This project involves training and testing machine learning models across various datasets to identify the best-performing model and configuration. The core objective is to analyze how different models behave with different types of data and determine the most effective model ("best fit") based on evaluation metrics like accuracy, precision, recall, F1-score, and/or other domain-specific measures.
- Supports multiple datasets with EDA
- Implements various machine learning algorithms like SVM, RF, boosting etc...
- Evaluates models using standard performance metrics
- Automatically selects and highlights the best-performing model
- Visualizes results for better insights and comparison
- Python
- Scikit-learn
- Pandas & NumPy
- Matplotlib & Seaborn
- Jupyter Notebooks
- Accuracy
- Precision
- Recall
- F1-Score