Iris Flower Classification

About

This project was completed as part of my Data Science internship at CodeAlpha.

Problem Statement

Classify Iris flowers into one of three species (setosa, versicolor, virginica) based on their sepal and petal measurements.

Dataset

Built-in Iris dataset from scikit-learn (sklearn.datasets.load_iris) — 150 samples, 4 features (sepal length/width, petal length/width), 3 balanced classes (50 each).

Approach

• Data loading and exploration (no missing values, perfectly balanced classes)
• Visualization: pairplot, feature distributions, correlation heatmap
• Train/test split (80/20, stratified)
• Trained and compared 3 models: Logistic Regression, K-Nearest Neighbors, Decision Tree
• Evaluated with accuracy, confusion matrix, and classification report

Results

• Logistic Regression: 96.67% accuracy
• KNN: 100% accuracy (best model)
• Decision Tree: 93.33% accuracy
• Petal length and petal width were the most important features for separating species

Tools Used

Python, pandas, scikit-learn, matplotlib, seaborn

How to Run

1. Clone this repo
2. Install requirements: pip install -r requirements.txt
3. Open notebook/iris_flower_classification.ipynb in Jupyter or Google Colab
4. Run all cells

Video Explanation

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