🌸 Iris Flower Classification

An end-to-end Machine Learning project that classifies iris flowers into three species — Iris-setosa, Iris-versicolor, and Iris-virginica — based on petal and sepal measurements. The Iris dataset is a classical dataset used to demonstrate classification algorithms. :contentReference[oaicite:0]{index=0}

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🧠 Project Overview

The Iris Flower Classification project applies supervised learning techniques to identify the species of a flower using features such as:

• Sepal Length (cm)
• Sepal Width (cm)
• Petal Length (cm)
• Petal Width (cm)

This project includes data preprocessing, model training, evaluation, and prediction. :contentReference[oaicite:1]{index=1}

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📁 Project Structure

1.Iris-Flower-Classification/ ├── Iris.csv ├── Iris_Flower_Classification.ipynb ├── model.pkl (if saved) ├── requirements.txt └── README.md

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🛠 Tech Stack

• Python
• pandas
• NumPy
• scikit-learn
• Matplotlib / Seaborn (optional)
• Jupyter Notebook

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🚀 How It Works

1. Load the Iris Dataset – Classic dataset with 150 samples and 3 species. :contentReference[oaicite:2]{index=2}
2. Preprocess Data – Feature selection, scaling (if required).
3. Train/Test Split – Split data into training and testing sets.
4. Train Model – Train a classifier (e.g., Logistic Regression, KNN, SVM, etc.).
5. Evaluate – Check model performance using accuracy, confusion matrix.
6. Predict – Classify unseen samples.

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📊 Machine Learning Models Used

✔ Logistic Regression
✔ K-Nearest Neighbors
✔ Support Vector Machine
✔ Decision Tree
✔ Random Forest
(Add or remove depending on what your code uses)

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🧪 Model Training & Evaluation

Inside the notebook (Iris_Flower_Classification.ipynb), you will find:

• Data loading and exploratory data analysis
• Feature visualization (e.g., scatter plots)
• Model training and performance evaluation
• Accuracy metrics and classification report

(Refer to the notebook for visuals and performance plots)

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📥 Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/shreyadesai20042009/Iris-project.git
cd Iris-project/1.Iris-Flower-Classification/1.Iris\ Flower\ Classification
2️⃣ Create virtual environment (optional)
python -m venv venv
source venv/bin/activate      # macOS/Linux
venv\Scripts\activate         # Windows
3️⃣ Install dependencies
pip install -r requirements.txt

If requirements file is missing, install manually:

pip install numpy pandas scikit-learn matplotlib seaborn
📈 How to Run

Open and run the Jupyter Notebook:

jupyter notebook Iris_Flower_Classification.ipynb

Follow the steps in the notebook to train the model and make predictions.

📌 Example Prediction
Input: 5.1, 3.5, 1.4, 0.2  
Predicted Species: Iris-setosa