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Programming for Data Analysis

Analysis of the Wisonsin Breast Cancer dataset

The dataset dates from 1995 and the features generated for the dataset are from an image of a biopsy of the breast tissue.
Fine Needle Aspiration (FNA) is a type of biopsy where a thin, hollow needle is used to remove fluid or tissue - in the case of this dataset, tissue.
Images were taken of the tissue samples, digitized and the features in the dataset describe characteristics of the cell nuclei present in the image.

Further explanation of FNA can be found here, and a detailed description of the dataset can be found here

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1. Clone repository
2. Open notebook on your own machine

See included libraries towards the top of the notebook. These are:
-Numpy
-Pandas
-Scikit-learn
-Fitter
-Matplotlib
-Seaborn

Use either:
pip install (package name)
or
conda install (package name)

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To explore some common classifiers and see how parameter optimisation and/or dataset size affects the peformance of the classifier.

A larger dataset improves classifier performance. This is because with more training data, the model learns the distribution of the real data better. The model accuracy is improved

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Project Link: https://github.com/fabiofabrizi/pfDA_Project2

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• Brian McGinley, ATU
• Tech icons
• Kaggle

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