Material Stream Identification System

An automated system for identifying material streams using feature extraction, data augmentation, and machine learning classifiers (SVM and k-NN).

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🚀 Project Pipeline

The project follows a structured machine learning workflow, divided into a core data preparation phase followed by algorithm-specific implementations.

🛠 Phase 1: Data Preparation

1. Load & Clean: Initial data ingestion and removal of noise or corrupted files.
2. Preprocessing: Data normalization and formatting for model readiness.
3. Train-Test Split: Partitioning the dataset into training and evaluation sets.
4. Data Augmentation: Expanding the dataset to improve model generalization.

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🧠 Model Workflows

🔹 k-Nearest Neighbors (k-NN)

Workflow designed for distance-based classification:

• Step 1: feature_extraction_KNN — Extracting relevant spatial or color features.
• Step 2: scaleData_KNN — Standardizing data to ensure equal feature weighting.
• Step 3: knn_train — Training the k-NN classifier.
• Step 4: camera_knn — Real-time identification via live camera feed.

🔸 Support Vector Machine (SVM)

Workflow optimized for high-dimensional boundary classification:

• Step 1: feature_extraction_SVM — Extracting features tailored for hyperplane separation.
• Step 2: scale_data_SVM — Feature scaling for optimal SVM convergence.
• Step 3: svm_train — Training the SVM model.
• Step 4: camera_svm — Real-time identification via live camera feed.

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📊 Summary Table

Stage	k-NN Path	SVM Path
Features	feature_extraction_KNN	feature_extraction_SVM
Scaling	scaleData_KNN	scale_data_SVM
Training	knn_train	svm_train
Inference	camera_knn	camera_svm

Note: The preprocessing and augmentation steps are shared across both models to ensure a fair comparison of performance.