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Focused on content types, top countries, release years, and popular genres.\"\n\n## 🔧 Tools Used\n- Python\n- pandas\n- Google Colab / Jupyter Notebook\n\n## 📁 Dataset\n- [Netflix Titles Dataset](https://www.kaggle.com/datasets/shivamb/netflix-shows)\n\n---\n\n## 📌 Key Questions Answered\n\n### 1. 🎬 What type of content dominates Netflix?\n\u003e ✅ **Movies** are more common (6,131) than TV Shows (2,676)\n\n### 2. 🌍 Which countries produce the most content?\n\u003e 🇺🇸 United States: 2,818 titles  \n\u003e 🇮🇳 India: 972 titles  \n\u003e 🇬🇧 UK: 419 titles  \n\u003e 🇯🇵 Japan: 245 titles\n\n### 3. 📅 In which years were most titles released?\n\u003e Top years: 2018 (1,147), 2017 (1,032), 2019 (1,030), 2020 (953), 2016 (902)\n\n### 4. 🎭 What are the most popular genres?\n\u003e - Dramas, International Movies  \n\u003e - Documentaries  \n\u003e - Stand-Up Comedy\n\n---\n\n## 📈 Future Improvements\n- Add visualizations using Matplotlib/Seaborn\n- Clean unknown country values\n- Create an interactive dashboard with Plotly\n\n---\n\n## 🧠 My Learning\nThis was my first real-world data project. I used pandas to filter, clean, and analyze structured data. It helped me build confidence in Python, data exploration, and communicating results.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsabaasif2501%2Fnetflix-data-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsabaasif2501%2Fnetflix-data-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsabaasif2501%2Fnetflix-data-analysis/lists"}