Iris Flower Classification 🌸

This project uses machine learning to classify Iris flower species based on four features:

• Sepal length
• Sepal width
• Petal length
• Petal width

The model was trained on the classic Iris dataset using a Random Forest Classifier and achieved 100% accuracy on the test set.

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📊 Project Workflow

• Exploratory Data Analysis (EDA)
• Label Encoding
• Train-Test Split (80/20)
• Model Training using RandomForestClassifier
• Model Evaluation (Accuracy, Confusion Matrix, Classification Report)
• Feature Importance Visualization

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📁 Files Included

• Iris_Classification_Project.py — Main Python script with full code
• confusion_matrix.png — Model performance visualization

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📦 Libraries Used

• Python
• Pandas
• Scikit-learn
• Seaborn
• Matplotlib

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📸 Confusion Matrix Output

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✅ Results

• Accuracy: 100%
• All 3 species were correctly predicted on the test set.
• Petal length and petal width were the most important features.

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🚀 Author

This project was completed as part of a machine learning internship.