AI/ML Coding Interview Assessment Platform

A standardized environment for conducting AI/ML coding interviews using the Kaggle Pokemon dataset. This platform allows candidates to demonstrate their data analysis and model building skills in a live interview setting.

Features

• Standardized Assessment: Structured prompts to evaluate candidates' skills in data exploration, statistical analysis, and model building
• Cross-Platform Compatibility: Works seamlessly across macOS, Linux, and Windows
• Python Version Support: Compatible with Python 3.9-3.12
• Flexible Environment: Supports both external Jupyter notebooks and VSCode's integrated notebook experience
• Essential Libraries: Includes pandas, numpy, scikit-learn, tensorflow, pytorch, matplotlib, seaborn, and other common data science packages

Assessment Structure

The assessment follows a structured four-step process:

1. Data Exploration: Evaluate the candidate's familiarity with Python and data analysis libraries
2. Distribution Analysis: Assess statistical understanding and visualization skills
3. Type Prediction Model: Evaluate model building skills for classification
4. Attack Prediction Model: Assess model building skills for regression

Getting Started

Prerequisites

• Python 3.9 or higher

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/agate.git
   cd agate
   

2. Run the setup script to create a virtual environment and install dependencies:

   python scripts/setup_env.py
   

3. Activate the virtual environment:

   • Windows: .venv\Scripts\activate
   • Unix/Linux/MacOS: source .venv/bin/activate

Manual Installation

If you prefer to set up the environment manually:

1. Create a virtual environment:

   python -m venv .venv
   

2. Activate the virtual environment:

   • Windows: .venv\Scripts\activate
   • Unix/Linux/MacOS: source .venv/bin/activate

3. Install the package:

   pip install -e .
   

Usage

1. Activate the virtual environment as described above.

2. Open the assessment notebook:

   jupyter notebook notebooks/pokemon_assessment.ipynb
   

   Or with VSCode:

   code notebooks/pokemon_assessment.ipynb
   

3. Follow the prompts in the notebook to guide the candidate through the assessment.

Project Structure

agate/
├── datasets/              # Dataset files
│   └── pokemon/           # Pokemon dataset
├── interview/             # Main module package
│   ├── data.py            # Data loading and processing
│   ├── visualization.py   # Plotting and visualization
│   ├── models.py          # Model building utilities
│   └── utils.py           # General utilities
├── notebooks/             # Jupyter notebooks
│   ├── pokemon_assessment.ipynb  # Main assessment
│   └── solutions/         # Reference solutions
├── scripts/               # Utility scripts
│   └── setup_env.py       # Environment setup script
└── tests/                 # Test suite

Data Conventions

The platform uses a consistent naming convention for dataset columns:

• All column names are lowercase (e.g., attack instead of Attack)
• Multi-word column names use underscores (e.g., sp_attack instead of Sp. Atk)
• Type columns are named type1 and type2
• Stat columns include: hp, attack, defense, sp_attack, sp_def, speed
• Other columns include: weight_kg, height_m, is_legendary, generation

This convention makes the code more pythonic and easier to work with programmatically.

Development

Running Tests

pytest

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• The Pokemon dataset is sourced from Kaggle and is in the public domain (CC0)