Iris Flower Classification with Streamlit App

  • Tech Stack: Python, Machine Learning, Streamlit
  • Github URL: Project Link

This Streamlit app is designed to showcase the classification of Iris flowers using machine learning models.

Overview:

  • The Iris dataset is a classic dataset used for machine learning and statistics. It consists of 150 samples of iris flowers, each with four features: sepal length, sepal width, petal length, and petal width. The task is to classify each sample into one of three species: Setosa, Versicolor, or Virginica.

Installation:

  • Clone this repository: 
    git clone https://github.com/Anshg07/iris-flower-classification.git
  • Navigate to the project directory: 
    cd iris-flower-classification
  • Install the required dependencies: 
    pip install -r requirements.txt

Usage:

  • After installing the dependencies, you can run the Streamlit app by executing the following command in your terminal: 
    streamlit run app.py
    This will start the Streamlit app in your default web browser, where you can interact with the interface to classify Iris flowers using different machine learning models.

Features:

  • Choose between different machine learning models (e.g., Logistic Regression, Support Vector Machine, Random Forest).
  • Input custom values for sepal length, sepal width, petal length, and petal width to predict the species of Iris flower.
  • Visualize the predictions with a confusion matrix and classification report.

Data:

  • The Iris dataset used in this app is included in the iris.csv file in the data directory. It contains 150 samples with four features each (sepal length, sepal width, petal length, and petal width), along with the corresponding species labels.

Models:

  • Three machine learning models are available for classification:
    • Logistic Regression
    • Support Vector Machine (SVM)
    • Random Forest
  • These models are trained on the Iris dataset and can be selected from the sidebar in the Streamlit app.

About:

  • This app was created by Ansh Gupta as a demonstration of building machine learning applications with Streamlit.