Breast Cancer Classification 🧬

This repository contains machine learning projects for classifying breast cancer tumors as malignant or benign using diagnostic features.
It demonstrates a full data science workflow — from data preprocessing and exploratory analysis to model training, evaluation, and comparison.

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📌 Project Overview

• Goal: Build accurate classification models for early breast cancer detection.
• Dataset: Breast Cancer Wisconsin (Diagnostic) dataset.
• Approach:
  • Data cleaning & preprocessing
  • Exploratory Data Analysis (EDA) with visualizations
  • Training multiple machine learning models
  • Comparing performance metrics
  • Visualizing model results

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📂 Repository Contents

• Breast_Cancer_Classification.ipynb
  → Clean notebook focusing on the end-to-end classification pipeline.

• Breast_Cancer_Classification_2.ipynb
  → Extended notebook with additional results and analysis, including visual outputs for immediate review.

• README.md
  → Project documentation.

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🛠️ Tools & Libraries

• Python 3.x
• NumPy, Pandas – data handling
• Matplotlib, Seaborn – visualization
• Scikit-learn – machine learning models & evaluation
• Jupyter Notebook

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📊 Models Implemented

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

Performance evaluated using:

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix

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