This project implements a Machine Learning model using the Random Forest Classifier to classify iris flowers into different species based on their physical characteristics. The model is trained on the famous Iris dataset available in Scikit-learn and demonstrates the fundamentals of supervised machine learning and classification.
To predict the species of an iris flower using the following features:
- Sepal Length
- Sepal Width
- Petal Length
- Petal Width
The model classifies flowers into:
- Iris Setosa
- Iris Versicolor
- Iris Virginica
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Data Loading
- Data Exploration and Analysis
- Data Preprocessing
- Train-Test Split
- Model Training using Random Forest Classifier
- Prediction
- Model Evaluation
- Feature Importance Analysis
- Achieved approximately 95% accuracy on the test dataset.
- Successfully classified all three iris flower species.
- Demonstrated the effectiveness of Random Forest for multiclass classification.
- Automated flower species classification
- Random Forest-based prediction model
- Feature importance visualization
- Clean and beginner-friendly implementation
- High classification accuracy
Iris-Flower-Classification/
│
├── iris_classification.py
├── requirements.txt
├── README.md
git clone https://github.com/yourusername/Iris-Flower-Classification.git
cd Iris-Flower-Classificationpip install -r requirements.txtpython iris_classification.pyThrough this project, I gained practical experience in:
- Supervised Machine Learning
- Classification Algorithms
- Random Forest Models
- Data Visualization
- Model Evaluation
- Feature Importance Analysis
- Python for Data Science
- Hyperparameter Tuning
- Cross-Validation
- Streamlit Web Application
- Deployment on Cloud Platforms
- Comparison with Multiple Classification Algorithms
Kavya Raghuvanshi
BCA (Artificial Intelligence & Machine Learning)
Aspiring AI Engineer | Data Analyst | Python Developer
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