🏥 Breast Cancer Dataset Analysis

A comprehensive statistical and exploratory data analysis of the Wisconsin Breast Cancer dataset for cancer diagnosis prediction.

📊 Project Overview

This project provides a complete analysis of breast cancer diagnostic data, using statistical methods and exploratory data analysis to identify key characteristics that distinguish malignant from benign tumors.

📋 Dataset Information

• Source: Wisconsin Breast Cancer Dataset
• Samples: 569 patients
• Features: 30 numeric features + 1 target variable
• Task: Binary classification (Malignant vs Benign)
• Target Distribution: 357 Benign (62.7%), 212 Malignant (37.3%)

🔬 Analysis Highlights

Key Findings

• No missing values - excellent data quality
• Strong predictive features identified through correlation analysis
• Statistically significant differences between malignant and benign groups
• Feature clustering reveals natural groupings of measurements

Top Discriminative Features

1. concave points_worst (correlation: 0.794)
2. perimeter_worst (correlation: 0.783)
3. concave points_mean (correlation: 0.777)
4. radius_worst (correlation: 0.776)
5. perimeter_mean (correlation: 0.743)

Statistical Results

• Hypothesis Testing: Highly significant differences in tumor size (p < 0.001)
• Effect Size: Large clinical effect (Cohen's d = 0.875)
• Clinical Significance: Malignant tumors are 5.32 units larger in radius on average

📁 File Structure

├── breast_Cancer_analysis.ipynb    # Main analysis notebook
├── Breast_Cancer_dataset          # Dataset file
└── README.md                      # This file

🚀 Notebook Sections

1. Data Summary and Overview - Dataset characteristics and quality assessment
2. Data Exploration Plan - Structured approach to analysis
3. Exploratory Data Analysis - Statistical analysis and visualizations
4. Key Findings and Insights - Summary of important discoveries
5. Hypotheses Development - Three research hypotheses formulated
6. Statistical Significance Testing - Rigorous hypothesis testing
7. Feature Relationship Analysis - Correlation and clustering analysis
8. Conclusions and Next Steps - Summary and recommendations

🛠️ Technologies Used

• Python 3.x
• Pandas - Data manipulation and analysis
• NumPy - Numerical computing
• Matplotlib & Seaborn - Data visualization
• SciPy - Statistical analysis and hypothesis testing
• Jupyter Notebook - Interactive analysis environment

📈 Key Visualizations

• Comprehensive correlation heatmaps
• Statistical distribution comparisons
• Feature relationship dendrograms
• Hypothesis testing visualizations
• Clinical significance plots

🎯 Results Summary

Statistical Validation

• ✅ Significant size differences between tumor types
• ✅ Large effect sizes indicating clinical relevance
• ✅ Robust feature correlations for prediction
• ✅ Clear feature clustering patterns identified

Clinical Implications

• Larger tumors strongly associated with malignancy
• Shape irregularities (concave points) are key indicators
• Multiple complementary features provide diagnostic confidence
• Statistical foundation supports clinical decision-making

🔄 Next Steps

Machine Learning Development

• Implement classification models (Random Forest, SVM, Neural Networks)
• Perform feature selection optimization
• Cross-validation and hyperparameter tuning
• Model performance evaluation

Advanced Analytics

• External dataset validation
• Ensemble method development
• Clinical decision support tool creation
• Cost-effectiveness analysis

Analysis completed: September 2025
Methods: Statistical Analysis, EDA, Hypothesis Testing
Tools: Python, Jupyter, Statistical Libraries