How tejsaxena-0811/tejsaxena_iris_dataset_ml works
This notebook project explores the Iris measurements and compares two species classifiers. The documented workflow covers exploratory visualization, feature/target preparation, an 80/20 split, Logistic Regression with five-fold cross-validation, and Random Forest; both models are evaluated, with a confusion-matrix heatmap specifically noted for Random Forest. The tree contains only the notebook, and no source excerpts were available, so relationships reflect README documentation rather than inspected implementation details.
Main entry points: Iris_Dataset.ipynb Train/test split
Components 9
Exploration
- EDA visualizations
Iris_Dataset.ipynb
Modeling
- Feature preparation
Iris_Dataset.ipynb - Train/test split
Iris_Dataset.ipynb - Logistic Regression
Iris_Dataset.ipynb - Random Forest
Iris_Dataset.ipynb
Evaluation
- Logistic evaluation
Iris_Dataset.ipynb - Forest evaluation
Iris_Dataset.ipynb
Other components
- Analyst
- Iris dataset
Connections 8
- Analyst → EDA visualizations: explores
- Iris dataset → EDA visualizations: visualizes
- Iris dataset → Feature preparation: provides data
- Feature preparation → Train/test split: prepares features
- Train/test split → Logistic Regression: trains
- Train/test split → Random Forest: trains
- Logistic Regression → Logistic evaluation: evaluates
- Random Forest → Forest evaluation: evaluates