Iris Flower Classification using Random Forest

This project demonstrates a complete machine learning workflow using the classic Iris dataset. A Random Forest Classifier is trained to identify flower species based on their sepal and petal dimensions. The model is evaluated using a Confusion Matrix and Classification Report to showcase its accuracy and predictive power.

Project Overview

• Objective: Build a model to classify Iris flowers into Setosa, Versicolor, or Virginica.
• Dataset: Built-in Iris dataset from sklearn.datasets.
• Tools Used: Python, Scikit-learn, Pandas, Matplotlib, Seaborn.

Technologies

• Python (Colab)
• scikit-learn
• pandas
• matplotlib
• seaborn

Process

1. Load and explore the Iris dataset
2. Split into training and test sets (70-30)
3. Train a Random Forest model
4. Predict on test data
5. Evaluate using:
   • Confusion Matrix (with visualization)
   • Classification Report (precision, recall, F1-score)
   • Accuracy Score
6. Feature importance visualization

Sample Output

Confusion Matrix: [[19 0 0] [ 0 13 0] [ 0 0 13]]

Accuracy: 100%

Conclusion

The Random Forest model achieved perfect classification on the test set. This notebook demonstrates fundamental machine learning concepts and is a strong foundation for future model comparison, tuning, or deployment.

Files

• IrisDataset.ipynb: The full Colab notebook
• README.md: This file

License

MIT License