Machine Learning Intern
As a Machine Learning Intern, I developed an AI-based Crop and Fertilizer Recommendation System during a remote internship program. My work included managing the entire machine learning pipeline, from data collection to model optimization, focusing on agricultural data. This role required strong proficiency in Python, supervised learning, feature engineering, and model evaluation within real-world constraints. • Designed and implemented machine learning models (Random Forest, SVM, KNN, Decision Tree) to achieve high classification accuracy. • Conducted extensive data collection and cleaning, applying feature engineering across multiple agronomic parameters. • Utilized 5-fold cross-validation and grid search for hyperparameter tuning and model optimization. • Reduced fertilizer recommendation error by 18% compared to baseline methods, delivering interpretable outputs for agricultural decision-making.