The world is full of Data. You will see many data generated every second, every hour and every day. These data are not in an organized way. It means you will get most of these data are in a raw format. It contains many errors, thus making it incomplete. As you know Data plays an important role in many factors like prediction, recommendation, e.t.c. You can make it predictable or useful by transforming the raw data into an understandable format. But to transform the data you have to know the different data preprocessing steps.
In this articles you will learn What is data preprocessing and What are the various steps you will take while doing data preprocessing. So Friends lets get started.
However before reading this post, if you are new in the machine learning field and want to Know what is it? Then Following articles will clear your doubt on machine learning.
What is Machine Learning ? An Introduction and Its Types
How to design best Machine Learning Datasets – A Complete Guide
Top 15 Datasets for Machine Learning and Statistics Projects : Must for every Data Scientist
What is Data PreProcessing in the Machine Learning?
Data Processing in the machine learning is a data mining technique. In this process, the raw data gathered and you analyze the data to find a way to transform it into useful data. Lets I am explaining to you through an example. When you search for the products in the e-commerce sites, You are basically generating the data. These data are transformed into the understandable format to get the recommended products for you.
When should you Use Data PreProcessing Steps?
Data are the fuel of technology. In the real world, you will see most of the data are noisy. It contains many errors making its unstructured data. In order to transform the unstructured data into structured data, you will use data preprocessing steps. Therefore, when you have raw data, you will definitely use data preprocessing machine learning steps. Let’s find out the best data preprocessing steps in the next section.
What are the steps in Data Preprocessing in the Machine Learning?
From the above sections, I am sure you know how the data is useful in many fields whether it is Industry sector, e-commerce sector e.t.c. Let’s know how you will do the data preprocessing.
Steps in Data Preprocessing. We will try to cover the only top four steps of data preprocessing as these are generally used.
Step -1 – Import the Libraries
In this step, you will import the following important libraries required in data preprocessing. I assume that you know Python basics as I will show you the steps in this language only. You always use the “import” keyword for importing libraries. These are the important libraries.
The code for importing all the above libraries are the following.
import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns
Let’s understand what the above libraries do.
Numpy – It is mostly used when you have to deal well with the complicated mathematical computations in the machine learning. For example, linear algebra, Fourier transform, mathematical calculations on N-dimensional Array.
Matplotlib – This library used for plotting the graphs and figures like Bar chart, Pie chart, Line chart e.t.c. It means you can create a visualization of your data by your analyzation for understanding the patterns of the data easily.
Pandas – Pandas is mostly used for data manipulations. It is an open source library. It contains all the functions related to data structures and it has all the tools for data analysis.
Seaborn – It is a data visualization library. You can say it an upgraded version of Matplotlib. It is used mostly to make graphs and charts more informatical.
Step 2 – Importing the datasets
Before you start the data preprocessing you must have datasets for it. Pandas library used to import the data sets. Mostly for small preprocessing you can easily import the data sets from the CSVs files. The format of the datasets file can be also in Html or Xlsx file. But as you already know CSVs files are low in size, therefore makes it fast importing than other formats.
Step 3 – Fill up the Missing Values in the Data Sets
When you import the datasets, then you will find there are some missing values inside it. If it is not corrected then it will be difficult for you to do preprocessing and management of the data. You will find the inaccurate information about it. Thus before data preprocessing, you have solved the missing values issues.
Replacing the missing values can be achieved by the two methods I am describing here.
1. If you have large datasets containing huge information, then you can delete the row of the data having the missing values. It will have a negligible effect on getting the accurate or predicting the output.
2. Suppose you have a numeric column in the datasets. It has null values i.e missing values. Then you can replace the missing values with calculated mean, median or mode of entire rows values of that particular column.
Step 4 – Modification of categorical or text values to numerical values.
Data Preprocessing in machine learning requires values of the data in numerical form. As you know machine learning models contains mathematical calculations, therefore you have to convert all the text values in the columns of data sets into numerical form. The LabelEncoder() class used to transform the categorical or string variable into the Numerical Values.
Other Steps in Data PreProcessing in the Machine Learning.
The above steps I have described are the top major steps you will take in preprocessing the data. But there are also other steps that are Creation of Traning and Test data sets and Feature Scaling. I will not cover this steps for making this article short.
Data is the fuel of the future. It is growing exponentially. Most of the data are Unstructured and we set some rules for converting it into useful data. That’s why data preprocessing came into existence. Try to apply the above steps while doing your machine learning projects. You will definitely find it very interesting and also boost your confidence.
At last, if you have any doubt or suggestion please contact us or comment below. We are always available to help you.
Data Science Learner Team
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