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This article is about what you need to learn about python data mining. The editor thinks it is very practical, so share it with you as a reference and follow the editor to have a look.

1. Operation of Pandas library

Panda is a particularly important library for data analysis. We need to master the following three points:

Pandas packet computing

Pandas index and multiple index

Indexing is difficult, but it is very important

Pandas multi-table operation and PivotTable report

2. Numpy numerical calculation.

Numpy data computing is mainly used in data mining, for the future machine learning, deep learning, this is also a library that must be mastered, we should master the following:

Numpy array understanding

Array index operation

Array calculation

Broadcasting (knowledge in linear algebra)

3. Data visualization-matplotlib and seaborn

Matplotib syntax

The most basic visualization tool for python is matplotlib. At first glance, Matplotlib and matlib look a bit alike. To find out what the relationship between them is, it will be easier to learn.

The use of seaborn

Seaborn is a very beautiful visualization tool.

Pandas drawing function

As mentioned earlier, pandas does data analysis, but it also provides some API for drawing.

4. Introduction to data Mining

This part is the most difficult and interesting part, to master the following parts:

The definition of machine learning

There is no difference between here and data mining.

The definition of cost function

Train/Test/Validate

The definition and avoidance of Overfitting

5. Data mining algorithm

With the development of data mining, there are many algorithms. We only need to master the simplest, most core and most commonly used algorithms.

Least square algorithm

Gradient decline

Vectorization

Maximum likelihood estimation

Logistic Regression

Decision Tree

RandomForesr

XGBoost

6. Data mining practice

The model is understood through scikit-learn, the most famous library in machine learning.

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