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.

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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.

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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.

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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