Poultry Feed Consumption Predictor - Cobb 500 ML System

Advanced Machine Learning system for predicting daily feed consumption in Cobb 500 chickens using Random Forest algorithm. Trained on real poultry farm data achieving 99%+ accuracy (R² > 0.99).

🔹 Industry Impact: Optimizes feed costs in commercial poultry operations 🔹 Exceptional Accuracy: R² > 0.99 with minimal prediction errors 🔹 Smart Features: 7 core variables + 4 engineered features 🔹 Production Ready: Complete training and prediction pipeline 🔹 Multi-Treatment Support: Handles different feeding treatments

Predicts daily feed consumption by growth stage

Reduces feed waste and optimizes nutrition costs

Supports treatment comparison and optimization

Enables data-driven poultry farm management

Variables: Day, weight, weight gain, treatment codes, conversion ratios Tech Stack: Python, Random Forest, scikit-learn, pandas, feature engineering

Business Value: Direct cost reduction through feed optimization, improved production efficiency, and data-driven decision making in commercial poultry operations.