📺 Netflix Content Analysis Using Python

A comprehensive Exploratory Data Analysis (EDA) project on the Netflix Movies & TV Shows Dataset (8,800+ titles) using Python. This project focuses on data cleaning, exploration, and visualization to uncover meaningful insights into Netflix's content library.

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🎯 Project Objective

Analyze the Netflix dataset to identify:

• 🎬 Movies vs TV Shows distribution
• 🌍 Top content-producing countries
• 🎭 Most popular genres
• ⭐ Content rating trends
• 📅 Release year patterns
• ⏱️ Movie duration analysis
• 🎥 Top directors and actors
• 📈 Business insights through data visualization

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🛠️ Tools & Technologies

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

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📂 Dataset

• Dataset: Netflix Movies & TV Shows
• Records: 8,800+ Titles
• Source: Kaggle

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🔍 Project Workflow

📌 Data Collection

• Loaded Netflix dataset using Pandas
• Explored dataset structure

📌 Data Cleaning

• Removed duplicate records
• Checked missing values
• Converted data types
• Processed date columns
• Created new features for analysis

📌 Exploratory Data Analysis (EDA)

• Movies vs TV Shows
• Country-wise Analysis
• Genre Analysis
• Rating Distribution
• Release Year Trend
• Duration Analysis
• Top Directors
• Top Actors

📌 Data Visualization

Created multiple visualizations using:

• 📊 Bar Charts
• 🥧 Pie Charts
• 📈 Line Charts
• 📉 Count Plots
• 🔥 Heatmaps
• 📦 Box Plots

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📊 Key Insights

• 🎬 Movies dominate the Netflix catalog (6,131) compared to TV Shows (2,676).
• 🌍 The United States contributes the highest number of Netflix titles.
• 🎭 Drama and International Movies are among the most popular genres.
• 📈 Netflix content additions increased significantly after 2015.
• ⭐ TV-MA is the most common content rating.
• 🎥 Most Netflix movies have a duration between 80–120 minutes.
• 🌐 Netflix has expanded its content library globally over the years.

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📈 Project Visualizations

🎬 Movies vs TV Shows

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🌍 Top 10 Countries

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🎭 Top 10 Genres

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🎬 Top 10 Directors

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⭐ Top 10 Actors

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📁 Project Structure

NETFLIX-data-analysis/
│
├── NETFLIX (2).html
├── netflix_titles.csv
├── README.md
├── movies_vs_tvshows (1).png
├── top_10_countries (1).png
├── top_10_genres (1).png
├── top_10_directors (1).png
└── top_10_actors (1).png

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🚀 How to Run

1️⃣ Clone the Repository

git clone https://github.com/singhdivanshu455-star/NETFLIX-data-analysis.git

2️⃣ Install Required Libraries

pip install pandas numpy matplotlib seaborn

3️⃣ Launch Jupyter Notebook

jupyter notebook

4️⃣ Open the Notebook

Open the .html/.ipynb file in the repo and run all cells to reproduce the complete analysis.

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💡 Business Recommendations

• Expand content production in emerging markets.
• Invest more in high-performing genres.
• Continue strengthening international content.
• Increase family-friendly content offerings.
• Use audience trends to optimize future content strategy.

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🚀 Future Improvements

• 📊 Interactive Power BI Dashboard
• 🌐 Streamlit Web Application
• 🤖 Movie Recommendation System
• 📈 Tableau Dashboard
• 📡 Real-time Content Analysis

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👨‍💻 Author

Divanshu Singh

Aspiring Data Analyst

Skills: Python • SQL • Excel • Power BI • Pandas • NumPy • Matplotlib • Seaborn

🔗 GitHub: https://github.com/singhdivanshu455-star

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