Streamlit-Based Machine Learning Application for Flower Classification and Car Price Prediction

This research paper presents a Streamlit-based machine learning application designed for real-time prediction tasks through an interactive web interface. The project includes two modules: Flower Classification using the Iris dataset and Car Price Prediction using user-defined parameters. The system is developed using Python libraries such as Streamlit, NumPy, Pandas, and Scikit-learn. Users can enter input values and receive instant predictions through a user-friendly dashboard. The flower classification model uses Logistic Regression and achieves high accuracy, while the car price prediction module applies regression techniques. This project demonstrates how machine learning models can be deployed into practical applications for academic and real-world use.