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Iris Flower Classification

This repository contains my implementation of the Iris Flower Classification Project, completed as part of my AI & ML Internship at CODEXINTERN.
The goal is to predict the species of an Iris flower — Setosa, Versicolor, or Virginica — using sepal and petal measurements.

Exploratory Data Analysis (EDA)

Performed detailed EDA including:

  • Dataset overview (head, tail, info)
  • Scatterplots between features:
    • Sepal Length vs Sepal Width
    • Petal Length vs Petal Width
    • Sepal Length vs Petal Length
    • Sepal Width vs Petal Width
  • Key Insights:
    • Petal features are the most important for classification.
    • Setosa is distinctly separable, while Versicolor and Virginica overlap slightly.

Models Implemented

Three supervised learning models were trained, evaluated, and compared:

Model Accuracy
K-Nearest Neighbors (k=3) 1.0 (100%)
Logistic Regression 1.0 (100%)
Decision Tree 1.0 (100%)
  • All models achieved perfect classification on the test set.
  • This confirms that the Iris dataset is highly separable with standard ML models.

Evaluation

Each model was evaluated using:

  • Accuracy
  • Confusion Matrix (heatmap with Seaborn)
  • Classification Report (Precision, Recall, F1-score)

Installation & Usage

Clone the repository and set up the environment:

Installation & Usage

1. Clone the repository and set up the environment

git clone https://github.com/varma1221/Iris-Flower-Classification.git
cd Iris-Flower-Classification

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate   # On Windows use: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Acknowledgements

Task Provider: CODEXINTERN

Dataset: Iris dataset - UCI Repository

Libraries: scikit-learn, pandas, seaborn, matplotlib