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Statistical Learning and Data Mining (QBUS6810)

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Name		Name	Last commit message	Last commit date
01 Introduction to Python.ipynb		01 Introduction to Python.ipynb
02 Data Manipulation with pandas.ipynb		02 Data Manipulation with pandas.ipynb
03 Supervised Learning with Scikit-Learn.ipynb		03 Supervised Learning with Scikit-Learn.ipynb
04 Linear Regression and k-Nearest Neighbours.ipynb		04 Linear Regression and k-Nearest Neighbours.ipynb
05 Some Tools for Feature Engineering.ipynb		05 Some Tools for Feature Engineering.ipynb
06 Model Selection.ipynb		06 Model Selection.ipynb
08 Linear Model Selection and Regularisation.ipynb		08 Linear Model Selection and Regularisation.ipynb
09 Logistic Regression.ipynb		09 Logistic Regression.ipynb
10 Credit Risk Application.ipynb		10 Credit Risk Application.ipynb
11 Nonlinear Modelling.ipynb		11 Nonlinear Modelling.ipynb
13 Gradient Boosting.ipynb		13 Gradient Boosting.ipynb
A1 Preparing a Submission for the Clasification Challenge.ipynb		A1 Preparing a Submission for the Clasification Challenge.ipynb
statlearning.py		statlearning.py

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Statistical Learning and Data Mining (QBUS6810)

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