3D BIOLOGICAL SHAPE ANALYSIS & PREDICTIVE MODELING

---

Applied machine learning pipeline in Python for the analysis of 3D biological structures using dimensionality reduction and regression techniques.

This project implements a comprehensive data science workflow to analyze the morphological characteristics of 3D blood vessel coordinates. By processing high-dimensional spatial data representing vessel shapes, the system identifies primary modes of structural variation and predicts shape attributes. The implementation features a robust Scikit-Learn pipeline integrating data imputation, feature scaling, and Linear Regression, with performance validated through RMSE metrics and spatial 3D visualizations.

FEATURES

---

• 3D Morphological Analysis: Processing and visualization of (x, y, z) coordinate data for complex biological shapes.
• Dimensionality Reduction: Implementation of Principal Component Analysis (PCA) to extract significant features and reduce noise from high-dimensional datasets.
• Automated ML Pipelines: End-to-end workflows using sklearn.pipeline to ensure reproducible data transformation and prevent data leakage during modeling.
• Data Preprocessing: Robust handling of missing values via SimpleImputer and feature normalization using StandardScaler.
• Performance Analytics: Comprehensive evaluation using Root Mean Squared Error (RMSE) and visual regression analysis (Actual vs. Predicted plots).

TECHNOLOGIES

---

• Language: Python
• Machine Learning: Scikit-Learn (PCA, Pipelines, Linear Regression, Imputers)
• Data Manipulation: Pandas, NumPy
• Visualization: Matplotlib (including mpl_toolkits.mplot3d for 3D plotting)

LEARNING OUTCOMES

---

This project demonstrates practical understanding of:

• Feature Engineering: Managing high-dimensional spatial data and optimizing feature sets through PCA.
• Pipeline Architecture: Building scalable and professional-grade machine learning workflows.
• Statistical Evaluation: Interpreting regression metrics and identifying model generalization through train-test splitting.
• Scientific Visualization: Representing complex 3D structures and statistical results through clear, professional graphics.