Machine learning project for classifying Iris flower species using algorithms like Logistic Regression and Decision Tree. Includes data preprocessing, model training, accuracy comparison, and visualization using Python and scikit-learn.
This project builds a machine learning model to classify Iris flowers into three species: Setosa, Versicolor, and Virginica. The project compares different classification algorithms to evaluate their accuracy.
- Understand classification algorithms
- Train and evaluate ML models
- Compare performance of multiple models
Iris Dataset (built into scikit-learn)
Features include:-
- Sepal length
- Sepal width
- Petal length
- Petal width
- Python
- scikit-learn
- Matplotlib
- Google Colab
- Logistic Regression
- Decision Tree
Both models achieve high accuracy on the Iris dataset.
- Add more classification algorithms
- Use cross-validation
- Build interactive visualization
Open the notebook in Google Colab and run all cells.