📺 Netflix Dataset Visualization

Dataset

• File: netflix_titles.csv
• Source: Netflix Movies and TV Shows Dataset (Kaggle)
• Format: CSV (~8,800 rows, 12 columns)
• Columns include:
  • type — Movie or TV Show
  • title — Title of the content
  • director, cast — Metadata about creators/actors
  • country — Country of origin
  • date_added — Date added on Netflix
  • release_year — Release year of the content
  • rating — Content rating (e.g., TV-MA, PG, R)
  • duration — Duration (minutes for Movies, seasons for TV Shows)
  • listed_in — Genre(s)

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What I Did

Performed Exploratory Data Analysis (EDA) using Seaborn and Matplotlib to explore Netflix content.

Key visualizations include:

• Count of Movies vs TV Shows
• Distribution of release years
• Ratings distribution
• Top 10 countries by content
• Release year trends by type
• Violin plots of release year by type
• Scatter plot of release year vs duration (Movies only)
• Average duration by rating (Movies only)
• Heatmap (numerical correlations)
• Pair plots (release year & duration)
• Pie chart of content type distribution

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How to Run

1. Place netflix_titles.csv in the project root.
2. Open Plots.ipynb in Jupyter Notebook or JupyterLab.
3. Run all cells — plots will be generated and saved into the Screenshots/ folder.

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📊 Visualizations

We used Seaborn and Matplotlib to explore the dataset.

📌 Plots

Count of Movies vs TV Shows

Distribution of Release Years

Ratings Distribution

Top 10 Countries by Content

Release Year vs Type

Violin Plot: Release Year by Type

Scatter Plot: Release Year vs Duration (Movies only, numeric duration)

Bar Plot: Average Duration by Rating (Movies only)

Heatmap: Correlation (only numerical fields)

Pie Chart: Distribution of Content Type

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Key Insights

• The Netflix library is dominated by Movies (~70%).
• The most frequent ratings are TV-MA and TV-14, indicating a focus on teens and adults.
• Content production surged significantly after 2015.
• United States and India are the top content-producing countries.
• Most movies have a duration of 80–120 minutes.

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Files in this Repo

• Plots.ipynb — Jupyter Notebook with code, plots, and analysis.
• netflix_titles.csv — Dataset file.
• Screenshots/ — Folder containing all saved plots.
• README.md — Project documentation.

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Next Steps

• Deeper analysis of genre trends across years.
• Explore directors and actors to find the most frequent contributors.
• Compare Netflix’s growth with competitors (Prime, Disney+, etc.) if datasets are available.
• Build an interactive dashboard to explore Netflix’s catalog by type, year, country, and genre.

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Author: Saloni Tiwari
License: Open for educational use