This is a web application that classifies iris flowers based on their sepal length, sepal width, petal length, and petal width. The app is built using Streamlit and the classification model is trained using Jupyter notebook.
Python 3.6+
Streamlit
Scikit-learn
Pandas
Numpy
- Clone the repository to your local machine.
- Install the required libraries using
pip install -r requirements.txt. - Run the app using
streamlit run app.py.
Once you run the app using streamlit run app.py, a browser window will open up with the app.
Enter the values for sepal length, sepal width, petal length, and petal width in the provided input fields.
Click on the 'Classify' button to get the predicted class for the input values.
The classification model is trained on the Iris dataset, which is a popular dataset for classification tasks. The dataset contains 150 samples with 3 classes of iris flowers (50 samples for each class).
The classification model is a simple logistic regression model, trained on the Iris dataset. The model achieves an accuracy of 97.8% on the test set.
The Iris dataset is taken from the UCI Machine Learning Repository.