Irrigation Need
تفاصيل العمل
accuracy on a Kaggle Playground competition. As part of an AI & Machine Learning program, I designed a robust feature engineering pipeline to handle tabular data using deep learning techniques. 🔍 Key Contributions: Built advanced feature engineering pipeline to extract meaningful insights from raw data Created features such as: Water stress & balance indicators Temperature–humidity interaction Rainfall vs irrigation ratios Rolling averages & trend features Evapotranspiration proxy indices ⚙️ Tools & Technologies: Python Pandas & NumPy Scikit-learn / Deep Learning Data preprocessing & feature engineering 📊 Results: Achieved 95.9% accuracy Improved model performance through data transformation and feature optimization
مهارات العمل
بطاقة العمل
طلب عمل مماثل
