Nowadays, knowing the science of data mining and data mining with Python is very necessary due to the high volume of data, and governments and organizations have realized its importance to increase their efficiency.
Learning the Python programming language is currently one of the most popular skills in the world, and mastering various Python libraries is important in most data science careers.
In fact, it can be said that it is one of the languages that is very useful in data mining science because this language has been used by all people due to its versatility and simplicity, and also this language, due to having different libraries, has caused Most programmers use this language. Therefore, in this article, we intend to fully describe data mining with Python.
It is important to know that Python data mining training courses try to explain all the methods and steps of Python data mining step by step for real projects.
Also, for people who are not familiar with Python, this language is taught briefly and the important points for preparing for data analysis with Python are explained.
Why Data Mining with Python
To solve their complex problems in various fields, data science professionals need to be familiar with a powerful programming language. Therefore, Python language has been able to achieve a good position among experts in this field due to having extensive and up-to-date libraries in the field of data sciences. Why implementation of data mining with Python has been considered:
- The simplicity of Python
- There are many different libraries in Python
- The widespread use of Python programming language in the field of data mining
- Ability to implement and use it in a variety of operating systems
Benefits of Data Mining with Python
Among the benefits of data mining are the following:
- Importing different types of data in different formats can be considered as one of the advantages of data mining with Python.
- The ability to process large volumes of data is one of the advantages of data mining with Python.
- One of the advantages of data mining with Python is statistical analysis, both simple and advanced.
- Data preprocessing is one of the advantages of data mining with Python.
- Another advantage of data mining with Python is data visualization.
- Another advantage of data mining with Python is the implementation of machine learning algorithms.
- Confusion matrix and model evaluation are other advantages of data mining with Python.
Who are the participants in the data mining course with Python?
Participants in the Python data mining course are graduates of master’s and doctoral degrees in nuclear engineering, industry, artificial intelligence, computer software, automation, and information technology management in a variety of fields such as program project management. Writing, data mining, web programming, banking systems design and analysis, business process management and scheduling.
Who is the Python data mining course suitable for?
- People who want to get acquainted with one of the most important data mining tools in a short period of time and analyze their customers’ data.
- The Python data mining course is also suitable for sales managers and marketers who want to analyze their customer data.
- Experts who work in the field of customer relationship management and intend to learn methods of analyzing customer data.
- Students and graduates who intend to use data mining science as part of their preparation to find a job in the field of customer relationship management and data mining.
As we said before, to perform data mining operations with Python, we need to get acquainted with the libraries needed in Python data mining so that we can use them to execute code. Among the libraries required for data mining with Python are the following:
This library is used in most scientific calculations in the Python programming language. In fact, the library provides tools for integrating C, C ++, and Fortran code, and is also used in Fourier transform calculations, linear algebra, and random numbers.
The Numpy library provides programmers with predefined functions of numeric routines.
Is an open source library used in mathematics, engineering and science. The application of Scipy library modules is in the field of optimization, integration, statistics, linear algebra, Fourier series as well as in differential equations. Using this library, n-dimensional arrays can be accessed.
It is one of the two-dimensional libraries used to draw diagrams in Python. This library allows programmers to quickly convert their data into graphs and graphs.
This library can also be used for simple scripts. Other uses for this library include web server applications, graphical interfaces, and lpython. This library is mostly for popular machine learning algorithms.
This library allows the user to provide information with high level structure for simple operations and data analysis.
This library is used in thematic modeling, document indexing and retrieval of similarities in large documents.
It is noteworthy that in order to use libraries in data mining with Python, they must be called before coding as follows:
<span style=“font-size: 16px;”>import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import seaborn as sns</span>
The steps for implementing data mining with Python are as follows:
Step 1: Prepare the data
The first step in implementing data mining with Python is to prepare the data. There are different ways to use different libraries depending on the type of data and the desired result. Data preparation for popular machine learning algorithms is one of the most important data mining tools with Python, which has the following applications:
- Analyze data
- Manage incomplete data
- Data normalization
- Categorize data into different types
- Introduce data to the program through the command
For example, the data of a work sample including 50 samples from 3 flower models are evaluated. The received data has 5 rows, the first 4 rows are the values and the last row is the sample class, and the order is as follows:
<span style=“font-size: 16px;”>import urllib2
url = ‘http://aima.cs.berkeley.edu/data/iris.csv’
u = urllib2.urlopen(url)
localFile = open(‘iris.csv’, ‘w’)
import genfromtxt, zeros
# read the first 4 columns
data = genfromtxt(‘iris.csv’,delimiter=‘,’,usecols=(0,1,2,3))
# read the fifth column
target = genfromtxt(‘iris.csv’,delimiter=‘,’,usecols=(4),dtype=str)
print set(target) # build a collection of unique elements
set([‘setosa’, ‘versicolor’, ‘virginica’])</span>
Step 2: Data Imaging
In order to understand what information the data provides us and how it is structured, it is an important matter in data mining that this information can be obtained with the help of illustration and graphics.
Using graphs helps us to compare the values of two different data graphically. Therefore, one of the steps in implementing data mining with Python is data visualization. For example, by writing the following command, a graph is drawn:
<span style=“font-size: 16px;”>import plot, show
plot(data[target== ‘setosa’,0],data[target ==‘setosa’,2],‘bo’)
plot(data[target== ‘versicolor’,0],data[target ==‘versicolor’,2],‘ro’)
plot(data[target== ‘virginica’,0],data[target ==‘virginica’,2],‘go’)
The graph above contains 150 dots and 3 colors that represent the classes.
Step 3: Classification and Regression
Understanding this step of implementing data mining with Python is more understandable than other steps. In this step, we first classify the data so that we can build a model from them that can be used to predict unknown categories. The following is an example of a classification code in Python:
It is necessary to know that the data classification step of the data mining implementation steps with Python has the following algorithms:
- Decision Tree
- Simple Bayes (Naïve Bayes)
- Multi Layer Perceptron Neural Network
- Support Vector Machine
- Nearest Neighbors (K-Nearest Neighbors)
- Ensemble Learning Methods
It is important to know that one of the data classification algorithms is the regression algorithm that examines the relationships between data and their modeling and the purpose of using this algorithm is to predict the value of a continuous variable according to the values of other variables. Which has 2 types:
- Linear Regression
- Logistic Regression
Step 3: Clustering
This step of the data mining implementation with Python is done automatically, which divides the data into categories that have similar members. The intended similarity varies depending on the application and the result and the type of analysis, so that in each category the members are similar and different from other categories.
The purpose of this step of implementing data mining with Python is to find a similar set of items among the input data that the number of clusters is the criterion for clustering and which cluster is more desirable depending on the algorithm And the goal is the individual.
The difference between clustering and classification is that clustering is to describe data, but classification is to create a predictive model that has the ability to classify data and to be able to predict that newly entered data Which class does it belong to? In the clustering stage, two algorithms are used:
- K-means algorithm
- DBSCAN algorithm
Step 4: Discover recurring patterns and association rules
The fourth step in implementing data mining with Python is discovering repetitive patterns and association rules. The purpose of association rules is to find items that are significantly correlated.
For example, one can examine the transaction of purchased goods to obtain a combination of goods that are usually purchased together.
To achieve this goal, the question must be answered that if a group of items are in the same transaction, which item seems to be in the same transaction with them? Therefore, the function that extracts this rule from the data is called the associative function, and the best measure of correlation is the Pearson correlation coefficient, which can be obtained by dividing the covariance of two variables. The following command clearly states the calculation method:
<span style=“font-size: 16px;”>from numpy import corrcoef
corr = corrcoef(data.T) # .T gives the transpose
The result of this command is a matrix containing correlations, the rows of which represent the variables and the columns of which are observations, and each member of which represents the correlation of the two variables. It is important to know that the correlation becomes positive when two variables grow together and becomes negative when one grows and the other decreases. But when the number of variables is high, a graph can be drawn with the following command:
<span style=“font-size: 16px;”>from pylab import pcolor, colorbar, xticks, yticks
from numpy import arrange
colorbar() # add
# arranging the names of the variables on the axis
xticks(arange(0.5,4.5),[‘sepal length’, ‘sepal width’, ‘petal length’, ‘petal width’],rotation=–20)
yticks(arange(0.5,4.5),[‘sepal length’, ‘sepal width’, ‘petal length’, ‘petal width’],rotation=–20)
The result of the above command is the following diagram:
Association rule algorithms
- Apriori algorithm
- Fp-frowth algorithm
Step 5: Model evaluation methods
The last step in the implementation of data mining with Python is model evaluation methods, which include the following:
We hope you find this article useful with Python Data Mining Tutorial.