Cancer Cell Classifier – High Accuracy

A Machine Learning–based project for classifying cancerous and non-cancerous cells using Support Vector Machine (SVM). The model achieves high accuracy and is evaluated using standard performance metrics.

---

🎯 Motivation

Early detection of cancer plays a crucial role in saving lives.
This project aims to assist in identifying whether a cell is Benign or Malignant using Machine Learning techniques on medical data.

---

📁 Dataset Description

The dataset contains numerical features extracted from breast cell samples.

Features:

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

Target Class:

• 2 → Benign (Non-Cancerous)
• 4 → Malignant (Cancerous)

The ID column is removed during preprocessing as it does not contribute to prediction.

---

⚙️ Project Workflow

1. Load CSV dataset
2. Data cleaning and preprocessing
3. Feature scaling using StandardScaler
4. Train-test split
5. Model training using SVM (RBF kernel)
6. Model evaluation and visualization

---

🤖 Machine Learning Model

• Algorithm: Support Vector Machine (SVM)
• Kernel: RBF (Radial Basis Function)

Why SVM?

• Performs well on high-dimensional data
• Effective for medical classification tasks
• Handles non-linear decision boundaries efficiently

---

📊 Results & Evaluation

• Accuracy: ~96%
• Performance evaluated using:
  • Accuracy Score
  • Precision, Recall, F1-score
  • Confusion Matrix

📈 Model Performance Visualizations

🔹 Accuracy The following image shows the achieved accuracy of the model:

---

🔹 Confusion Matrix The confusion matrix visualizes correct and incorrect predictions:

---

🛠️ Tech Stack

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

---

🚀 How to Run the Project

1️⃣ Clone the repository

git clone https://github.com/madhusudanladda/Cancer-Cell-Classifier-High-Acc.git

cd Cancer-Cell-Classifier-High-Acc

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run the notebook

Open main.ipynb in Jupyter Notebook or VS Code and execute the cells.

📌 Repository Structure text Copy code ├── main.ipynb

├── cancer_cell_dataset.csv

├── requirements.txt

├── accuracy.png

├── confusion_matrix.png

└── README.md

🔮 Future Scope

Deploy the model using Streamlit or Flask

Compare SVM with Random Forest and Neural Networks

Use larger real-world medical datasets

Cloud deployment for remote access

👨‍💻 Author

Madhusudan Ladda

BTech Computer Science

Interested in Machine Learning & Medical AI