Getting Started. Your First ML Project

This guide walks you through a first machine learning project in about 30 minutes.

For the full curriculum map, stage order, and exit gates, read START-HERE.md and FOUNDATION_AND_JOB_READINESS.md.

Quick Start: Iris Classification

The Iris flower classification project is a small, clean first ML run. Follow these steps:

Step 1: Set Up Environment

# Create virtual environment
python -m venv ml-env

# Activate (Windows)
ml-env\Scripts\activate

# Activate (Mac/Linux)
source ml-env/bin/activate

# Install required packages
pip install numpy pandas matplotlib seaborn scikit-learn

Step 2: Run the Project

Navigate to the project directory:

cd 16-projects-beginner/project-02-iris-classification

Run the complete implementation:

python iris_classification.py

Or follow along with the step-by-step guide in README.md.

Step 3: What You'll See

The script will:

1. Load and explore the Iris dataset
2. Create visualizations (pair plots, box plots, heatmaps)
3. Train 3 different models
4. Compare their performance
5. Show confusion matrix for the best model
6. Make predictions on new data

Expected Output

• On this clean toy dataset, models often land above about 95% accuracy. Treat that as a demo result, not a general ML promise.
• You will usually see visualization images saved in the project folder
• The script will pick a best model among the ones it trains
• It will show predictions for a few new flower measurements

Understanding the Results

• Accuracy: Percentage of correct predictions
• Confusion Matrix: Shows which classes are confused with each other
• Model Comparison: Visual comparison of different algorithms

Next Steps

1. Modify the code. Try different models
2. Experiment with different train/test splits
3. Add your own features
4. Move to the next project: House Price Prediction

Troubleshooting

Import errors?

• Install from the repository root: pip install -r requirements.txt (run this from the top-level road-to-machine-learning folder, not inside a project subfolder)

Plots not showing?

• On some systems, you may need: plt.show() at the end
• Check if images are saved in the current directory

Need help?

• Check the project README for detailed explanations
• Review the code comments
• Open an issue on GitHub

Why Start Here?

• Simple Dataset: Well-known, clean data
• Clear Results: Easy to understand outcomes
• Complete Example: Full working code provided
• Quick Win. See results in minutes on a toy dataset.

Ready? Go to 16-projects-beginner/project-02-iris-classification/ and start coding!