Cancer-Tumor-Classification-using-SVM

Here’s a professional and detailed README.md for your GitHub repository based on the uploaded notebook titled Cancer_Tumor_Classification_Cleaned.ipynb:

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🧬 Cancer Tumor Classification using Machine Learning

This project applies machine learning techniques to classify whether a tumor is benign or malignant using the Breast Cancer Wisconsin (Diagnostic) Dataset. The notebook includes comprehensive steps such as data cleaning, exploratory data analysis (EDA), feature selection, model training, and evaluation.

🚀 Project Goals

• Understand and clean the cancer dataset.
• Explore key features and visualize data patterns.
• Train multiple classification models.
• Evaluate model performance using accuracy, confusion matrix, and classification reports.
• Select the best-performing model for cancer prediction.

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📁 Dataset

• Source: UCI Machine Learning Repository – Breast Cancer Wisconsin (Diagnostic)
• Features: 30 numeric features describing characteristics of cell nuclei present in digitized images.
• Target: Diagnosis (M for malignant, B for benign).

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📌 Key Steps in the Notebook

📊 1. Data Exploration & Cleaning

• Checked for null values and duplicates.
• Removed irrelevant columns (e.g., ID).
• Converted categorical target labels to numerical form (M → 1, B → 0).

🧠 2. Model Training

Trained and compared the following classification algorithms:

• Logistic Regression
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)
• Decision Tree
• Random Forest
• Gradient Boosting

📈 3. Model Evaluation

• Accuracy Score
• Confusion Matrix
• Precision, Recall, F1-Score
• ROC-AUC Score

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✅ Best Performing Model

The Random Forest Classifier achieved the highest accuracy and robustness across metrics, making it the best choice for real-world deployment.

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📎 Requirements

Install the required libraries using:

pip install numpy pandas matplotlib seaborn scikit-learn

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🧪 How to Run

1. Clone the repository:

git clone https://github.com/your-username/Cancer_Tumor_Classification.git
cd Cancer_Tumor_Classification

2. Open the notebook in Jupyter:

jupyter notebook Cancer_Tumor_Classification_Cleaned.ipynb

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📌 Use Cases

• Early breast cancer detection
• Medical diagnosis tools
• Academic teaching for binary classification

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🙌 Acknowledgements

• UCI Machine Learning Repository for the dataset
• scikit-learn for the robust ML toolkit