Classifier-Selection-Using-Randomized-Search-On-Hyperparameter-Tuning

Making use of Grid Search on AdaBoost Classifier for Hyperparameter Tuning.

Supervised Learning

Project: Parameter Tuning using Grid Search

Install

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn

You will also need to have software installed to run and execute an iPython Notebook

We recommend installing Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

Code

Template code is provided in the Classifier Selection Using HyperParameter Tuning.ipynb notebook file. You will also be required to use the included diabetes.csv Python file. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file.

Run

In a terminal or command window, navigate to the top-level project directory finding_donors/ (that contains this README) and run one of the following commands:

ipython notebook finding_donors.ipynb

or

jupyter notebook finding_donors.ipynb

This will open the iPython Notebook software and project file in your browser.