https://github.com/praatibhsurana/breast-cancer-prediction-svm

A SVM classifier coded in Python using Scikit-Learn to classify whether a patient's tumor is malignant or benign.
https://github.com/praatibhsurana/breast-cancer-prediction-svm

kaggle-dataset linear-classifier machine-learning-algorithms python scikit-learn svm-classifier

Last synced: 5 days ago
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A SVM classifier coded in Python using Scikit-Learn to classify whether a patient's tumor is malignant or benign.

• Host: GitHub
• URL: https://github.com/praatibhsurana/breast-cancer-prediction-svm
• Owner: praatibhsurana
• Created: 2020-10-26T10:50:23.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-10-28T07:57:10.000Z (over 4 years ago)
• Last Synced: 2024-01-15T01:19:16.771Z (about 1 year ago)
• Topics: kaggle-dataset, linear-classifier, machine-learning-algorithms, python, scikit-learn, svm-classifier
• Language: Python
• Homepage:
• Size: 168 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Brief

The project was carried out on the breast cancer dataset compiled for research. It can be found at: [UCI ML Repository](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29) and also on [Kaggle](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data)

### Attribute Information:

1) ID number

2) Diagnosis (M = malignant, B = benign)

3-32)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter)

b) texture (standard deviation of gray-scale values)

c) perimeter

d) area

e) smoothness (local variation in radius lengths)

f) compactness (perimeter^2 / area - 1.0)

g) concavity (severity of concave portions of the contour)

h) concave points (number of concave portions of the contour)

i) symmetry

j) fractal dimension ("coastline approximation" - 1)

The mean, standard error and "worst" or largest (mean of the three

largest values) of these features were computed for each image,

resulting in 30 features. For instance, field 3 is Mean Radius, field

13 is Radius SE, field 23 is Worst Radius.

All feature values are recoded with four significant digits. | 

Missing attribute values: none |

Class distribution: 357 benign, 212 malignant

### Correlation Heatmap of the various parameters after basic EDA

![Correlation Heatmap](https://github.com/praatibhsurana/Breast-Cancer-Prediction-SVM/blob/master/corr_heatmap.png?raw=true)

### Model

A SVM Classifier was used. Preprocessing and EDA was carried out and the 26 best parameters that affected the prediction were chosen. A little bit of tweaking on the C parameter and use of rbf kernel yielded better results as compared to a linear kernel.

The scores obtained were as follows:

1) Accuracy = 0.93

2) Precision = 0.95

3) Recall = 0.74

4) F1-Score = 0.83

The score can be improved on further analysis and experimentation with various kernels and tweaking of 'C' and 'gamma' parameters.