Automated Data Mining In Python

Automated Data Mining In Python

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In this article, we will learn Automated data mining in Python.

In Data Science, data mining is one of the most important processes. It is crucial to mine the data in order to get actionable insights from it and make business decisions.

In the realm of Data Science, deleting excessive and unavailable data portions and concentrating on the accurate and correct data is advantageous and often essential.

What is Automated Data Mining in Python?

Automated data mining in Python is a way to get useful information from large data sets by using a number of different computation techniques. It is a tool for understanding the structure and content of data and finding patterns and trends that can help you make better decisions.

Data mining with Python, is one of the most popular data mining technologies, including data cleaning, data organizing, and the application of machine learning algorithms.

In addition, it can be a vague and scary task for data scientists because it requires a wide range of skills and knowledge of a lot of different data mining techniques in order to take raw data and successfully get insights from it.

Python provides an extensive collection of scientific computing libraries; thus, it is one of the most popular and effective tools for data mining. Data Mining consists of:

  1. Visualizing the data
  2. Classifying the data
  3. Discovering the relationship between the data
  4. Reducing the data
  5. Analyzing the data

Benefits of Automated Data Mining in Python

  1. It may assist you in discovering links and patterns in your data that you would not have been able to discover manually.
  2. It may help you detect anomalies in your data that were previously hidden.
  3. It may assist you in discovering patterns in your data that you may not have been able to discover yourself.
  4. It may assist you in discovering correlations in your data that you may not have been able to discover manually.
  5. It may assist you in discovering relationships in your data that you may not have been able to discover yourself.

Data Mining Different Techniques

Data mining is the extraction of useful information from large data sets. The objective of data mining is to identify patterns and correlations in the data that may be utilized to enhance business choices or anticipate future occurrences.

There are several techniques that may be used for data mining, such as:

  • Clustering
  • Association rules
  • Sequential pattern mining
  • Classification
  • Regression

Also read: How Python Lambda Sorted with Advanced Examples

Different Software Packages That Can Be Used For Automated Data Mining

Automated data mining is the process of using software to find patterns and connections in large sets of data. Automated data mining can be used to help businesses make better decisions or to guess what will happen in the future.

There are various software programs that may be used for automated data mining, such as:

  • SAS
  • SPSS
  • RapidMiner
  • KNIME

Each of these software programs has unique advantages and disadvantages. It is essential to pick the software application that is optimal for the given work.

Tools and Libraries For Automated Data Mining

There are a various of tools and libraries for automated data mining.

For data pre-processing, data cleaning, and feature engineering, there are the following tools:

  • Pandas
  • Scikit-learn
  • NLTK

In data mining, there are the following tools:

  • XGBoost
  • TensorFlow
  • Scikit-learn
  • Spark
  • NumPy

For data visualization, there are the following tools:

  • Matplotlib
  • Seaborn
  • Bokeh

Creating a regression model in Python

Regression is a way to figure out how variables work together, which can be used to make predictions. With linear regression analysis, the value of one variable is predicted based on the value of another variable.

The thing you want to make a prediction about is called the “dependent variable.” The variable whose value you want to predictive modeling is called the “independent variable.”

To understand, we can build a synthetic dataset and perform data regression over it:

from numpy.random import rand
x = rand(50,1) #independent variable
y = x*x*x+rand(50,1)/5 #dependant variable

We can now use the Linear Regression model from the sklear.linear model package. This model figures out the best line to fit the data by minimizing the sum of the squares of the vertical differences between each data point and the line.

from sklearn.linear_model import LinearRegression
linreg = LinearRegression()
linreg.fit(x,y)

We can plot this line over the data points as,

from numpy import linspace, matrix
import matplotlib.pyplot as plt
xx = linspace(0,1,40)
plt.plot(x,y,'o',xx,linreg.predict(matrix(xx).T),'--r')
plt.show()

From the graph, we can see that the line goes through the middle of our data and shows that the trend is going up.

With mean squared error, we can figure out how accurate a prediction is. This metric measures the expected squared distance between what was predicted and what was actually true. When the prediction is right, the value is 0.

from sklearn.metrics import mean_squared_error
print(mean_squared_error(linreg.predict(x),y))

#Output
0.014552409677571747

Creating a Clustering Model in Python

K-Means Cluster models function as follows:

  1. Start with a set of k randomly selected centroids (the supposed centers of the k clusters).
  2. Determine which observation belongs to which cluster based on its proximity to the centroid (using the squared Euclidean distance: pj=1(xijxi′j)2, where p is the number of dimensions).
  3. Recalculate the cluster centroids by reducing the squared Euclidean distance to each cluster observation.
  4. Repeat steps 2. and 3. until neither the members of the clusters nor the positions of their centroids change.

Exploratory Data Analysis

You must install a few modules, including the new Sci-kit Learn module, which is a set of tools for machine learning and data mining in Python. Cluster is the sci-kit module that imports functions containing clustering techniques, and is therefore imported from sci-kit.

import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

import sklearn
from sklearn import cluster

%matplotlib inline

faithful = pd.read_csv('/Users/michaelrundell/Desktop/faithful.csv')
faithful.head()

Building the cluster model

In the lines of code below, I establish some important variables and alter the format of the data.

faith = np.array(faithful)

k = 2
kmeans = cluster.KMeans(n_clusters=k)
kmeans.fit(faith)

labels = kmeans.labels_
centroids = kmeans.cluster_centers_

Formatting and function creation.

  • I convert the faithful data frame to a numpy array so that sci-kit can read the data file.
  • K = 2 was chosen as the number of clusters because we are attempting to form two distinct groups.
  • The output from the cluster module in sci-kit is what defines the ‘kmeans’ variable. We instruct it to form K clusters and fit the data in the array ‘faith’.

Summary

Using Python to automate data mining can be very helpful and save a lot of time in many situations. Some of the most used Python data mining tools are Clustering, Regression, and Association Rules. Data mining is a very important part of the Data Science Project, so you should do as much of it as you can.

We hope that this article on “Automated Data Mining with Python” will help you.

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