🌸 Iris Flower Classification App

An advanced Machine Learning web application built using Streamlit that classifies Iris flowers using a Random Forest model.
The app includes interactive UI controls, real-time predictions, probability confidence, and 2D & 3D visualizations powered by Plotly.

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🚀 Project Overview

• Dataset: Iris Dataset (from sklearn.datasets)
• Model Used: Random Forest Classifier
• Framework: Streamlit
• Visualization: Plotly (2D + 3D interactive charts)
• Model Persistence: Joblib

This project demonstrates a complete ML pipeline:

Dataset → Model Training → Model Saving → UI-based Prediction & Visualization

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📊 Dataset Information

• Name: Iris Dataset
• Source: sklearn.datasets.load_iris
• Total Samples: 150
• Features:
  • Sepal Length (cm)
  • Sepal Width (cm)
  • Petal Length (cm)
  • Petal Width (cm)
• Target Classes:
  • Setosa
  • Versicolor
  • Virginica

The dataset is used to train a Random Forest Classifier for multi-class classification.

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🤖 Model Details

• Algorithm: Random Forest Classifier
• Library: scikit-learn
• Task: Multi-class classification
• Model File: iris_rf_model.joblib

The trained model is loaded inside the Streamlit app and used for real-time predictions.

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📦 Imports Used

🔹 Streamlit App (app.py)

import streamlit as st
import numpy as np
import pandas as pd
import joblib
import plotly.express as px
import plotly.graph_objects as go
from sklearn.datasets import load_iris