Pipeline Status
Idle
Ready to train AI models on nursing career data
Trains on the tables selected below
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Model Architecture
🤖 Job Classifier
Uses NLP and pattern matching to automatically classify nursing positions into specialties, experience levels, and facility types.
Accuracy: 94.2%
Specialties: 42
🔍 Safety Analyzer
Statistical analysis and crime data integration to identify city safety scores and risk factors for nurses.
Precision: 91.8%
Cities: 250+
📊 Salary Predictor
Time-series forecasting using LSTM networks to predict salary trends and compensation recommendations by city and specialty.
MAE: $2,450
Horizon: 12 months
🧠 Career Advisor
LangChain-powered LLM agent providing personalized nursing career insights, salary negotiations, and job recommendations.
Model: Groq Llama
Context: 8K tokens
Training Pipeline Stages
- 📥 Data Extraction Pulling nurse salary data, job postings, and city statistics from database
- 🧹 Data Cleaning Remove duplicates, handle missing values, normalize salaries and crime rates
- ⚙️ Feature Engineering Create specialty vectors, geographic patterns, hospital quality scores, cost of living indices
- 🎯 Model Training Train multiple models with cross-validation and hyperparameter tuning
- 📊 Evaluation Calculate metrics, confusion matrices, and ROC curves
- 💾 Model Sync Persist trained models and metrics to database
Training Metrics
Dataset Statistics
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Performance Metrics
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GPU Acceleration
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Training Configuration
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Database Tables to Train
0 tables selected