Python complete tutorial
  • Python Complete Tutorial
  • About this book
  • What you need to prepare
  • 1️⃣Try python for the first time
    • Install python
    • Hello world!
    • Hello world in a nutshell
    • The first simple python project
    • most useful libraries
    • Recommended websites
  • 2️⃣Data structure and basic operations
    • Python data structure
    • Data structure without hash table
    • Data structure with hash table
    • Variability and address
    • basic python programming
    • basic python programming 2
    • basic python programming 3
    • some additions
    • Fibonacci sequence
    • Judging prime numbers
    • txt/csv file operation
  • 🐍Practice program
    • 🚩fancy print
    • 🚩Remove duplicate elements
    • 🚩Palindrome detection
  • 😎leetcode
    • what is leetcode
  • 3️⃣Data mining and machine learning
    • What is data mining
    • iris data set
    • Mean median mode
    • Harmonic mean
    • Histogram
    • Correlation algorithm
    • Gaussian distribution data set
    • projection
    • PCA
    • MDS
    • Bayesian and Frequentist
    • Data normalization
    • binary SVM
    • One Hot Encoding
    • Multi-class SVM
    • Accuracy and error rate
    • Confusion matrix & Accuracy, Precision, Recall
    • F1 score
    • ROC and AUC
  • 4️⃣big data and data visualization
    • line chart
    • Parallel coordinates
    • Histogram chart
  • 5️⃣Mathematical algorithm and digital signal processing
    • Mathematical constants and basic operations
    • Normal distribution
    • Permutation and combination
    • Bernoulli distribution
    • Chaotic system
  • 6️⃣Classes and design patterns
    • Classes and design patterns
  • 7️⃣Operate the database with python
    • MySQL
      • Install MySQL
      • First try MySQL
      • MySQL Architecture
      • database operations
      • database
  • 8️⃣Cryptography
    • beginning of Cryptography
  • 9️⃣deep learning
    • What is Deep Learning
    • basic
  • 💔algorithm
    • Algorithms and Data Structures
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  1. Data mining and machine learning

What is data mining

Introduction to iris data set

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Last updated 3 years ago

Data mining is to find out some patterns from the data.

For example, here is a piece of data about income and outlay

income = [1200, 1400, 1600, 1800, 2000, 2200, 2400]
outlay = [1000, 1240, 1320, 1500, 1790, 1924, 2218.75]

Each income corresponds to an outlay

If we use the following procedure, we can draw a clear diagram of the relationship between income and expenditure:

income = [1200, 1400, 1600, 1800, 2000, 2200, 2400]
outlay = [1000, 1240, 1320, 1500, 1790, 1924, 2218.75]

import matplotlib.pyplot as plt

plt.plot(income, outlay)

plt.title('The relationship between income and outlay')
plt.xlabel('income')
plt.ylabel('outlay')

plt.grid()
plt.show()

In this graph, we will find that when income increases, expenditures will increase accordingly.

This is a common sense of life, that is, when we earn more, we are more inclined to consume more, we earn less, we don't have so much money to consume.

We can dig out the above content from this data.

This is data mining. We find some patterns hidden in the data, and you will find that python is born for data mining. Then we will explain more in the next chapter.

Statistics

Start time of this page: December 19, 2021

Completion time of this page: December 19, 2021

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The relationship between income and outlay