This notebook aims to analyze a Breast Cancer Wisconsin (Diagnostic) Data Set and give predictions about the diagnosis of further patients using machine learning models. Early and accurate diagnosis is crucial in improving treatment outcomes, and this analysis seeks to leverage data-driven approaches to enhance diagnostic accuracy.
- Basic Data Exploration: Understand the dataset's structure and key statistics.
- Data Visualization: Visualize the data to identify patterns and relationships.
- Data Preprocessing: Clean and prepare the data for analysis.
- Label Encoding / Mapping: Convert categorical variables into numerical format for model compatibility.
- Classification Models: Implement various machine learning models to predict diagnoses:
- Logistic Regression: A statistical method for binary classification.
- Decision Tree: A model that uses a tree-like graph of decisions.
- Random Forest: An ensemble method that combines multiple decision trees.
- K-Nearest Neighbors: A simple, instance-based learning algorithm.
- Model Validation/Performance: Evaluate the models' performance using appropriate metrics.
- Conclusion: Summarize findings and potential implications for future work.