https://github.com/anas436/cancer-detection-using-support-vector-machines-with-python

https://github.com/anas436/cancer-detection-using-support-vector-machines-with-python

confusion-matrix f1-score itertools jaccard-score jupyterlab matplotlib model-selection numpy pandas pylab python3 scipy sklearn

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/anas436/cancer-detection-using-support-vector-machines-with-python
• Owner: Anas436
• Created: 2022-09-05T17:10:00.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-05T17:14:06.000Z (over 2 years ago)
• Last Synced: 2025-02-01T15:30:59.153Z (3 months ago)
• Topics: confusion-matrix, f1-score, itertools, jaccard-score, jupyterlab, matplotlib, model-selection, numpy, pandas, pylab, python3, scipy, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 37.1 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Cancer-Detection-using-Support-Vector-Machines-with-Python

## Objectives

After completing this lab you will be able to:

*   Use scikit-learn to Support Vector Machine to classify

In this notebook, you will use SVM (Support Vector Machines) to build and train a model using human cell records, and classify cells to whether the samples are benign or malignant.

SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.

Table of contents




    


        
Load the Cancer data

        
Modeling

        
Evaluation

        
Practice

    








Load the Cancer data

The example is based on a dataset that is publicly available from the UCI Machine Learning Repository (Asuncion and Newman, 2007). The dataset consists of several hundred human cell sample records, each of which contains the values of a set of cell characteristics. The fields in each record are:


| Field name  | Description                 |

| ----------- | --------------------------- |

| ID          | Clump thickness             |

| Clump       | Clump thickness             |

| UnifSize    | Uniformity of cell size     |

| UnifShape   | Uniformity of cell shape    |

| MargAdh     | Marginal adhesion           |

| SingEpiSize | Single epithelial cell size |

| BareNuc     | Bare nuclei                 |

| BlandChrom  | Bland chromatin             |

| NormNucl    | Normal nucleoli             |

| Mit         | Mitoses                     |

| Class       | Benign or malignant         |