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76 lines
3.2 KiB
Markdown
76 lines
3.2 KiB
Markdown
# Mobile Devices Database
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[Русский](README.md) · **English**
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[](https://github.com/EDeev/mobiles_dataset/actions/workflows/ci.yml)
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[](https://github.com/EDeev/mobiles_dataset/releases)
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Database design coursework: the 2025 mobile devices dataset split into a normalized PostgreSQL database,
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a PyQt6 desktop app to work with it, and a measurement of index gains with `EXPLAIN ANALYZE`.
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**Status:** coursework ("Database Design and Administration", Moscow Polytechnic University, 2025), completed
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**Stack:** Python 3.11 · PostgreSQL 15 · psycopg2 · PyQt6 · pandas · Docker
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## Features
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- 3NF schema of five tables (companies, processors, models, regions, prices) with foreign keys and
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cascading actions
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- Import of the [Kaggle dataset](https://www.kaggle.com/datasets/abdulmalik1518/mobiles-dataset-2025)
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(930 rows; 914 models and 4569 prices after cleaning) split into reference tables
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- App: adding companies, models with create, edit and delete, prices in five regions (Pakistan, India,
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China, UAE, USA) with currency symbols, search by name, company and RAM, a price statistics tab
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- Index experiment: `EXPLAIN ANALYZE` queries before and after, plans saved in `sql/explain_results/`
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| Metric | No indexes | With indexes | Difference |
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|---|---|---|---|
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| Search | 0.234 ms | 0.089 ms | 62% faster |
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| Four-table JOIN | 18.6 ms | 0.95 ms | 95% faster |
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| Planner cost | 44.76 | 12.45 | 72% lower |
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| Rows scanned | 914 | 18 | 98% fewer |
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Measured on this dataset (hundreds of rows), so absolute times are fractions of a millisecond.
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## Quick start
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```bash
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git clone https://github.com/EDeev/mobiles_dataset.git && cd mobiles_dataset
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docker compose up -d # PostgreSQL 15 with the schema from sql/create_schema.sql
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pip install -r requirements.txt
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python scripts/import_data.py # load the dataset
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python main.py # the app
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```
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A ready-made Windows build (`.exe`) is in the [releases](https://github.com/EDeev/mobiles_dataset/releases).
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## Without Docker
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1. Run `sql/create_schema.sql` in psql or pgAdmin as `postgres` — the script creates the
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`mobile_devices_db` database itself.
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2. Set the connection with `DB_HOST`, `DB_PORT`, `DB_NAME`, `DB_USER`, `DB_PASSWORD`
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(defaults: `localhost:5432`, `mobile_devices_db`, `admin` / `password`).
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3. `python scripts/import_data.py`, then `python main.py`.
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## Screenshots
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| Companies | Analytics |
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|---|---|
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## License
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Coursework (Database Design and Administration, Moscow Polytechnic University, 2025). The code is open
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for study; there is no separate license. The dataset comes from Kaggle under its author's terms.
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## Author
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**Egor Deev** — [GitHub](https://github.com/EDeev) · [Telegram](https://t.me/DeevEgor) · [egor@deev.space](mailto:egor@deev.space)
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---
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<div align="center">
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<sub>⭐ If you find this project useful, give it a star on GitHub!</sub>
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<p><sub>Made with ❤️ — <a href="https://deev.space">deev.space</a></sub></p>
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</div>
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