🌸 Iris Classification using Random Forest – SageMaker Studio Lab

This project implements a complete machine learning pipeline to classify iris flower species using the classic Iris dataset. The workflow is built and tested on Amazon SageMaker Studio Lab, simulating an end-to-end ML project lifecycle from preprocessing to evaluation.

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📌 Project Overview

• Objective: Predict the iris species (Setosa, Versicolor, Virginica) based on sepal and petal dimensions.
• Algorithm Used: Random Forest Classifier
• Platform: SageMaker Studio Lab (CPU instance)
• Dataset: Iris dataset (150 samples, 4 features, 3 classes)

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🔧 Tools & Libraries

• Python 3
• Scikit-learn
• Pandas
• NumPy
• Matplotlib / Seaborn (optional for visualization)
• SageMaker Studio Lab (environment)

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🧪 Pipeline Steps

1. Import Dataset

2. Data Exploration & Visualization

3. Preprocessing & Feature Selection

4. Train-Test Split

5. Model Training (Random Forest)

6. Prediction & Evaluation

   • Accuracy
   • Confusion Matrix
   • Classification Report

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🎯 Results

• Model Used: Random Forest Classifier
• Accuracy Achieved: 90%
• Training Time: Under 1 second (due to small dataset)

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📁 Folder Structure

 ├️ 📄 iris_classifier_sagemaker.ipynb
 ├️ 📄 README.md

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🚀 How to Run

1. Open SageMaker Studio Lab
2. Upload iris_classifier.ipynb
3. Choose CPU runtime
4. Run each cell to execute the pipeline

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🧐 Learning Outcomes

• End-to-end pipeline construction using Scikit-learn
• Using Random Forest for multi-class classification
• Model evaluation techniques and interpretation
• Hands-on with SageMaker Studio Lab (AWS-hosted Jupyter environment)

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🔮 Future Work

• Integrate with AWS SageMaker SDK for deployment
• Compare with other models like SVM, XGBoost, KNN
• Add interactive dashboard using Gradio or Streamlit
• Perform hyperparameter tuning for improved accuracy
• Expand to more complex, real-world datasets