Iris Classification Notebook

https://berkiris.streamlit.app/

This repository contains a simple notebook for Iris classification using Jupyter Notebook and Python, now integrated with Streamlit for interactive web applications.

The Iris dataset is a classic dataset in the field of machine learning. It contains 150 observations of iris flowers, with four features measured for each observation: the lengths and widths of the sepals and petals. The dataset is divided into three classes, each representing a different species of iris.

• notebooks/ - Contains the Jupyter Notebooks for the project.
• data/ - Contains the Iris dataset.
• app/ - Contains the Streamlit application.

• Python (version 3.6 or higher)
• Jupyter Notebook
• Streamlit
• pip

1. Clone the repository:

   git clone https://github.com/demirciberk/Iris.git
   cd Iris

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

1. Navigate to the notebooks/ directory:

   cd notebooks

2. Start Jupyter Notebook:

   jupyter notebook

3. Open and run the Iris_Classification.ipynb notebook.

1. Navigate to the app/ directory:

   cd app

2. Start the Streamlit app:

   streamlit run app.py

3. Open your browser and go to http://localhost:8501 to interact with the Iris classification app.

The notebook and the Streamlit app demonstrate the following steps:

• Loading the Iris dataset
• Exploring the dataset
• Visualizing the dataset
• Building and evaluating a classification model
• Make a streamlit web app to display insights

• The Iris dataset is from the UCI Machine Learning Repository.
• This project is inspired by various online tutorials and resources on machine learning.