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-# breast-cancer-detection
-Binary classification of breast cancer based on biomedical markers using logistic regression and statistical learning techniques.
+#  🩺 Early Breast Cancer Detection using Blood Biomarkers
+
+**Université Paris Dauphine - PSL**  
+Statistical Learning Project — Academic Year 2024–2025  
+**Supervisors:** Prof. Gabriel Turinici, Dr. Laetitia Comminges  
+
+## Project Objective
+
+The goal of this project is to predict the presence of breast cancer using blood-based biomarkers. Several supervised classification models are compared, with a strong focus on **recall** to reflect the clinical need to minimize false negatives (i.e., undiagnosed patients).
+
+## Dataset
+
+- **Source:** Breast Cancer Coimbra Dataset (116 patients, 9 biomarkers)
+- **Target variable:** `Classification` (0 = healthy, 1 = cancer)
+- **Preprocessing steps:**
+  - Logarithmic transformation of skewed variables
+  - Standardization (Z-score)
+  - Stratified train/test split (92/24)
+
+## Models Compared
+
+| Model                        | Recall | F1-score | AUC   |
+|-----------------------------|--------|----------|-------|
+| k-Nearest Neighbors (k=23)  | 0.92   | 0.788    | 0.88  |
+| Neural Network (MLP)        | 0.92   | 0.69     | 0.83  |
+| Logistic Regression (L2)    | 0.69   | 0.75     | 0.79  |
+| Gaussian Naïve Bayes        | 0.58   | 0.68     | 0.72  |
+
+**Best model for clinical usage (recall priority):** k-NN (k = 23)
+
+## Repository Structure
+
+- `eda_analysis.ipynb` – Data exploration, visualization, and preprocessing
+- `logistic_regression.ipynb` – Logistic regression (basic and optimized via GridSearchCV)
+- `knn.ipynb` – k-Nearest Neighbors with cross-validation and performance tuning
+- `neural_network.ipynb` – Feedforward neural network (MLPClassifier)
+- `naive_bayes.ipynb` – Gaussian Naïve Bayes with log-transformed inputs
+- `svm.ipynb` – Preliminary experiments with SVM (bonus, not included in the final report)
+- `Subject_3_Ouabdesselam_Forest_Durousseau_Danjou_vonSiemens.pdf` – Final report (comprehensive analysis and conclusions)
+- `README.md` – This file
+
+## Evaluation Metrics
+
+- **Recall:** prioritized (to avoid false negatives)
+- **F1-score:** balances precision and recall
+- **ROC & AUC:** overall discriminative ability
+
+## Clinical Recommendation
+
+Assuming false positives are acceptable to avoid missing cancer cases, the k-NN (k = 23) model is preferred. It offers the best compromise between recall and F1-score, and reliably identifies patients at risk.
+
+## Authors
+
+Erwan Ouabdesselam  
+Antonin Durousseau  
+Moritz von Siemens  
+Arthur Danjou  
+Thaïs Forest
+
+---
+
+Project completed as part of the Statistical Learning course in the Master’s program in Applied Mathematics at Université Paris Dauphine - PSL.