ML | Data Preprocessing in Python - GeeksforGeeks

15 Jul, 2021
Pre-processing refers to the transformations applied to our data before feeding it to the algorithm. Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis.
Need of Data Preprocessing 
For achieving better results from the applied model in Machine Learning projects the format of the data has to be in a proper manner. Some specified Machine Learning model needs information in a specified format, for example, Random Forest algorithm does not support null values, therefore to execute random forest algorithm null values have to be managed from the original raw data set.
Another aspect is that the data set should be formatted in such a way that more than one Machine Learning and Deep Learning algorithm are executed in one data set, and best out of them is chosen.
This article contains 3 different data preprocessing techniques for machine learning. 
The Pima Indian diabetes dataset is used in each technique. This is a binary classification problem where all of the attributes are numeric and have different scales. It is a great example of a dataset that can benefit from pre-processing. You can find this dataset on the UCI Machine Learning Repository webpage. Note that the program might not run on Geeksforgeeks IDE, but it can run easily on your local python interpreter, provided, you have installed the required libraries.  
1. Rescale Data  
When our data is comprised of attributes with varying scales, many machine learning algorithms can benefit from rescaling the attributes to all have the same scale.
This is useful for optimization algorithms in used in the core of machine learning algorithms like gradient descent.
It is also useful for algorithms that weight inputs like regression and neural networks and algorithms that use distance measures like K-Nearest Neighbors.
We can rescale your data using scikit-learn using the MinMaxScaler class.
Code: Python code to Rescale data (between 0 and 1)  
Python
# importing librariesimport pandasimport scipyimport numpyfrom sklearn.preprocessing import MinMaxScaler # data set linkurl = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"# data parametersnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] # preparating of dataframe using the data at given link and defined columns listdataframe = pandas.read_csv(url, names = names)array = dataframe.values # separate array into input and output componentsX = array[:,0:8]Y = array[:,8] # initialising the MinMaxScalerscaler = MinMaxScaler(feature_range=(0, 1))# learning the statistical parameters for each of the data and transformingrescaledX = scaler.fit_transform(X) # summarize transformed datanumpy.set_printoptions(precision=3)print(rescaledX[0:5,:])
After rescaling see that all of the values are in the range between 0 and 1. 
Output: 
[[ 0.353  0.744  0.59   0.354  0.0    0.501  0.234  0.483]
 [ 0.059  0.427  0.541  0.293  0.0    0.396  0.117  0.167]
 [ 0.471  0.92   0.525  0.     0.0    0.347  0.254  0.183]
 [ 0.059  0.447  0.541  0.232  0.111  0.419  0.038  0.0  ]
 [ 0.0    0.688  0.328  0.354  0.199  0.642  0.944  0.2  ]]
2. Binarize Data (Make Binary)  
We can transform our data using a binary threshold. All values above the threshold are marked 1 and all equal to or below are marked as 0.
This is called binarizing your data or threshold your data. It can be useful when you have probabilities that you want to make crisp values. It is also useful when feature engineering and you want to add new features that indicate something meaningful.
We can create new binary attributes in Python using scikit-learn with the Binarizer class.
Code: Python code for binarization  
Python
# import librariesfrom sklearn.preprocessing import Binarizerimport pandasimport numpy # data set linkurl = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"# data parametersnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] # preparating of dataframe using the data at given link and defined columns listdataframe = pandas.read_csv(url, names = names)array = dataframe.values # separate array into input and output componentsX = array[:, 0:8]Y = array[:, 8]binarizer = Binarizer(threshold = 0.0).fit(X)binaryX = binarizer.transform(X) # summarize transformed datanumpy.set_printoptions(precision = 3)print(binaryX[0:5,:])
We can see that all values equal or less than 0 are marked 0 and all of those above 0 are marked 1. 
Output: 
[[ 1.  1.  1.  1.  0.  1.  1.  1.]
 [ 1.  1.  1.  1.  0.  1.  1.  1.]
 [ 1.  1.  1.  0.  0.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.]]
3. Standardize Data  
Standardization is a useful technique to transform attributes with a Gaussian distribution and differing means and standard deviations to a standard Gaussian distribution with a mean of 0 and a standard deviation of 1.
We can standardize data using scikit-learn with the StandardScaler class.
Code: Python code to Standardize data (0 mean, 1 stdev)  
Python
# importing librariesfrom sklearn.preprocessing import StandardScalerimport pandasimport numpy # data set linkurl = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"# data parametersnames = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] # preparating of dataframe using the data at given link and defined columns listdataframe = pandas.read_csv(url, names = names)array = dataframe.values # separate array into input and output componentsX = array[:, 0:8]Y = array[:, 8]scaler = StandardScaler().fit(X)rescaledX = scaler.transform(X) # summarize transformed datanumpy.set_printoptions(precision = 3)print(rescaledX[0:5,:])
The values for each attribute now have a mean value of 0 and a standard deviation of 1. 
Output: 
[[ 0.64   0.848  0.15   0.907 -0.693  0.204  0.468  1.426]
 [-0.845 -1.123 -0.161  0.531 -0.693 -0.684 -0.365 -0.191]
 [ 1.234  1.944 -0.264 -1.288 -0.693 -1.103  0.604 -0.106]
 [-0.845 -0.998 -0.161  0.155  0.123 -0.494 -0.921 -1.042]
 [-1.142  0.504 -1.505  0.907  0.766  1.41   5.485 -0.02 ]]
References:  
https://www.analyticsvidhya.com/blog/2016/07/practical-guide-data-preprocessing-python-scikit-learn/
https://www.xenonstack.com/blog/data-preprocessing-data-wrangling-in-machine-learning-deep-learning
 
anikakapoor
Advanced Computer Subject
Machine Learning
Python
Machine Learning
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