Cancer Detection using SVM

This project applies a Support Vector Machine (SVM) model to classify cell samples as Benign (2) or Malignant (4) using medical data from cell_samples.csv.

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Objective

To develop a binary classification model that can assist in the detection of cancerous cells using key biological features.

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Dataset

The dataset cell_samples.csv contains 9 attributes extracted from cell images:

• Clump Thickness
• Uniformity of Cell Size
• Uniformity of Cell Shape
• Marginal Adhesion
• Single Epithelial Cell Size
• Bare Nuclei
• Bland Chromatin
• Normal Nucleoli
• Mitoses

The Class column is the target:

• 2: Benign
• 4: Malignant

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How It Works

1. Loads and cleans dataset
2. Extracts features and target variable
3. Trains an SVM (linear kernel)
4. Evaluates using accuracy, confusion matrix, precision, recall

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Results

• Classifier: SVM (Linear Kernel)

• Accuracy: ~96%

• Evaluation:

                   precision   recall  f1-score   support
             2       1.00      0.94      0.97        90
             4       0.90      1.00      0.95        47
  
      accuracy                           0.96       137
     macro avg       0.95      0.97      0.96       137
  weighted avg       0.97      0.96      0.96       137
  

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Setup

Requirements

Install dependencies via:

pip install -r requirements.txt