https://github.com/smmariquit/ml-iris-workshop
QCU Python data-science workshop materials — hands-on tutorials covering Pandas, scikit-learn, and scikit-image with the classic Iris dataset.
https://github.com/smmariquit/ml-iris-workshop
data-science iris-dataset machine-learning pandas python scikit-image scikit-learn tutorial workshop
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QCU Python data-science workshop materials — hands-on tutorials covering Pandas, scikit-learn, and scikit-image with the classic Iris dataset.
- Host: GitHub
- URL: https://github.com/smmariquit/ml-iris-workshop
- Owner: smmariquit
- Created: 2025-09-25T04:08:47.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2026-07-04T18:45:58.000Z (5 days ago)
- Last Synced: 2026-07-04T19:11:56.358Z (5 days ago)
- Topics: data-science, iris-dataset, machine-learning, pandas, python, scikit-image, scikit-learn, tutorial, workshop
- Size: 9.77 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Python Data Science Tutorial Collection
A full collection of Python tutorials covering essential data science libraries: **Pandas**, **Scikit-Learn**, and **Scikit-Image**. This repository contains hands-on examples and exercises perfect for learning data manipulation, machine learning, and image processing.
## 📁 Project Structure
```
QCU/
├── README.md # This file
├── polyA_helix_CA.pdb # Protein structure file
├── iris/ # Dataset folder
│ ├── database.sqlite # SQLite database
│ └── Iris.csv # Famous Iris flower dataset
├── pandas/ # Pandas tutorials
│ ├── pandas01.py # Introduction to data structures
│ ├── pandas02.py # Data manipulation basics
│ ├── pandas03.py # Advanced data operations
│ ├── pandas04.py # Data analysis techniques
│ ├── pandas05.py # Data visualization
│ └── pandas06.py # Real-world data processing
├── sklearn/ # Scikit-Learn tutorials
│ └── sklearn_tutorial.py # Complete ML tutorial with Iris dataset
└── skimage/ # Scikit-Image tutorials
└── skimage_tutorial.py # Complete image processing tutorial
```
## 🐼 Pandas Tutorials
The `pandas/` folder contains a progressive series of tutorials covering:
- **pandas01.py**: Introduction to Pandas data structures (Series, DataFrame)
- **pandas02.py**: Data manipulation and basic operations
- **pandas03.py**: Advanced data operations and transformations
- **pandas04.py**: Data analysis and statistical operations
- **pandas05.py**: Data visualization with matplotlib integration
- **pandas06.py**: Real-world data processing examples
### Getting Started with Pandas
```bash
cd pandas
python pandas01.py
```
## 🤖 Scikit-Learn Tutorial
The `sklearn/` folder contains a full machine learning tutorial:
- **sklearn_tutorial.py**: Complete ML workflow using the Iris dataset
- Data loading and exploration
- Data preprocessing and feature scaling
- Multiple ML algorithms (Logistic Regression, Decision Trees, Random Forest, SVM)
- Model evaluation and comparison
- Visualizations and insights
### Running the ML Tutorial
```bash
cd sklearn
python sklearn_tutorial.py
```
**Note**: The tutorial automatically loads the Iris dataset from the `../iris/` directory. If the CSV file is not found, it falls back to scikit-learn's built-in dataset.
## 🖼️ Scikit-Image Tutorial
The `skimage/` folder contains a full image processing tutorial:
- **skimage_tutorial.py**: Complete guide to image processing
- Loading and displaying images
- Basic image operations
- Filtering and enhancement
- Edge detection and feature extraction
- Morphological operations
- Image segmentation
- Real-world applications
### Running the Image Processing Tutorial
```bash
cd skimage
python skimage_tutorial.py
```
## 📊 Dataset Information
### Iris Dataset (`iris/`)
- **Iris.csv**: The famous iris flower dataset
- **database.sqlite**: SQLite version of the dataset
- Contains 150 samples of iris flowers with 4 features each
- Perfect for classification tasks and ML learning
## 🚀 Prerequisites
Make sure you have the following Python libraries installed:
```bash
pip install pandas numpy matplotlib seaborn scikit-learn scikit-image
```
## 📝 Usage Instructions
1. **Start with Pandas**: Begin with the pandas tutorials to understand data manipulation
2. **Progress to ML**: Move to the sklearn tutorial to learn machine learning
3. **Explore Image Processing**: Use the skimage tutorial for computer vision tasks
Each tutorial is self-contained and includes:
- ✅ Detailed explanations and comments
- ✅ Code examples with output
- ✅ Visualizations and plots
- ✅ Progressive difficulty levels
- ✅ Real-world applications
## 🎯 Learning Path
### Beginner
- Start with `pandas01.py` for basic data structures
- Practice with `pandas02.py` for data manipulation
- Explore `sklearn_tutorial.py` for your first ML project
### Intermediate
- Work through all pandas tutorials (`pandas01.py` - `pandas06.py`)
- Complete the sklearn tutorial with different datasets
- Begin with basic image processing in `skimage_tutorial.py`
### Advanced
- Combine techniques from all three libraries
- Modify tutorials for your own datasets
- Experiment with advanced algorithms and techniques
## 🔧 Troubleshooting
- **Import Errors**: Make sure all required libraries are installed
- **File Not Found**: Ensure you're running scripts from the correct directory
- **Dataset Issues**: The sklearn tutorial will automatically fallback to built-in datasets if files are missing
## 📚 Additional Resources
- [Pandas Documentation](https://pandas.pydata.org/docs/)
- [Scikit-Learn Documentation](https://scikit-learn.org/stable/)
- [Scikit-Image Documentation](https://scikit-image.org/)
---
**Happy Learning!** 🎉
*This tutorial collection was organized and documented by GitHub Copilot on September 25, 2025.*