- Classified the levels of anxiety among different age groups by various Machine Learning algorithms to predict the level of anxiety.
- Conducted in-depth Exploratory Data Analysis (EDA) on a diverse dataset to gain insights into underlying patterns and trends.
- Implemented and compared various machine learning models, including Logistic Regression, Decision Tree, and Support Vector Machine (SVM), to assess their prediction performance.
- Employed Principal Component Analysis (PCA) to reduce dimensionality and enhance model efficiency.
- Utilized GridSearchCV to fine-tune model hyperparameters and optimize model performance.
- Employed K-Fold Cross Validation to evaluate model accuracy and Area Under the Curve (AUC) scores, providing a robust assessment of model performance.
sudikshanavik/Comparative-Analysis-of-ML-Models
Classified the levels of anxiety among different age groups by various Machine Learning algorithms to predict the level of anxiety.
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