we want to rescale by features in data sets.

• Jason Brownlee September 25, 2019 at 5:56 am #

I’m happy that it helped.

Normalize generally, and use standardization when a variable is gaussian.

If in doubt, test both and use whichever results in a model with the best skill.

22. Marcos Cesar M. Pablos November 3, 2019 at 6:09 am #

Hello Jason, thanks for all information, and, lets see if you can help me.

I do the scaling in my predictors, and when I do the prediction, the result comes out in scientific notation, researching I saw that there is a way to do an inverse_transform, reverse the scaling process, I tried, but I failed to successfully reverse, can you help me?
Below is the test code.

import pandas as pd
import numpy as np
X = base.iloc[:, 3:19].values
y = base.iloc[:, 2].values

from sklearn.model_selection import train_test_split
X_treinamento, X_teste, y_treinamento, y_teste = train_test_split(X, y,
test_size = 0.3,
random_state = 0)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_treinamento = scaler.fit_transform(X_treinamento)

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

poly = PolynomialFeatures(degree=4)
X_treinamento_poly = poly.fit_transform(X_treinamento)
X_teste_poly = poly.transform(X_teste)

regressor = LinearRegression()
regressor.fit(X_treinamento_poly, y_treinamento)
score = regressor.score(X_treinamento_poly, y_treinamento)

previsoes = regressor.predict(X_teste_poly)

previsoes = scaler.inverse_transform(previsoes) = is not working.

23. Marcos Cesar M. Pablos November 3, 2019 at 8:26 am #

Hello Jason,
Thanks for the tip, reading what you told me, I saw that I can inverse_transform, but applying I have an error, which seems to be basic and easy to solve, but I’m not getting, it gives the error:

ValueError: operands could not be broadcast together with shapes (6484,) (16,) (6484,)

I tried to apply the reshape to the predictions variable, but it doesn’t work at all.

any suggestion?

• Jason Brownlee November 4, 2019 at 6:35 am #

The shape of the data and order of the columns in the data must be identical when calling transform() and inverse_transform().

Perhaps check this.

24. Marcos Cesar M. Pablos November 4, 2019 at 9:20 am #

Got It, now is working, thanks!

import pandas as pd

X = base.iloc[:, 3:19].values
y = base.iloc[:, 2:3].values

from sklearn.preprocessing import StandardScaler
scaler_x = StandardScaler()
X = scaler_x.fit_transform(X)
scaler_y = StandardScaler()
y = scaler_y.fit_transform(y)

from sklearn.model_selection import train_test_split
X_treinamento, X_teste, y_treinamento, y_teste = train_test_split(X, y,
test_size = 0.3,
random_state = 0)

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

poly = PolynomialFeatures(degree=4)
X_treinamento_poly = poly.fit_transform(X_treinamento)
X_teste_poly = poly.transform(X_teste)

regressor = LinearRegression()
regressor.fit(X_treinamento_poly, y_treinamento)
score = regressor.score(X_treinamento_poly, y_treinamento)

# previsĂłes com o scalonamento reverso
previsoes1 = scaler_y.inverse_transform(regressor.predict(X_teste_poly))

y_teste = scaler_y.inverse_transform(y_teste)

from sklearn.metrics import mean_absolute_error
mae = mean_absolute_error(y_teste, previsoes1)

#Testando com o y_teste e previsĂłes ainda com scalonamento
previsoes = regressor.predict(X_teste_poly)

scaler_teste = StandardScaler()
y_teste = scaler_teste.fit_transform(y_teste)

from sklearn.metrics import mean_absolute_error
mae = mean_absolute_error(y_teste, previsoes)

25. Sia November 12, 2019 at 12:47 am #

Sir how I get the mean=0 and standard deviation =1 for a given dataset in Python?

26. Vibhaas January 20, 2020 at 11:07 pm #

Good article Jason. I learned from few points from your post and taken it forward by implementing ML model to compare the impact. here is the article https://medium.com/@vibhaas.kotwal/feature-scaling-8c92bdd080a1

• Jason Brownlee January 21, 2020 at 7:13 am #

Thanks.

Sorry, I don’t have the capacity to review your piece.

27. Rana January 29, 2020 at 12:19 am #

Sir,
I get intercept_ =3.1378, 93 support_vectors_ and 93 dual_coef_ then,
how can i get the hyperplane equation of polynomial SVM in python.
Thank you.

• Jason Brownlee January 29, 2020 at 6:39 am #

I believe you can retrieve all coefficients from the sklearn API.

28. Akram March 31, 2020 at 8:53 am #

Dear Jason,
I have a csv dataset which need to be normalized. However, I just need to normalize all columns except target, how can I perform it?

import pandas as pd
from sklearn import preprocessing

min_max_scaler = preprocessing.MinMaxScaler()
np_scaled = min_max_scaler.fit_transform(data)
df_normalized = pd.DataFrame(np_scaled)
df_normalized = df_normalized.to_csv(Norm_File.csv’,header=True, index=False)

Thanks a lot.

• Jason Brownlee March 31, 2020 at 1:33 pm #

Not sure you can use the scaler directly on dataframes, perhaps extract the numpy array from them first?

29. Nhu April 18, 2020 at 7:12 pm #

hi

• Jason Brownlee April 19, 2020 at 5:53 am #

30. Nhu April 18, 2020 at 7:21 pm #

hi

i have a dataset
inlude 3 columns
receney frequency monetary
so i can preprocessing date by Standardize or Normalize? (i use dataset for Kmeans )
pls help me. sorry my english not good
Thank u so much

31. nhu April 18, 2020 at 7:25 pm #

i am only start to learn
i wroten it
pls can u help me check my code is right or wrong
i did preprocessing
***********
from sklearn.cluster import KMeans
import pandas as pd
import matplotlib.pyplot as plt

sse = {}

#load our data from CSV
tx_user = pd.read_csv(‘rfm_data.csv’, sep =’,’ , engine=’python’)

# display(tx_user[[‘M’]].boxplot())
#PRE-PROCESSING ———————————————–
col_names = [‘R’,’F’, ‘M’]

#Step 1: Rescale Data
#from sklearn.preprocessing import MinMaxScaler
#min_max_scaler = MinMaxScaler()
#tx_user[col_names] = min_max_scaler.fit_transform(tx_user[col_names])

#Step 2: Standardize Data
from sklearn.preprocessing import StandardScaler
standard_scaler = StandardScaler()
tx_user[col_names]=pd.read_csv(‘rfm_data.csv’, sep =’,’ , engine=’python’)
tx_user[col_names] = standard_scaler.fit_transform(tx_user[col_names])

#Step 3: Normalize Data
#from sklearn.preprocessing import Normalizer
#normalizer = Normalizer()
#tx_user[col_names] = normalizer.fit_transform(tx_user[col_names])

#print(‘Descriptive statistic of preprocessed data: ‘)
#display(tx_user.describe())
#END OF PRE-PROCESSING ——————————————-

for k in range(1, 10):
kmeans = KMeans(n_clusters=k, max_iter=1000).fit(tx_user[[‘R’,’F’, ‘M’]])
tx_user[“clusters”] = kmeans.labels_
sse[k] = kmeans.inertia_
print(‘\n \n Sum of squared distances of samples to their closest cluster center: \n’)

df_sse = pd.DataFrame(sse.items(), columns = [‘K Cluster’,’Sum of Squared Errors’])
display(df_sse)

keys = list(sse.keys())
values = list(sse.values())

plt.figure()
plt.plot(keys, values)
plt.xlabel(“Number of cluster – Kmean on dataraw _ group by”)

# Add title and axis names
plt.title(‘Within-Cluster-Sum of Squared Errors (WSS) for different values of k’)
plt.xlabel(‘K cluster’)
plt.ylabel(‘Sum of Squared Errors (WSS)’)

plt.show()

32. Nhu April 19, 2020 at 1:56 pm #

I want to cluster customers. I have used 2 methods for the same data set. a method I code manually using RFM model in economics. Another method I use clustering using the Kmeans algorithm. Now I want to compare which method is better. But I had trouble. I still haven’t figured out which method will give better results

• Jason Brownlee April 20, 2020 at 5:22 am #

Select a metric, design a test harness, then apply both methods in the test harness to see which does better on your metric.

33. Nhu April 20, 2020 at 7:28 pm #

Thank u so much

34. Nhu April 27, 2020 at 5:12 am #

Hi
After I have clustered “customer segmentation the clients, I want to visualization those clusters. What I have to do

• Jason Brownlee April 27, 2020 at 5:40 am #

Perhaps try pair-wise scatter plots?

35. Nhu April 28, 2020 at 3:22 am #

yes yes yes.i want pair-wise scatter plots

36. Nhu April 28, 2020 at 3:25 am #

i was code . but i got “AttributeError: ‘KMeans’ object has no attribute ‘labels’
“. but i still can not fix
# Modules
import matplotlib.pyplot as plt
from matplotlib.image import imread
import pandas as pd
import seaborn as sns
from sklearn.datasets.samples_generator import (make_blobs,
make_circles,
make_moons)
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import silhouette_samples, silhouette_score

%matplotlib inline
sns.set_context(‘notebook’)
plt.style.use(‘fivethirtyeight’)
from warnings import filterwarnings
filterwarnings(‘ignore’)
# Import the data

# Plot the data
plt.figure(figsize=(6, 6))
plt.scatter(df.iloc[:, 0], df.iloc[:, 1])
plt.xlabel(‘Eruption time in mins’)
plt.ylabel(‘Waiting time to next eruption’)
plt.title(‘Visualization of raw data’);

# Standardize the data
X_std = StandardScaler().fit_transform(df)

# Run local implementation of kmeans
def cluster(n_clusters):
km = Kmeans(n_clusters=2, max_iter=100)
km.fit(X_std)
centroids = km.centroids

# Plot the clustered data
fig, ax = plt.subplots(figsize=(6, 6))
plt.scatter(X_std[km.labels == 0, 0], X_std[km.labels == 0, 1],
c=’green’, label=’cluster 1′)
plt.scatter(X_std[km.labels == 1, 0], X_std[km.labels == 1, 1],
c=’blue’, label=’cluster 2′)
plt.scatter(centroids[:, 0], centroids[:, 1], marker=’*’, s=300,
c=’r’, label=’centroid’)
plt.legend()
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.xlabel(‘Eruption time in mins’)
plt.ylabel(‘Waiting time to next eruption’)
plt.title(‘Visualization of clustered data’, fontweight=’bold’)
ax.set_aspect(‘equal’);

37. Nhu April 30, 2020 at 1:40 am #

thank u so much so much

38. meems May 19, 2020 at 1:45 am #

hello i am new to all these but I was given a task I am not sure how to do
Normalize data with pandas:
a. Subtract the mean value of each feature from the dataset.
b. After subtracting the mean, additionally scale (divide) the feature values by their
respective â€śstandard deviations.â€ť
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import preprocessing
#step 1
col_names = [“Size”,”Bedrooms”,”Price”]#name cols
#importing data
df2 = pd.read_csv(“dataset2.txt”, header = None,skiprows=0, names= col_names)
#print first 5 elements of Dataframe
print(df2.describe())#show some stats

I have no idea how to subtract means and the std please can you show me how

39. Malik Elam June 8, 2020 at 4:23 am #

Do you recommend using Sigmoid function transformation of input feature to get outliers closer to the bulk of other values, given that outliers are extreme values that are not errors. I.e. -1+2/(1+e^-ax); this would replace outliers and standardize data.

• Jason Brownlee June 8, 2020 at 6:19 am #

Not really. Perhaps try it and see if it is appropriate for your data/model/project.

40. sarah June 10, 2020 at 2:13 am #

Hi Jason,

Do you have a tutorial on how to normalize/scale multivariate time series data?

Thanks for all of your valuable guides,

41. Bilal July 13, 2020 at 1:35 pm #

Hi,
Please tell me the formula behind the preprocessing.normalize()

42. Dmitry August 13, 2020 at 2:25 am #

What I don’t understand is how to unscale predicted dataset since it has different dimensions than the training features dataset?

• Jason Brownlee August 13, 2020 at 6:19 am #

Use the scaler object and call the inverse_transform() function and pass in the predictions.

The scaler for the target takes one column for y or yhat – the same dimensions.

43. Jaydeep Chauhan October 9, 2020 at 11:16 am #

Hi all,
Can you please help me to understand how the mean files are calculated in the following repository?
I m getting different values for the same dataset.

https://github.com/Veleslavia/EUSIPCO2017/tree/master/means

• Jason Brownlee October 9, 2020 at 1:46 pm #

I recommend contacting the author directly.

44. Yao October 13, 2020 at 4:52 pm #

Hi Jason,
Thank you so much for the post and I found it very helpful.

I’ve got a question on how to use scaler. For example, I’m working on a regression problem and I have my input X (10 columns,100k rows) and target y (1 column, 100k rows) as my training dataset. I used two StandardScaler X_scaler and y_scaler to scale X and y respectively. Since I used scaled X and y for model fitting, I would expect the prediction result y_hat is scaled as well. For training I can easily inverse my prediction to its original scale since I have the y_scaler. However, for real prediction, I only have my new dataset X’. If I scale my X’ and put them into my model, I would expect to get a scaled prediction. But how can I inverse my prediction to its original scale since it may differ from the training y?

Could you please help me with this? Thank you so much!

• Jason Brownlee October 14, 2020 at 6:13 am #

You’re welcome.

Yes, you must scale all new data in the same way as the training data. e.g. input data. You can also scale the target in training data and the model will learn to predict scaled targets. You can then invert the transform on the predictions to get the original scale.

You must ensure your training dataset is sufficiently representative of the data so that the model learns the problem and that the transform captures the scale of the data. If this is challenging, you can manage the scaling/transform of the data manually (e.g. clip values out of range in new data)

Does that help?

45. Fatima March 20, 2021 at 12:35 pm #

Hi Dr Jason,, here in Data Normalization example to
normalize the data attributes
normalized_X = preprocessing.normalize(X)

what the function or the rule that the normalize submit to apply the normalization ( rescaling) can I know the detailed information about this technique ..

thanks alot

• Jason Brownlee March 21, 2021 at 6:05 am #

Use normalization if it results in better model performance than not using it. That is the very best rule.

46. San May 11, 2021 at 2:34 am #

I’ve a dataset where I’ve to normalize as data is in different scales. Now, is there a way to get back to the original data i.e., kinda denormalizing and going back to the data with different scales ?

• Jason Brownlee May 11, 2021 at 6:44 am #

Yes, you can invert the transform using the scaler objects directly, e.g. scaler.inverse_transform()

47. Hammed May 13, 2021 at 10:06 am #

Good day, my brother and I love your book and how you simplified a lot of things in it.

So my question is on Normalization of dataset. I have a large dataset, which has an amount column that goes literarily from -574617714.32 to 600000000.0 and about 10 million transactions also (and this is just the sample).

I normalized the amount and also used categorical encoder on some other features. But my problem arise when I want to predict the outcome of a new data which has not been normalized or encoded. For encoding the data, I used a dictionary to store the encoded values which I then use to swap out the values in the new data to be predicted with the corresponding values in the dictionary.

If I am to normalize the amount with sklearn, it would only normalize on the new data to be predicted not taking into account the earlier minmax, If I add two rows to indicate min and max, the results are still different.

I tried using “MaxAbsScaler().fit(dataset[[‘AMOUNT’]]).max_abs_” to retrieve the fit parameter and store in either json or csv, then use it to transform the amount colum of the new data, still did not work.

My question, how do I store the parameters used in the normalization of the train data, so I can use that to transform the new data to be predicted.

Or do I just use this as you showed in one of your books

“dataset[‘AMOUNT’].apply(lambda x: (x – minmax[0]) / (minmax[1] – minmax[0]))”

and then I define min and max. I am not actually confident with this as the changes in the normalized amount is rather insignificant, compared to that of MaxAbsScaler.

Another question, as the column contain amount and its contents varies from negative to positive, normalization or standardization, which is best for optimum result?

Thanks.

48. Hammed May 14, 2021 at 8:32 pm #

Thank you, would try it out.

49. Ayesha July 14, 2021 at 5:11 am #

Hi, Great article,
I need a bit guidance regarding scaling.
I am training neural net. I used scaling in this way:

from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(x_train)
X_train = scaler.transform(x_train)
X_test=scaler.transform(Xv)
scalerY = preprocessing.StandardScaler().fit(y_train.values.reshape(-1, 1))
Y_train = scalerY.transform(y_train.values.reshape(-1, 1))
Y_test=scalerY.transform(yv.values.reshape(-1, 1))

But I am getting confused that whether it is right or wrong technique. This way of scaling provides me better results but when I scale data using StandardScaler, I got very worse results. Can you please guide me in this regard? Thanks

• Jason Brownlee July 14, 2021 at 5:31 am #

Generally, you must fit the scaler object on the training set then apply to the train and test sets to avoid data leakage.