A deep learning web app , made using streamlit. Web app can deployed straight from Google collab

This project is a Streamlit-based web application for flower classification using a deep learning model trained from scratch. The web app allows users to upload an image of a flower and predicts the flower species

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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.

End-to-end Iris flower classification project using Logistic Regression, deployed as an interactive Streamlit web app.

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This is an Iris Flower Classification app developed using Streamlit(a pure python library for web app development).

This project implements an end-to-end Image Classification pipeline using the Oxford 102 Flower Dataset, leveraging Transfer Learning (EfficientNetB0) for robust feature extraction and fine-tuning. The trained model is deployed via a Streamlit web app for real-time image predictions.

A Flower Classification Web App built with Streamlit that allows users to upload flower images and get real-time classification results using a pre-trained machine learning model.

Iris Flower Classification Web App using Streamlit

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Iris flower classification converted to Web App using Streamlit

Iris flower classification web app using ML and Streamlit.

Iris flower classification web app with Streamlit and scikit-learn

FloralNet is a Streamlit app using TensorFlow for easy flower image classification. It identifies five flower types: Daisy, Dandelion, Rose, Sunflower, and Tulip, showcasing AI's capability in practical solutions

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Iris flower classification web app built with Python, Scikit-learn, and Streamlit.

Machine Learning Iris Flower Classification Web App built with Python and Streamlit.

A simple Streamlit web app for Iris flower classification using scikit-learn

A Streamlit web app for iris flower classification using a trained machine learning model.

Predicted the species of Iris Flower using a supervised (classification) method and created Web-App using Streamlit

Machine Learning Web App for Iris Flower Classification using Scikit-learn and Streamlit with real-time prediction.

Iris flower classification app using Perceptron algorithm. Interactive Streamlit web application with Docker deployment for machine learning demonstration.

Iris Flower Classification Web App using Machine Learning and Streamlit. The application predicts the species of an Iris flower based on sepal and petal measurements.

A Streamlit app built for teaching basic MLOps (model integration into web apps) using the Iris Flower Classification Task as an example

🌸 Iris Flower Classification ML Model | Oasis Infobyte Internship Task 1 Machine learning model with Streamlit web app for classifying iris flower species with 96%+ accuracy.

Interactive ML web app for iris flower classification with training, prediction, and analytics features. Built with Streamlit and scikit-learn.

🌸 Streamlit web app for Iris flower classification using a Random Forest model with real‑time predictions and visual insights.

🌸 Streamlit web app for Iris flower classification using a Random Forest model with real‑time predictions and visual insights.

Iris Flower Classification App: A machine learning web application that predicts iris flower species based on measurements using a Random Forest classifier. Built with Streamlit, Scikit-learn, and deployed on Streamlit Cloud.