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Machine-learning-Python-

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# Machine-learning-Python-
It's my python practice for the machine learning.
In my code, you have to prepare the matplotlib, numpy toolkit first.

There are two main codes.
# Example 1 (Classifier and Dimension Reduction)
'Main_randomSample_Classifier_Dimension.py'
It's the example code for the classifier (Gaussian Classifier/QDC) and the dimension reduction approaches(PCA, LDA/DAFE)
The data were randomly generated by the normal distribution.

The implement result is shown as follows: 
![alt tag](https://github.com/TommyHuang821/Machine-learning-Python-/blob/master/Example_Classifier_DimensionReduction.png)
In dimension reduction, it is easy to understand that the supervised approach (LDA) is better than unsupervised approach (PCA) visually.

# Example 2 (Regression)
'main_regression.py'
It's an example code for the regression case.
RegressionExample.txt is the example data.
The first column is the dependent variable, and second-fourth columns are the independent variables.
I just apply the Linear regression and ridge regression.
The result is shown as follows: 
![alt tag](https://github.com/TommyHuang821/Machine-learning-Python-/blob/master/RegressionExample.png)
x-axis depicts the ground truth, and y-axis depicts the prediction result.

# Note:
Sklearn is a powerful toolkit for machine learning.
I will demo how to use it in the future.
http://scikit-learn.org/stable/

Machine-learning-Python-

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