Iris Dataset Analysis Web App

A fully functional web application that performs in-depth Exploratory Data Analysis (EDA) on the classic Iris flower dataset. Built with Python and Flask, the app dynamically generates statistical insights and visualizations to make the data easily understandable.

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About the Project

The goal of this project is to demonstrate a complete data science workflow — from initial dataset exploration to deploying a user-friendly web application.

The app provides an interactive interface for exploring the Iris dataset’s:

• Features
• Distributions
• Relationships

The frontend is clean, modern, and responsive, featuring a light/dark theme switcher for better user experience.

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Features

• Descriptive Statistics Summary table with mean, standard deviation, min, max, etc.

• Species Distribution Count of each Iris species.

• Dynamic Visualizations

  • Histograms: Feature distribution.
  • Box Plots: Compare feature values across species.
  • Pair Plot: Relationships between all features, colored by species.

• Responsive Design Works seamlessly on desktop and mobile devices.

• Theme Switcher Light/dark mode toggle.

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Tech Stack

Backend: Python, Flask Data Analysis: Pandas, Scikit-learn Data Visualization: Matplotlib, Seaborn Frontend: HTML, Tailwind CSS, JavaScript Deployment: Render, Gunicorn

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Getting Started

Prerequisites

• Python 3.9+
• pip (Python package manager)

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Installation

1️⃣ Clone the Repository

git clone https://github.com/shreyas277092004/iris-analysis-app.git
cd iris-analysis-app

2️⃣ Create & Activate Virtual Environment

For Windows:

python -m venv venv
venv\Scripts\activate

For macOS/Linux:

python3 -m venv venv
source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Application

python app.py

5️⃣ Open in Browser Visit:

http://127.0.0.1:5000