Breast Cancer Classification using Logistic Regression

This project applies Logistic Regression to classify breast cancer tumors as Benign or Malignant using the Breast Cancer Wisconsin (Diagnostic) dataset from Kaggle.

The goal is to build a simple but effective predictive model for early detection of breast cancer, which is a critical step in assisting doctors with diagnosis.

📊 Dataset

• Source: Breast Cancer Wisconsin (Diagnostic) Dataset - Kaggle
• Features: 30 numerical features computed from digitized images of breast masses (radius, texture, smoothness, etc.)
• Target:
  • 0 → Malignant (cancerous)
  • 1 → Benign (non-cancerous)
• Samples: 569 total

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🛠️ Tech Stack

• Language: Python
• Environment: Google Colab
• Libraries Used:
  • numpy
  • pandas
  • matplotlib / seaborn (for visualization)
  • scikit-learn (for preprocessing, model training & evaluation)

🚀 Project Workflow

1. Data Loading & Exploration

   • Import dataset, inspect features, check for missing values
   • Visualize class distribution

2. Data Preprocessing

   • Train-test split

3. Model Building

   • Logistic Regression model using Scikit-learn

4. Model Evaluation

   • Accuracy Score

📈 Results

• Accuracy Achieved: ~95% (depending on random split)
• Logistic Regression proved effective in distinguishing between benign and malignant cases.

🧑‍💻 Author

• Thevindu Dilmith