📺 Netflix Movie Duration Analysis

Investigating the Trend of Declining Movie Lengths

🎬 Project Overview

In this data analysis project, we dive into the fascinating world of Netflix's content library to investigate a compelling hypothesis: Are movie durations on the platform getting shorter over time? This question not only touches on evolving viewer preferences but also reflects broader trends in the entertainment industry.

As a graduate student in data science, I've approached this analysis with rigorous methodology and a keen eye for insights that go beyond surface-level observations. By leveraging Python and key data analysis libraries, we'll uncover the truth behind this trend and explore potential contributing factors.

📊 Dataset

Our analysis is based on the netflix_data.csv file, which contains comprehensive information about Netflix's content library. Here's a brief overview of the key columns in our dataset:

• show_id: Unique identifier for each show/movie
• type: Content type (Movie or TV Show)
• title: Title of the show/movie
• director: Director(s) of the show/movie
• cast: Main cast of the show/movie
• country: Country(ies) of production
• date_added: Date when added to Netflix
• release_year: Original release year
• rating: Content rating
• duration: Length of the content (minutes for movies, seasons for TV shows)
• listed_in: Genre(s) of the show/movie
• description: Brief description of the show/movie

🛠️ Technologies Used

• Python 3.7+: Our primary programming language
• Pandas: For data manipulation and analysis
• Matplotlib: For creating static, animated, and interactive visualizations
• Jupyter Notebook: For interactive development and presentation of our analysis

🚀 Getting Started

To replicate this analysis on your local machine:

# Clone this repository
git clone https://github.com/yourusername/netflix-duration-analysis.git

# Navigate to the project directory
cd netflix-duration-analysis

# Install required packages
pip install -r requirements.txt

# Launch Jupyter Notebook
jupyter notebook Netflix_Movie_Duration_Analysis.ipynb

📈 Key Findings

(Note: Replace this section with your actual findings once the analysis is complete)

My analysis revealed several interesting trends:

1. Overall trend in movie durations from 1997 to present
2. Variations in movie length across different genres
3. Correlation between release year and movie duration
4. Impact of content rating on movie length

🤔 Future Work

This project opens up several avenues for further exploration:

1. Investigate the relationship between movie duration and viewer ratings/popularity
2. Analyze trends in TV show episode lengths and season counts
3. Explore regional differences in content duration preferences
4. Conduct a comparative analysis with other streaming platforms

🤝 Contributing

As this project is part of my graduate studies, I welcome feedback, suggestions, and discussions. If you have ideas for improving the analysis or spot any issues, please open an issue or submit a pull request.

🙏 Acknowledgments

• Netflix for providing a rich dataset for analysis
• My academic advisors for their guidance on data analysis methodologies
• The open-source community for the fantastic tools that made this analysis possible

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Developed with 🍿 and 📊 by [Bringesh Chowdavarapu]

"Data is the new popcorn – let's see what it reveals about our binge-watching habits!"