High-Precision ML Model for Predicting Tensile Load in A36 Steel

Developed a high-precision Machine Learning model to predict ultimate tensile load in A36 steel samples using only physical dimensions as input variables. This solution optimizes structural design processes and reduces dependency on expensive destructive testing.

🔹 Technical Achievement: 98.47% accuracy (R² = 0.9847) with Random Forest Regressor 🔹 Dataset: 20,000 tensile test records for robust model training 🔹 Precision: RMSE of only 53.35 kgf prediction error 🔹 Impact: Enables engineers to predict material behavior before physical testing

Comprehensive EDA revealing material behavior patterns

Advanced feature engineering (Area, Stress calculations)

Professional data visualizations and correlation analysis

Production-ready model with batch prediction capabilities

Tech Stack: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn Engineering Value: Reduces testing costs, accelerates design validation, improves structural safety

This project demonstrates practical ML application in civil engineering, combining domain expertise with advanced predictive modeling to solve real-world engineering challenges.