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01 - IPython Notebook Demo.ipynb
01 - IPython Notebook Demo.ipynb
 
 
02.1 - Collaborative Filtering.ipynb
02.1 - Collaborative Filtering.ipynb
 
 
02.2 - Movie Rating.ipynb
02.2 - Movie Rating.ipynb
 
 
04 - Summary Statistics and Basic Plotting.ipynb
04 - Summary Statistics and Basic Plotting.ipynb
 
 
05 - Baby Names.ipynb
05 - Baby Names.ipynb
 
 
06 - Working With Missing Data.ipynb
06 - Working With Missing Data.ipynb
 
 
07 - Data Transformation.ipynb
07 - Data Transformation.ipynb
 
 
08 - Dimensionality Reduction.ipynb
08 - Dimensionality Reduction.ipynb
 
 
10 - Apriori.ipynb
10 - Apriori.ipynb
 
 
11 - FP-Growth.ipynb
11 - FP-Growth.ipynb
 
 
12 - k-means Clustering.ipynb
12 - k-means Clustering.ipynb
 
 
13 - Hierarchical Clustering.ipynb
13 - Hierarchical Clustering.ipynb
 
 
18 - KNN.ipynb
18 - KNN.ipynb
 
 
23 - Decision Trees.ipynb
23 - Decision Trees.ipynb
 
 
27 - Evaluating Classifiers.ipynb
27 - Evaluating Classifiers.ipynb
 
 
30 - Boosting.ipynb
30 - Boosting.ipynb
 
 
31 - Decision Tree Ensembles.ipynb
31 - Decision Tree Ensembles.ipynb
 
 
32 - Stacking & Blending.ipynb
32 - Stacking & Blending.ipynb
 
 
33 - Artificial Neural Networks.ipynb
33 - Artificial Neural Networks.ipynb
 
 
README.md
README.md
 
 

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Data Mining — CSE 40647/60647 — Fall 2014

This repository contains the code snippets that were covered in Data Mining (CSE 40647/60647) at the University of Notre Dame. While these files are hosted here, we will be utilizing IPython Notebook Viewer (NBViewer) to display the IPython Notebooks.

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ND CSE 40647: Data Mining

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