This repository contains a Streamlit application for predicting the types of Iris flowers based on their sepal and petal measurements. The Iris flower dataset is a classic machine learning dataset that is commonly used for introductory classification tasks.
The Streamlit application provides an interactive interface where users can input the measurements of a new Iris flower, including sepal length, sepal width, petal length, and petal width. Based on these inputs, the application utilizes a trained machine learning model to predict the type of Iris flower.
- Interactive User Interface: The Streamlit application offers a user-friendly interface that allows users to input the measurements of an Iris flower and obtain predictions instantly.
- Machine Learning Model: The application employs a pre-trained machine learning model, which has been trained on the famous Iris flower dataset, to make accurate -predictions based on the input features.
- Real-Time Predictions: Users can see the predicted Iris flower type along with probability scores in real time, providing immediate feedback on the classification outcome.
- Data Visualization: The application also includes visualizations such as scatter plots and histograms, enabling users to explore the Iris flower dataset and gain insights into the relationships between different features.
Contributions to this repository are welcome! If you have any ideas for improvement, bug fixes, or additional features, feel free to open an issue or submit a pull request. Together, we can enhance the functionality and usability of this Iris flower prediction application.
By providing an interactive and visually appealing user experience, this Streamlit application makes it easy for users to predict the types of Iris flowers accurately. It serves as an excellent starting point for learning about classification tasks and showcases the capabilities of Streamlit for building machine learning applications.