📺 Netflix Binge Pattern Analyzer

Analyze trends, genres, durations, and viewing patterns on Netflix using Python and Pandas

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📌 Project Overview

This project dives into a dataset of Netflix titles to uncover binge patterns, genre trends, content duration analysis, and more. The goal is to explore what makes Netflix content binge-worthy and visualize key insights that could help Netflix or any streaming platform better understand their content library.

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🧠 Problem Statement

Netflix has thousands of titles. Understanding what genres dominate, how long content typically is, and how user engagement might differ between movies and TV shows can help drive smarter content decisions.
The project answers:

• 🎬Which genres are most commonly produced for movies vs. TV shows?
• 🌍 Which countries contribute most to Netflix's binge-worthy content?

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📁 Dataset

• 📊 Netflix Movies and TV Shows Dataset
• Shape: 8807 rows × 12 columns

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Key Columns Used:

• type – Movie or TV Show
• title – Name of the content
• country – Origin country
• date_added – Date added to Netflix
• release_year – Year of release
• duration – Length in minutes or number of seasons
• listed_in – Genres
• rating – Age rating (e.g., PG, TV-MA)

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📊 Exploratory Data Analysis (EDA)

• Cleaned null values in key columns
• Converted date_added to datetime format
• Split duration into numeric and unit

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✅ Key Insights:

• 📈 Most content on Netflix is Movies :
  • About 69.6% are Movies and the rest 30.31% are TV Shows , indicating that more than half of the netflix distribution are movies.
• 🌍 US dominates in content production :
  • USA tops the chart with 2809 total production followed by India with 972 total production.
• 🔢 Number of Seasons Pattern
  • Season 1 was released for 1791 content.
• ➕ Content trend
  • Year 2019 saw the highest number of content being added to the platform and

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