🌸 Iris Flower Classifier & Visualizer

An interactive machine learning and data visualization web app using Streamlit, Scikit-learn, Pandas, and Seaborn. This project classifies Iris flower species and visualizes their feature relationships.

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📌 Project Overview

This project is based on the classic Iris flower dataset. It:

• Trains a Logistic Regression model on iris features.
• Shows model accuracy and a confusion matrix.
• Allows real-time prediction of species based on input.
• Offers interactive visualizations such as:
  • Pairplot
  • Violinplot
  • Distribution charts

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📂 Dataset

• Name: Iris Flower Dataset
• Source: Kaggle - UCI Iris Dataset
• File Used: Iris.csv
• Features:
  • SepalLengthCm
  • SepalWidthCm
  • PetalLengthCm
  • PetalWidthCm
  • Species (Target)

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🧠 Tools & Libraries Used

Tool	Purpose
Python	Core programming language
Streamlit	Web framework for interactive UI
Pandas	Data loading and manipulation
Seaborn/Matplotlib	Visualizations
Scikit-learn	ML model (Logistic Regression), metrics

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⚙️ Project Features

• Load and display the raw dataset
• Visualize feature relationships
• Train and evaluate Logistic Regression model
• Show model accuracy and confusion matrix
• Predict flower species based on user input

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🚀 How to Run the Project Locally

✅ 1. Clone the Repository

git clone https://github.com/your-username/iris-classifier.git
cd iris-classifier


# 🌸 Iris Flower Classifier & Visualizer

An interactive machine learning and data visualization web app using **Streamlit**, **Scikit-learn**, **Pandas**, and **Seaborn**. This project classifies Iris flower species and visualizes their feature relationships.

---

## 📌 Project Overview

This project is based on the classic **Iris flower dataset**. It:

* Trains a **Logistic Regression** model on iris features.
* Shows model accuracy and a **confusion matrix**.
* Allows real-time prediction of species based on input.
* Offers **interactive visualizations** such as:

  * Pairplot
  * Violinplot
  * Distribution charts

---

## 📂 Dataset

* **Name:** Iris Flower Dataset
* **Source:** [Kaggle - UCI Iris Dataset](https://www.kaggle.com/datasets/uciml/iris)
* **File Used:** `Iris.csv`
* **Features:**

  * `SepalLengthCm`
  * `SepalWidthCm`
  * `PetalLengthCm`
  * `PetalWidthCm`
  * `Species` (Target)

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## 🧠 Tools & Libraries Used

| Tool               | Purpose                                 |
| ------------------ | --------------------------------------- |
| Python             | Core programming language               |
| Streamlit          | Web framework for interactive UI        |
| Pandas             | Data loading and manipulation           |
| Seaborn/Matplotlib | Visualizations                          |
| Scikit-learn       | ML model (Logistic Regression), metrics |

---

## ⚙️ Project Features

* Load and display the raw dataset
* Visualize feature relationships
* Train and evaluate Logistic Regression model
* Show model accuracy and confusion matrix
* Predict flower species based on user input

---

## 🚀 How to Run the Project Locally

### ✅ 1. Clone the Repository

```bash
git clone https://github.com/your-username/iris-classifier.git
cd iris-classifier

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✅ 2. Create & Activate Virtual Environment

# Create virtual environment
python -m venv venv

# Activate on PowerShell
.\venv\Scripts\Activate.ps1

# Or on CMD
venv\Scripts\activate.bat

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✅ 3. Install Dependencies

pip install -r requirements.txt

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✅ 4. Download Dataset

• Download from Kaggle Dataset Page
• Place Iris.csv in the root project directory

Your folder should look like:

iris-classifier/
├── app.py
├── Iris.csv
├── requirements.txt
└── README.md

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✅ 5. Run the Streamlit App

streamlit run app.py

Then visit http://localhost:8501 in your browser.

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🧾 Sample requirements.txt

streamlit
pandas
numpy
matplotlib
seaborn
scikit-learn

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🗈️ Screenshot (Optional)

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📈 Model Info

• Algorithm Used: Logistic Regression

• Train/Test Split: 80/20

• Evaluation: Accuracy Score, Confusion Matrix

• Input Features:

  • Sepal Length
  • Sepal Width
  • Petal Length
  • Petal Width

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👨‍💻 Author

Rohan Hake Final Year IT Student | Python & Web Developer 📧 LinkedIn

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📄 License

This project is licensed under the MIT License - feel free to use and modify.