Predicting Irrigation Need – Kaggle Competition

Developed a high-performance Irrigation Prediction model as part of a Kaggle competition, focusing on delivering accurate predictions using advanced techniques. Instead of following the common approach used in the competition, I implemented a Deep Learning model to handle structured data and achieve better performance. Key Highlights: Built and optimized a Deep Learning model Performed data preprocessing and feature handling Applied hyperparameter tuning for improved accuracy Results: Achieved a 96.308 Kaggle score with strong competitive performance Tools & Technologies: Python, TensorFlow/Keras, Pandas, NumPy, Scikit-learn This project demonstrates the ability to apply innovative solutions and leverage Deep Learning in non-traditional use cases.