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🤖 ML Model Training Report

📊 Model Performance

Model Performance Metrics

Mean Absolute Error = 0.000368
Mean Squared Error = 0.000000
R² Score = 1.000000
Final Training Loss = 1.00e-11
Final Validation Loss = 5.17e-11
Training Samples = 35
Test Samples = 15
Model Architecture = Single Dense Layer
Optimizer = Adam (lr=0.1)
Training Status = ✅ EXCELLENT PERFORMANCE

## 📈 Model Visualization
![](https://asset.cml.dev/e85900506e1613e56fec912d06ef54c1701cdc55?cml=png&cache-bypass=5ad45139-7154-411f-8c3f-44c606aadaa2)

## 🔧 Model Configuration
- **Framework**: TensorFlow/Keras
- **Model Type**: Sequential Neural Network
- **Architecture**: Single Dense Layer (Linear Regression)
- **Optimizer**: Adam (learning_rate=0.1)
- **Loss Function**: Mean Squared Error
- **Training Epochs**: 200
- **Data Split**: 70/30 (shuffled)
- **Normalization**: StandardScaler applied

## 📋 Training Details
- **Dataset**: Synthetic linear relationship (y = x + 10)
- **Total Samples**: 50
- **Training Samples**: 35
- **Test Samples**: 15
- **Data Range**: X ∈ [-100, 96], y ∈ [-90, 106]
- **Validation Split**: 20% of training data

## 🎯 Results Summary
✅ **EXCEPTIONAL PERFORMANCE ACHIEVED**
- Near-perfect linear relationship learned
- R² = 1.0 (perfect correlation)
- MAE < 0.001 (sub-millimeter precision)
- No overfitting detected
- Model ready for production deployment


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