Netflix Data Analysis 🎬📊

⭐ Overview

This project presents an exploratory data analysis (EDA) of a Netflix dataset using Python. It covers data cleaning, transformation, visualization, and insight extraction to uncover meaningful patterns in Netflix movie/TV data. The goal is to understand genre trends, popularity distribution, voting patterns, and release year insights.

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🚀 Features

• 📥 Data Loading: Load and inspect structured CSV datasets.

• 🧹 Data Cleaning:

  • Handle missing values and duplicates.
  • Convert date columns to datetime format and extract year.
  • Drop irrelevant columns.

• 📊 Exploratory Data Analysis:

  • Descriptive statistics.
  • Vote categorization into popular, average, below_avg, not_popular.
  • Genre splitting and normalization.

• 📈 Visualizations:

  • Genre frequency distribution.
  • Vote category distribution.
  • Popularity extremes (most/least popular movies).
  • Release year trends.

• 📌 Insights Extraction: Identify top genres, most popular titles, and yearly content trends.

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📁 File Structure

netflix-data-analysis/
│
├── Netflix_Data_Analysis.ipynb   # Main Jupyter notebook
├── netflix_dataset.csv           # Dataset used
├── README.md                     # Project description
└── requirements.txt              # Python dependencies

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📦 Installation

1️⃣ Clone the Repository

git clone https://github.com/vinitjain2005/Netflix-Data-Analysis.git
cd Netflix-Data-Analysis

2️⃣ Install Dependencies

pip install -r requirements.txt

⚠️ Make sure you have Jupyter installed:

pip install notebook

3️⃣ Run the Notebook

jupyter notebook

Open Netflix_Data_Analysis.ipynb and run the cells to reproduce the analysis.

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🛠️ Tech Stack

• Python 3.x
• Jupyter Notebook
• Pandas – Data manipulation and cleaning
• Matplotlib / Seaborn – Data visualization

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📊 Sample Visualizations

• Genre distribution bar charts.
• Vote category counts.
• Most popular vs least popular movies.
• Release year histogram.

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📄 License

This project is open-source under the MIT License.

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🤝 Contributing

Contributions are welcome! Fork the repository, enhance the notebook, or suggest new visualizations via pull requests.