What does python data mining need to learn?
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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