Mushroom Classification – Kaggle Competition
تفاصيل العمل
Developed a high-performance machine learning model to classify mushrooms as edible or poisonous using a large-scale dataset (3M+ rows). Performed end-to-end data preprocessing, exploration, and visualization, with One-Hot Encoding for categorical features. Built and optimized an XGBoost model using GridSearch, achieving ~99% accuracy on training and test data and ~97% on Kaggle, demonstrating strong generalization and reliability in a competitive environment.
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