Gender Classification Model - Logistic Regression
MY ROLES
TIMELINE
2019
TOOLS USED
Classification, Logistic Regression, SVM, Python, Pandas, Scikit-learn, Decision Boundaries, Maximum-Margin Classifier, Feature Engineering, Statistical Modeling
DESCRIPTION
This project implemented classification algorithms to predict gender based on facial feature measurements. Using logistic regression and support vector machines (SVM), the analysis explored discriminative classification techniques to find optimal decision boundaries for gender prediction. The project compared various classification approaches and evaluated their performance in creating linearly separable boundaries between classes.
OUTCOMES
- Developed a classification model with 87% accuracy for gender prediction
- Identified key facial features that contribute most significantly to gender classification
- Created visualizations of decision boundaries and probability distributions
FEATURES
Discriminative classifier implementation, decision boundary visualization, probability threshold analysis, feature importance evaluation, model comparison framework
CHALLENGES
Identifying the most informative features for gender classification. Finding the optimal balance between model complexity and generalizability. Visualizing high-dimensional decision boundaries in an interpretable way.
APPROACH
Preprocessed facial measurement data and engineered relevant features. Implemented logistic regression as a baseline classifier. Explored maximum-margin classifiers including SVMs to find optimal decision boundaries. Visualized decision boundaries and probability distributions. Evaluated models using accuracy metrics and cross-validation to ensure robust performance.