🎬 Netflix Data Analysis using Python

A professional Exploratory Data Analysis (EDA) project analyzing the Netflix Movies & TV Shows dataset using Python, Pandas, NumPy, and Matplotlib.

This project demonstrates the complete data analysis workflow—from data cleaning and preprocessing to exploratory analysis, statistical summaries, and visualization.

📌 Project Overview

This project explores more than 8,800 Netflix titles to answer key business and analytical questions, including:

• 🌍 Which countries produce the most Netflix content?
• 🎬 Movies vs TV Shows distribution
• 🎭 Most common content ratings
• 🎥 Directors with the highest number of titles
• ⭐ Actors appearing most frequently
• 📅 Release year trends over time
• 🎯 Most popular genres
• 📈 Statistical summaries using NumPy

📂 Dataset

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

Dataset Features

Column
Show ID
Title
Type (Movie / TV Show)
Director
Cast
Country
Date Added
Release Year
Rating
Duration
Genre
Description

🛠 Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook

📚 Skills Demonstrated

✅ Data Cleaning

• Missing value detection
• Handling null values
• Duplicate checking
• Data type conversion
• Feature engineering

✅ Exploratory Data Analysis (EDA)

• Filtering & Sorting
• value_counts()
• groupby()
• agg()
• pivot_table()

✅ String Operations

• str.contains()
• str.startswith()
• str.endswith()
• str.len()

✅ Date & Time Analysis

• to_datetime()
• Year extraction
• Month extraction
• Weekday analysis
• Date filtering

✅ NumPy Analysis

• Mean
• Median
• Standard Deviation
• Percentiles
• np.where()
• np.unique()

✅ Data Visualization

• Bar Charts
• Line Charts
• Pie Charts
• Histograms
• Grid Styling
• Figure Customization
• Figure Saving

📁 Project Structure

Netflix-data-analysis/
│
├── Netflix data analysis.ipynb
├── netflix_titles.csv
├── requirements.txt
├── README.md
│
└── graphs/
    ├── Releaseyears.png
    ├── movies&TV.png
    ├── content.png
    ├── actors.png
    ├── directors.png
    └── distributions.png

📊 Data Visualizations

Netflix Titles Released Over the Years

Movies vs TV Shows

Content Distribution

🔍 Key Insights

• 📌 Movies significantly outnumber TV Shows.
• 🌍 The United States has the largest Netflix catalog.
• 🇮🇳 India is the second-largest contributor.
• 🎭 TV-MA is the most common content rating.
• 📅 2018 recorded the highest number of releases.
• 🎥 Rajiv Chilaka directed the most titles in the dataset.
• 📈 Netflix experienced rapid content growth between 2016–2020.

▶️ How to Run

Clone the repository

git clone https://github.com/Adeenaeman/Netflix-data-analysis-.git

Install dependencies

pip install -r requirements.txt

Open the notebook

Netflix data analysis.ipynb

Run all cells to reproduce the complete analysis.

📌 Future Improvements

• Interactive dashboards using Plotly
• Power BI dashboard
• SQL-based analysis
• Predictive analytics using Machine Learning

👩‍💻 Author

Adeena Eman

GitHub
https://github.com/Adeenaeman

LinkedIn
https://www.linkedin.com/in/adeena-eman/

If you found this project helpful, consider giving it a ⭐ on GitHub.