🎀 𝐁𝐫𝐞𝐚𝐬𝐭 𝐂𝐚𝐧𝐜𝐞𝐫 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 — 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 (𝐄𝐃𝐀 + 𝐌𝐋)

🧬 Transforming raw medical data into predictive intelligence.

🛠️ Analytical Platform :

Built using Python, Pandas, NumPy, Seaborn, Matplotlib & Scikit-Learn in a structured ML workflow.

📥 Data Ingestion :

Imported the Breast Cancer dataset, validated structure, and prepared it for analysis.

🧹 Data Cleaning :

Handled missing values, removed noise, standardized features, ensuring high-quality medical data.

📊 Univariate Analysis :

Explored distributions of key cellular features (radius, texture, smoothness, etc.).

🔗 Bivariate Analysis :

Identified relationships between tumor characteristics and diagnosis (benign vs malignant).

🧠 Multivariate Analysis :

Analyzed multiple features collectively to uncover the most influential cellular characteristics.

⚙️ Feature Engineering :

Created transformed features, normalized data, and encoded target labels for ML modeling.

🤖 Machine Learning :

Developed classification and regression pipelines with preprocessing, scaling & model evaluation.

📈 Implement & Evaluate Regression Models :

Compared Linear Regression, Random Forest, and Gradient Boosting using MAE, MSE & R².

📉 Visualize Regression Model Performance :

Plotted predicted vs actual tumor measurement patterns to evaluate model accuracy.

🏆 Select & Visualize Best Regression Model :

Identified the top model and visualized final performance for medical interpretability.