🌸 Iris Flower Classification

• A Machine Learning based web application that predicts the species of an iris flower using different features like sepal and petal measurements.

🚀 Project Overview

• This project uses Data Analysis and Machine Learning to classify iris flowers into different species.

• The model is trained on the Iris dataset and deployed using Streamlit for real-time predictions.

🛠 Technologies Used

• Python

• Pandas

• NumPy

• Matplotlib

• Seaborn

• Scikit-learn

• Streamlit

• Pickle

🤖 Machine Learning Model Used

• Random Forest Classifier

📊 Dataset Details

• Dataset: Iris Dataset

• Problem Type: Classification

• Target Variable: species

🔍 Methodology

1️⃣ Data Analysis

• Checked dataset info

• Viewed shape and structure

• Counted species values

2️⃣ Data Visualization

• Bar chart for species distribution

• Pairplot for feature relationships

3️⃣ Model Training

• Split data into train and test

• Applied Random Forest Classifier

• Trained model on dataset

4️⃣ Model Evaluation

• Accuracy Score

• Classification Report

📸 Project Screenshots

📈 Model Performance

• Model achieved high accuracy

• Random Forest performed well on classification

🌐 Deployment

• Model saved using Pickle

• Integrated into Streamlit Web App

Users can:

• Enter flower measurements

• Click Predict

• Get predicted species instantly

▶️ How to Run the Project

##Install dependencies

• pip install -r requirements.txt

##Run Streamlit app

• streamlit run app.py

🔮 Future Improvements

• Add more ML models

• Improve UI design