Netflix Movie Database Analysis & Visualization

A comprehensive data analysis and visualization project exploring movie trends, genres, ratings, and popularity patterns using a Netflix-like movie database.

📊 Project Overview

This project analyzes a movie database containing nearly 10,000 movies to uncover insights about:

• Genre distribution and popularity
• Movie rating patterns
• Release date trends
• Popularity metrics
• Vote average categorization

🗂️ Dataset

File: mymoviedb.csv

• Size: 9,827 movies
• Original Columns: 9 features
• Key Features:
  • Release Date
  • Title
  • Overview
  • Popularity
  • Vote Count
  • Vote Average
  • Original Language
  • Genre
  • Poster URL

🛠️ Technologies Used

• Python 3.x
• Libraries:
  • pandas - Data manipulation and analysis
  • numpy - Numerical computing
  • matplotlib - Data visualization
  • seaborn - Statistical data visualization

📋 Data Processing Pipeline

1. Data Loading & Initial Exploration

• Load dataset from CSV file
• Examine data structure and types
• Check for duplicates and missing values

2. Data Cleaning & Transformation

• Date Processing: Convert release dates to datetime format and extract year
• Column Removal: Drop unnecessary columns (Overview, Original_Language, Poster_Url)
• Missing Data: Handle null values by dropping incomplete records
• Genre Processing: Split multi-genre entries and explode into separate rows
• Data Type Optimization: Convert Genre to categorical type

3. Feature Engineering

• Vote Average Categorization:
  • Implement quartile-based binning function
  • Categories: 'poor', 'below_avg', 'average', 'popular'
  • Based on statistical distribution (min, 25%, 50%, 75%, max)

📈 Visualizations & Analysis

1. Genre Distribution Analysis

• Visualization: Horizontal count plot of movie genres
• Insights: Shows the most popular genres in the dataset
• Styling: Custom color scheme (#48275f)

2. Vote Average Distribution

• Visualization: Count plot of rating categories
• Categories: Poor, Below Average, Average, Popular
• Styling: Custom color scheme (#4827f5)

3. Popularity Analysis

• Maximum Popularity: Identify the most popular movie(s)
• Minimum Popularity: Identify the least popular movie(s)

4. Release Date Trends

• Visualization: Histogram of movie releases by year
• Analysis: Distribution of movies across different time periods

🚀 Getting Started

Prerequisites

pip install pandas numpy matplotlib seaborn

Running the Analysis

1. Clone this repository
2. Ensure mymoviedb.csv is in the project directory
3. Open and run project.ipynb in Jupyter Notebook/Lab or VS Code

File Structure

netfilx_visualization/
│
├── mymoviedb.csv          # Dataset file
├── project.ipynb          # Main analysis notebook
└── README.md             # Project documentation

🔍 Key Findings

Data Quality

• Original Dataset: 9,827 movies with 9 features
• No Duplicates: Clean dataset with unique entries
• Data Completeness: Handled missing values appropriately

Feature Engineering

• Successfully categorized vote averages into meaningful segments
• Exploded multi-genre entries for granular analysis
• Optimized data types for better performance

Visualization Insights

• Genre distribution reveals content preferences
• Rating categorization shows quality distribution
• Release date analysis shows temporal patterns
• Popularity metrics identify standout content

📊 Data Schema (Post-Processing)

Column	Type	Description
Release_Date	int64	Year of movie release
Title	object	Movie title
Popularity	float64	Popularity score
Vote_Count	int64	Number of votes received
Vote_Average	category	Categorized rating (poor/below_avg/average/popular)
Genre	category	Individual movie genre

🎯 Future Enhancements

• Add interactive visualizations with Plotly
• Implement sentiment analysis on movie overviews
• Create a dashboard with filters and drill-down capabilities
• Add statistical correlation analysis
• Implement machine learning models for recommendation
• Include temporal trend analysis with time series
• Add comparison with industry benchmarks

📝 Notes

• The analysis focuses on data exploration and visualization
• Genre data was exploded to enable individual genre analysis
• Vote averages were categorized using quartile-based binning
• Visualizations use a consistent color scheme for professional appearance