🍿 Netflix Data Analysis

A chill project that dives into Netflix’s content universe using Python — cleaning, analyzing, and visualizing what’s up with movies & TV shows in the dataset.

🚀 What’s the Project About?

This is a dope data analysis project that explores the Netflix dataset (from Kaggle) using Python and popular libs like Pandas, NumPy, Matplotlib & Seaborn. You'll find trends, insights, and cool charts that break down what Netflix is offering across the world. :contentReference[oaicite:0]{index=0}

🧠 Why It’s Cool

• 🧹 Cleaned the messy stuff — handled missing values & fixed formats
• 📊 Context on content — compared movies vs TV shows
• 🌍 Geography vibes — saw which countries make the most content
• 📅 Timeline insights — tracked releases over the years
• 🎬 What’s hot — popular genres, durations, and more
• 📈 Visuals that actually make sense

🧰 Tools & Tech Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

📁 What’s in the Repo

📦 netflix-data-analysis
┣ 📂 images/ #sample charts & visuals
┣ 📄 analysis.ipynb # the main notebook with code + plots
┣ 📄 netflix_titles.csv # raw Netflix dataset (Kaggle)
┣ 📄 README.md #you’re reading it rn 😊

📌 How to Run It Locally

1. Clone the repo

   git clone https://github.com/1PoPTRoN/netflix-data-analysis.git
   cd netflix-data-analysis

2. Open analysis.ipynb in Jupyter Notebook or **VS Code

3. Make sure you have packages:

   pip install pandas numpy matplotlib seaborn notebook

4. Run cells and watch the magic 🪄

📊 Sample Output

Check the images/ folder for visual snapshots of charts like genre trends, content type distribution, and release patterns.

📜 Dataset Source

Netflix dataset comes from Kaggle (Netflix Movies and TV Shows) — grab it if you wanna play with your own copy. GitHub

👉 https://www.kaggle.com/datasets/shivamb/netflix-shows

💡 What I Learned

This project helped boost my data analysis flow — from cleaning messy real-world data to slicing insights and plotting graphs that actually tell stories.

🤝 Contributing

Feel free to open issues or PRs if you want to add new insights, clean more data, or make it even more rad or to use/modify this — go wild 🚀

Made with 🧠 & ☕