System Features

Model Explainability

Understand exactly which factors drive your waste predictions using SHAP (SHapley Additive exPlanations) values. The system identifies the top 5 waste-driving factors for each prediction.

• ✓ SHAP-based feature importance
• ✓ Top 5 waste-driving factors
• ✓ Impact direction (increases/decreases waste)
• ✓ Contribution values for transparency

Prediction Reliability

Get confidence indicators (High/Medium/Low) that reflect how representative your input data is relative to the training dataset.

• ✓ Three reliability levels
• ✓ Quantitative reliability score (0-100%)
• ✓ Descriptive messages
• ✓ Based on k-nearest neighbors analysis

Cost Impact Estimation

Calculate the financial implications of predicted steel waste, including waste cost and potential savings from waste reduction strategies.

• ✓ Waste cost calculation (USD)
• ✓ Potential cost savings
• ✓ Configurable steel quantity
• ✓ Customizable unit costs

CO₂ Impact Analysis

Estimate the environmental footprint of predicted waste, including CO₂ emissions and potential reductions from optimization.

• ✓ CO₂ emissions calculation (kg)
• ✓ Potential CO₂ reduction
• ✓ Environmental impact metrics
• ✓ Sustainability reporting

Real-Time Predictions

Get instant predictions and comprehensive analysis to make informed decisions during project planning and execution.

• ✓ Instant predictions
• ✓ Comprehensive analysis
• ✓ Actionable insights
• ✓ User-friendly interface

Advanced ML Models

Built using state-of-the-art machine learning algorithms with comprehensive model comparison and selection.

• ✓ 10 different ML models tested
• ✓ Gradient Boosting (Best Model)
• ✓ 93% accuracy (R² Score)
• ✓ 10-fold cross-validation