{"id":31780628,"url":"https://github.com/smusab9152/pokemon_data_analysis","last_synced_at":"2026-05-02T17:35:29.875Z","repository":{"id":316402342,"uuid":"1062743619","full_name":"smusab9152/Pokemon_Data_Analysis","owner":"smusab9152","description":"This repo that explores and analyzes a dataset of Pokémon attributes. 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The analysis includes data cleaning, exploratory data analysis (EDA), and visualizations to derive insights about Pokémon characteristics across generations and types.\n\n## Overview\nThis project provides a comprehensive analysis of Pokémon data, focusing on:\n- Statistical comparisons across generations\n- Type-based performance analysis\n- Legendary vs. non-Legendary Pokémon statistics\n- Distribution and frequency of types and type combinations\n- Correlations among key attributes\nAll analysis is performed in a Jupyter Notebook using Python libraries such as pandas, numpy, and matplotlib.\n\n## Analysis Questions\nThe notebook addresses the following questions:\n- **General Analysis**\n  - How do the average stats (HP, Attack, Defense, Sp. Atk, Sp. Def, Speed) compare across generations?\n  - Which Pokémon type has the highest and lowest average Attack stat?\n  - Is there a significant difference in total stats between Legendary and non-Legendary Pokémon?\n  - What is the distribution of Pokémon types and combinations?\n- **Comparative and Relationship Analysis**\n  - Is there a correlation between Speed and Attack?\n  - How do stats compare between dual-type and single-type Pokémon?\n  - Which Pokémon has the highest stat in each category?\n- **Specific Analysis**\n  - How many Pokémon are present in each generation?\n  - What is the most common type combination?\n  - How do average stats vary among Pokémon types?\n\n## Dataset\nThe dataset (`pokemon_data.csv`) contains 800 Pokémon and the following attributes:\n- Number, Name, Type 1, Type 2\n- HP, Attack, Defense, Sp. Atk, Sp. Def, Speed\n- Generation, Legendary status\n\n## Project Structure\n```\nPokemon_Data_Analysis/\n│\n├── Pokemon_Data_Analysis.ipynb  # Main analysis notebook\n├── pokemon_data.csv             # Dataset file\n└── README.md                    # Project documentation\n```\n\n## Getting Started\n\n1. **Clone the repository**\n   ```bash\n   git clone https://github.com/smusab9152/Pokemon_Data_Analysis.git\n   ```\n\n2. **Install dependencies**\n   - Python 3.x\n   - Jupyter Notebook\n   - pandas, numpy, matplotlib\n\n   Install with pip:\n   ```bash\n   pip install pandas numpy matplotlib\n   ```\n\n3. **Open the notebook**\n   ```bash\n   jupyter notebook Pokemon_Data_Analysis.ipynb\n   ```\n\n## Key Insights\n\nSome of the results from the analysis include:\n- **Generational Stats:** Average stats vary across generations, with certain generations showing higher HP or Attack.\n- **Type Analysis:** Dragon-type Pokémon have the highest average Attack; Bug-type Pokémon have the lowest.\n- **Legendary Comparison:** Legendary Pokémon have significantly higher stats compared to non-Legendaries.\n- **Type Frequency:** Water and Normal types are the most common; Flying and Fairy are among the least.\n- **Stat Leaders:** Pokémon like Blissey, Mewtwo (Mega forms), and Deoxys hold records for highest individual stats.\n\n\n## License\n\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmusab9152%2Fpokemon_data_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsmusab9152%2Fpokemon_data_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsmusab9152%2Fpokemon_data_analysis/lists"}