itsluckysharma01/Iris-flower-Prediction-With-MachineLearning

Iris Flower Detection Web Application

This is a simple Flask web application that uses a machine learning model to predict the species of iris flowers based on measurements.

Files and Structure

• —app.py - The main Flask application
• —iris_model.pkl / new_iris_model.pkl - The trained machine learning model
• —templates/ - Folder containing HTML templates
• —form.html - Input form for flower measurements
• —result.html - Page showing prediction results
• —create_new_model.py - Script to create a fresh model if needed
• —test_app.py - Script to test the application functionality
• —run_app.bat - Windows batch file to easily run the application

How to Run

1. 1.Double-click on run_app.bat or run python app.py in your terminal
2. 2.Open your web browser and go to http://127.0.0.1:5000
3. 3.Enter the flower measurements and click "Predict Flower Species"

Sample Measurements

Iris Setosa

• —Sepal Length: 5.1 cm
• —Sepal Width: 3.5 cm
• —Petal Length: 1.4 cm
• —Petal Width: 0.2 cm

Iris Versicolor

• —Sepal Length: 6.0 cm
• —Sepal Width: 2.7 cm
• —Petal Length: 4.2 cm
• —Petal Width: 1.3 cm

Iris Virginica

• —Sepal Length: 6.8 cm
• —Sepal Width: 3.0 cm
• —Petal Length: 5.5 cm
• —Petal Width: 2.1 cm

Troubleshooting

If you encounter issues:

1. 1.Run python test_app.py to verify the model is working correctly
2. 2.Check that you have all the required Python packages installed:
3. 3.Flask
4. 4.scikit-learn
5. 5.joblib
6. 6.numpy
7. 7.Try generating a new model with python create_new_model.py