++++++++++++++++++++File List++++++++++++++++++++
Forest_Cover_Data_Visualization.ipynb
Forest_Cover_Decision_Tree.ipynb
Forest_Cover_Decision_Tree_Cross_Validation.ipynb
Forest_Cover_Logistic_Regression.ipynb
Forest_Cover_Multilayer_Perceptron.ipynb
plot_tool.py
load_dataset.py
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Forest_Cover_Data_Visualization.ipynb
Detail:
In this file you can perform data visualization.
You can also visualize the data by using pca1 and pca2.
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Forest_Cover_Decision_Tree.ipynb
Forest_Cover_Decision_Tree_Cross_Validation.ipynb
Detail:
These two file almost the same. The only difference is the second has the cross validation
line chart. I did the 10 fold cross validation.
You can use these two files to train and evaluate decision tree model.
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Forest_Cover_Logistic_Regression.ipynb
This file is about training logistic regression model to do classification on the dataset.
You can train and evaluate the model.
The performance is similar with decision tree.
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Forest_Cover_Multilayer_Perceptron.ipynb
This file is about training multilayer perceptron to do classification on the dataset.
You can train and evaluate the model.
The performance is worst amony the three methods.
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plot_tool.py
Details:
This Python file provides two functions, bar chart plot and pca chart plot.
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load_dataset.py
Details:
This Python file provides some necessary functions for loading data when preparing for training data.
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lrm_model8.model
lrm_model9.model
lrm_model10.model
Details:
These three files are trained logistic regression model, you can load it and evaluate it, 
Or you can use it to make predictions.