UVIC Data Mining Labs

Download and install the Anaconda Python 3.6 distribution
- Other distributions will work but these instructions assume you are using Anaconda
- When using other Python installations you may need to manually install other packages, such as Scikit-Learn and Jupyter Notebook
(Optional) Create a new conda virtual environment
- conda create -n seng474 python=3.6
- source activate seng474
Clone this repository and open the new folder
- git clone https://github.com/fortjohnson/SENG474.git
- cd SENG474
Start the Notebook
- jupyter notebook
Your web browser should automatically open to the Notebook Home Page
- If not, go to http://localhost:8888/
Open a file with the .ipynb extension
Now you can modify the code in the Notebook
- To execute a cell in the notebook enter Shift + Enter

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
data		data
.gitignore		.gitignore
IDS_Tree_Graph.pdf		IDS_Tree_Graph.pdf
Markov Chains and Hidden Markov Models.ipynb		Markov Chains and Hidden Markov Models.ipynb
README.md		README.md
SENG 474 - Decision Trees and Evaluation.ipynb		SENG 474 - Decision Trees and Evaluation.ipynb
SENG 474 - Lab 04 - NLP.ipynb		SENG 474 - Lab 04 - NLP.ipynb
SENG 474 - Lab 2 - Answers.ipynb		SENG 474 - Lab 2 - Answers.ipynb
SENG 474 Lab 03 - Evaluation of Classifiers.ipynb		SENG 474 Lab 03 - Evaluation of Classifiers.ipynb
SENG474_asn1.pdf		SENG474_asn1.pdf
intrusion_detection.py		intrusion_detection.py
load_frog_data.py		load_frog_data.py
load_spam_data.py		load_spam_data.py
plot_hmm_sampling.ipynb		plot_hmm_sampling.ipynb
plot_hmm_stock_analysis.ipynb		plot_hmm_stock_analysis.ipynb

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