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The average user age is **34** (range: 7–73).  \n  - Zip code values stood out as an area worth deeper investigation.  \n\n### Movies  \n- **1,680 movies** with titles, release dates, and up to 18 genre tags.  \n- **Key findings:**  \n  - Movies often belong to multiple genres.  \n  - **Drama** and **Comedy** were the most common.  \n\n### Ratings  \n- **100,000 ratings** linked to users and movies, each with a timestamp.  \n- **Key findings:**  \n  - The average movie rating is **3.53** out of 5.  \n\n## Insights \u0026 Discoveries  \n\n- **Genre Trends:**  \n  - Movies are spread across 18 genres.  \n  - About half belong to more than one genre.  \n  - Drama and Comedy dominate in volume.  \n\n- **Genre Preferences:**  \n  - **Film-Noir** had the highest average rating (3.92).  \n  - **Fantasy** scored the lowest (3.21).  \n  - Overall, 72% of genres received ratings above the global average of 3.5.  \n\n- **Movie Favorites:**  \n  - By average rating: *Great Day in Harlem, A* and *Prefontaine*.  \n  - By popularity: *Star Wars* had the highest number of ratings.  \n\n- **Demographics \u0026 Ratings:**  \n  - The dataset is **71% male**.  \n  - Men and women rated movies almost the same (~3.53).  \n  - Non-working users gave the highest ratings.  \n  - Healthcare workers gave the lowest, especially female healthcare workers.  \n\n## Skills Applied  \n\n- Data cleaning and preprocessing with **NumPy** and **Pandas**.  \n- Exploring datasets with descriptive statistics and summaries.  \n- Deriving insights from real-world data.  \n- Understanding relationships between **demographics, genres, and ratings**.  \n\n## Why This Project Matters  \nThis case study shows how raw data can be transformed into meaningful insights.  \nIt highlights:  \n- How to clean and structure real-world datasets.  \n- Ways to uncover hidden patterns in data.  \n- The importance of combining technical skills with curiosity-driven exploration.  \n\nMost importantly, it lays the groundwork for **more advanced machine learning and AI applications**, where understanding the data is always the first step.  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhelenaden%2Fdata-science-fundamentals","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhelenaden%2Fdata-science-fundamentals","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhelenaden%2Fdata-science-fundamentals/lists"}