Iris Flower Classification

CodeAlpha Data Science Internship — Task 1

Project Overview

A machine learning model that classifies Iris flowers into three species (Setosa, Versicolor, Virginica) based on sepal and petal measurements.

Dataset

• Source: Scikit-learn Built-in Iris Dataset
• Details: 150 samples, 3 species, 4 features

Steps Performed

1. Data Loading: Used Pandas to handle the dataset.
2. EDA: Visualized feature relationships using Seaborn & Matplotlib.
3. Train/Test Split: Divided data (80/20) for fair evaluation.
4. Model Training: Trained a Random Forest Classifier (100 trees).
5. Evaluation: Achieved 100% accuracy on the test set.

Results

• Model Accuracy: 100%
• Confusion Matrix: Zero misclassifications across all 30 test samples.

Libraries Used

• Pandas
• Scikit-learn
• Matplotlib
• Seaborn

Author

Syed Fazeel Ahmed — Data Science Intern at CodeAlpha