# 🎬 Netflix Content Analysis

**Tools:** Python (Pandas, Matplotlib) · SQL  |  **Dataset:** Netflix Movies & TV Shows (Kaggle, 8,800+ titles)

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## 🎯 Objective
Analyse Netflix's global content library to uncover trends in content type, genre popularity, country-wise production, and release patterns — helping understand how Netflix builds its catalog strategy.

## 🛠️ Tools Used
- **Python** – Pandas (data cleaning, transformation), Matplotlib/Seaborn (visualisation)
- **SQL** – Exploratory queries, filtering, aggregation
- **Dataset** – Netflix Movies and TV Shows (Kaggle)

## 🔍 Analysis Performed
- **Content Type Split** — Movies vs TV Shows ratio over the years
- **Top Genres** — Most frequent genres globally and by region
- **Country-wise Output** — Top content-producing countries (USA, India, UK)
- **Release Year Trends** — Content addition spikes (2018–2020 peak)
- **Rating Distribution** — TV-MA vs TV-14 vs PG content breakdown
- **Duration Patterns** — Average movie length and TV show season count

## 💡 Key Findings
- Netflix added the most content between **2018–2020**, slowing post-pandemic
- **Movies** make up ~70% of the catalog; **TV Shows** growing faster in recent years
- **Drama** and **International Movies** are consistently the top two genres
- **United States** leads content production, followed by **India** and the **United Kingdom**

## 📂 Files
```
Netflix-Data-analysis/
├── Netflix/           # Dataset and analysis files
└── README.md
```

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*Part of my Data Analyst portfolio | [View all projects](https://github.com/Sakthi3112-Analyst)*