Irrigation Need Prediction using Deep Neural Network

Built a Deep Neural Network using TensorFlow/Keras to predict irrigation needs (Low, Medium, High) from environmental and soil data, achieving high performance in a Kaggle competition.

Applied advanced preprocessing, feature engineering, and class balancing to enhance model accuracy and generalization.

Ranked among competition winners using Stratified K-Fold Cross Validation and robust training strategies for reliable predictions.