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🎬 Netflix Data Analysis & Visualization 📌 Project Overview
This project presents an end-to-end data analysis and visualization of Netflix movie data using Python. The aim is to extract meaningful business insights by answering key analytical questions and presenting findings through clear, well-structured visualizations.
The project follows a real-world Exploratory Data Analysis (EDA) workflow, making it suitable for data analyst internships and entry-level roles, including Netflix.
🔗 Project Notebook: 👉 Netflix Data Analysis.ipynb
🎯 Objectives of the Project
Perform structured data cleaning and preprocessing
Answer 5 core analytical questions
Create multiple visualizations per question
Identify trends related to popularity, ratings, genres, votes, and release years
Demonstrate data storytelling and analytical thinking
🛠️ Tools & Technologies Used
Python
Pandas – data manipulation & cleaning
Matplotlib & Seaborn – data visualization
Jupyter Notebook (PyCharm)
Git & GitHub
📂 Dataset Description
The dataset contains Netflix movie information with the following columns:
Title – Movie name
Release_Date – Year of release
Genre – Movie genre
Popularity – Popularity score
Vote_Count – Number of user votes
Vote_Average – Average user rating
Each movie may appear multiple times due to multiple genres, allowing genre-wise analysis.
🧹 Data Cleaning & Preparation
The following steps were performed before analysis:
Removed missing and inconsistent values
Standardized column formats
Ensured correct data types
Handled duplicate movie-genre records
Prepared data for visualization-ready analysis
📊 Analytical Questions & Insights
Question 1: What is the distribution of movie popularity on Netflix?
Identified highly popular movies
Visualized popularity distribution to understand content reach
📌 Visualizations used: Bar charts, distributions
📓 View full analysis in notebook
Question 2: Which genres are most common on Netflix?
Action, Adventure, and Drama appear most frequently
Shows Netflix’s focus on high-demand genres
📌 Visualizations used: Count plots, bar charts
Question 3: Which genres receive the highest popularity and votes?
Action and Sci-Fi genres show strong popularity
Higher popularity correlates with higher vote counts
📌 Visualizations used: Bar plots, comparison charts
Question 4: How do ratings (Vote_Average) vary across genres and years?
Most movies fall in the 6–8 rating range
Ratings remain relatively stable over the years
📌 Visualizations used: Box plots, trend charts
Question 5: What is the relationship between popularity, votes, and release year?
Movies with higher popularity tend to receive more votes
Recent years show increased content production
📌 Visualizations used: Scatter plots, year-wise analysis
Beyond the core analytical questions, the following visualizations provide deeper insights into Netflix’s content strategy and audience engagement patterns.
Netflix’s catalog is dominated by movies, highlighting its strong focus on film-based content.
Netflix shows a steady increase in content production, especially after 2015, reflecting platform expansion.
Action, Adventure, and Sci-Fi genres attract higher popularity scores, indicating strong viewer interest.
Movies with higher popularity tend to receive more votes, showing a positive correlation between reach and engagement.
Despite increased content volume, Netflix maintains consistent average ratings over the years.
📈 Key Insights Summary
• Action & Adventure genres dominate Netflix’s popular content
• Movies generate higher user engagement compared to TV Shows
• Higher popularity strongly correlates with increased vote counts
• Netflix has consistently expanded content production over the years
• Average ratings remain stable, reflecting maintained content quality
• Genre-wise analysis highlights clear audience preference patterns
• Data visualization enhances clarity and business decision-making
🚀 Why This Project Stands Out
✔ Industry-style EDA structure ✔ Clear analytical questions ✔ Multiple visualizations per insight ✔ Clean, readable, recruiter-friendly notebook ✔ Demonstrates real-world data analytics workflow
Clone the repository
git clone https://github.com/Nitisha707/Netflix-Data-Analysis-and-Visualization.git
Install required libraries
pip install pandas matplotlib seaborn
Open Netflix Data Analysis.ipynb in Jupyter or PyCharm
Run all cells sequentially
👩💻 Author
Nitisha Sharma Aspiring Data Analyst | Python | Data Visualization