Data Preprocessing for Machine learning in Python
• 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 data set should be formatted in such a way that more than one Machine Learning and Deep Learning algorithms 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.
# Python code to Rescale data (between 0 and 1) import pandas import scipy import numpy from sklearn.preprocessing import MinMaxScaler names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] dataframe = pandas.read_csv(url, names=names) array = dataframe.values # separate array into input and output components X = array[:,0:8] Y = array[:,8] scaler = MinMaxScaler(feature_range=(0, 1)) rescaledX = scaler.fit_transform(X) # summarize transformed data numpy.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.
# Python code for binarization from sklearn.preprocessing import Binarizer import pandas import numpy names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] dataframe = pandas.read_csv(url, names=names) array = dataframe.values # separate array into input and output components X = array[:,0:8] Y = array[:,8] binarizer = Binarizer(threshold=0.0).fit(X) binaryX = binarizer.transform(X) # summarize transformed data numpy.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.
# Python code to Standardize data (0 mean, 1 stdev) from sklearn.preprocessing import StandardScaler import pandas import numpy names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] dataframe = pandas.read_csv(url, names=names) array = dataframe.values # separate array into input and output components X = array[:,0:8] Y = array[:,8] scaler = StandardScaler().fit(X) rescaledX = scaler.transform(X) # summarize transformed data numpy.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 ]]
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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