Iris Flower Classification System 😄

Description

Iris flower classification is a very popular machine learning project. The iris dataset contains three classes of flowers, Versicolor, Setosa, Virginica, and each class contains 4 features, ‘Sepal length’, ‘Sepal width’, ‘Petal length’, ‘Petal width’.

Objectives

The aim of the iris flower classification is to predict flowers based on their specific features.

Life Cycle of Machine Learning Project

Life Cycle of implementing machine learning application.

• Gathering the Data
• Data Preparation
• Data Preprocessing
• Create Model
• Evaluate Model
• Deploy the model

Dataset

The Iris Flower Dataset has been used for this purpose, taken from the Kaggle. link is below.

• dataset

Homepage (Responsive)

🛠️ Requirements

• Python (Programming Language version 3.7+)
• Flask (Python Backend Framework)
• sklearn (Machine Learning Library)
• pandas (Python Library for Data operations)
• NumPy (Python Library for Numerical operations)
• VS code (IDE)
• Azure (Cloud platform)
  
  

How to run this code...

• Create virtual environment

conda create -n myenv python=3.8

• Activate the environment

conda activate myenv

• Install the packages

pip install -r requirements.txt

• Run the app

python app.py

• Navigate to URL http://127.0.0.1:5000/

• Enter valid values in all input boxes and hit Predict.

If everything goes well, you should be able to see the predcited salary Class on the HTML page!

Authors

Devansh Mistry - Linkedin

If you like this project, please do give the star. If you have any suggestions or issues, please drop me a message.