AI Algorithm Performance Evaluation on Persona Dataset | Research Project

Led an independent research initiative to evaluate the effectiveness of various machine learning classifiers in predicting outcomes using a structured persona dataset. Focused on comparing model performance through rigorous experimentation, I applied algorithms including Extra Trees Classifier (ETC), Random Forest Classifier, and others such as K-Nearest Neighbors (KNN) and Support Vector Machines (SVM).

Key contributions:

Preprocessed and engineered features to optimize model performance.

Conducted hyperparameter tuning using GridSearchCV and cross-validation techniques.

Assessed models based on accuracy, precision, recall, and F1-score to determine the best fit.

Discovered the top-performing algorithm and its optimal parameters for this dataset, backed by data-driven evidence.

This project strengthened my expertise in supervised learning, model evaluation, and data-centric experimentation—equipping me with practical skills in algorithm benchmarking and research-based model selection.