Fizyr Assessment

Abstract

1. Introduction

Air Quality Analysis CLI Assessment Project for Fizyr

2. Objective

Air Quality Analysis CLI Assessment Project for Fizyr

This project demonstrates how Rust, Docker, PostgreSQL can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Rust toolchain (rustup / cargo)
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/mohammadzainabbas/fizyr-assessment.git
cd fizyr-assessment

1. Setup Environment:
2. Install and run PostgreSQL locally.
3. Create a database (e.g., createdb air_quality).
4. Set environment variables (export in your shell or use a .env file and a tool like dotenv-cli):
5. Build & Run:
6. Run Tests:
7. Unit Tests: (Located in src/cli/commands.rs)
8. Database Integration Tests: (Located in src/db/postgres.rs)

# .env (Example - Adjust DATABASE_URL for your local setup)
DATABASE_URL=postgres://your_user:your_password@localhost:5432/air_quality
OPENAQ_KEY=your_actual_api_key_here
RUST_LOG=info # Optional: Set log level (e.g., debug, trace)

# Build the project
cargo build

# Run the interactive application (make sure the database is running first)
# If using .env, you might need a tool like dotenv-cli:
# dotenv cargo run
cargo run

cargo test

# 1. Ensure a PostgreSQL database is running and accessible via DATABASE_URL.
#    Example using Docker Compose:
#    docker-compose up -d database
#    export DATABASE_URL="postgres://postgres:postgres@localhost:5432/air_quality" # Set for local shell

# 2. Run only the integration tests using the feature flag:
cargo test --features integration-tests

# 3. Stop the Dockerized database if you started it just for the tests:
#    docker-compose down

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by mohammadzainabbas and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.