TechLabs Sommer26: Machine Learning Fundamentals

Welcome to the learning phase of your Data Science journey! Over the next 6 weeks, you will learn the fundamentals of Python, Data Wrangling, and Machine Learning by solving the Spaceship Titanic challenge.

🚀 The Mission

The year is 2912. The spaceship Titanic was an interstellar passenger liner. While rounding Alpha Centauri, it collided with a space-time anomaly hidden within a dust cloud. Almost half of the passengers were transported to another dimension!

Your goal: Predict which passengers were transported using records recovered from the ship’s damaged computer system.

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📂 Project Structure

├── data/               # Project datasets
│   └── train.csv       
├── src/                # Your code goes here
│   ├── assignment_1.py 
│   ├── assignment_2.py 
│   └── assignment_3.py 
├── tests/              # Automated tests (don't modify)
├── requirements.txt    # Required libraries
└── .github/            # GitHub Actions logic

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🛠 Setup Instructions

1. Fork this repository: Click the "Fork" button at the top right of this GitHub page. This creates a copy of the project in your own GitHub account.

2. Clone your fork:

   git clone https://github.com/[your GitHub]/Assignments-DataScience.git
   cd Assignments-DataScience

3. Set up a Virtual Environment (Recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

4. Install Dependencies:

   pip install -r requirements.txt

5. The Data: The train.csv file is already provided in the data/ folder. You can use this for all your analysis and modeling. Please do not delete the file, as you will need it throughout the assignments. Also make sure, that you do not override this file and keep it in this version.

6. Testing: You can test your own code by running the corresponding terminal command as it is written for each assignment. Please do not delete or change the code inside the tests/ folder

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📝 Assignments

1. Assignment 1: Data Logistics (Week 2)

Focus: Data loading, cleaning missing values, and type conversion.

• Run Tests: pytest tests/test_assignment_1.py

2. Assignment 2: The Data Scientist (Week 4)

Focus: Feature engineering (vectorization, string parsing) and statistical outlier detection (IQR).

• Run Tests: pytest tests/test_assignment_2.py

3. Assignment 3: The Navigator (Week 6)

Focus: Machine learning pipeline, target encoding, data splitting, and model evaluation.

• Run Tests: pytest tests/test_assignment_3.py

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📤 How to Submit

1. Create a new branch for each assignment in your fork:

   git checkout -b feature/assignment-X-yourname

2. Commit and push your changes to your fork.
3. Open a Pull Request (PR) from your fork's branch to the original repository's main branch.
4. The GitHub Action will automatically run the tests.
   • ❌ Note: Your PR will not be merged into the main repository. This is intentional! The PR serves as your submission, and the test results (green checkmark ✅) are your grade.