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🎬 Netflix Data Visualization Project

🌟 Project Overview

This project dives into Netflix’s vast dataset to uncover insights about:

• 📊 Which genres dominate viewership
• ⭐ How ratings are distributed across shows and movies

To make the analysis versatile, it’s implemented in both Python and R:

• Python: Handles data extraction, cleaning, and visualization of both genres and ratings.
• R: Focuses on a streamlined visualization of ratings distribution using ggplot2.

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📂 Project Files

• netflix_analysis.py → Extracts, cleans, and visualizes Netflix data (genres + ratings).
• netflix_ratings_distribution.R → R-based script for ratings visualization.
• netflix_data.zip → Compressed raw Netflix dataset.
• Netflix_shows_movies.csv → Cleaned dataset generated for analysis.

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▶️ Execution Flow

1. Run netflix_analysis.py (Python): Extract, clean, and prepare the dataset + visualize genres & ratings.
2. Run netflix_ratings_distribution.R (R): Load the cleaned dataset and generate ratings distribution plots.

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⚙️ Requirements

• Python 3.x → Install pandas, seaborn, matplotlib
• R → Install ggplot2, dplyr

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🐍 Python Script Workflow (netflix_analysis.py)

1. Data Prep → Extracts netflix_data.csv from the ZIP and renames it to Netflix_shows_movies.csv.
2. Data Cleaning → Fills missing values (director, cast, country, rating, date_added).
3. Exploration → Generates descriptive stats to understand the dataset.
4. Visualization → Creates insightful plots (most watched genres + ratings distribution).

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📊 R Script Workflow (netflix_ratings_distribution.R)

1. Load Data → Reads in Netflix_shows_movies.csv.
2. Data Cleaning → Handles missing values in key fields.
3. Visualization → Builds clear, engaging ratings distribution charts with ggplot2.

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📝 Notes

• Keep all files (netflix_analysis.py, netflix_ratings_distribution.R, netflix_data.zip, Netflix_shows_movies.csv) in the same directory before running.
• The Python script generates the cleaned CSV, which is then used as input for the R script.

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✨ This project demonstrates data wrangling, visualization, and cross-language analytics — skills that directly translate into real-world business intelligence and data science roles.

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