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.