Python-EDA--Netflix-User-Dataset

Netflix User Database Analysis 📊

Project Overview

This project performs Exploratory Data Analysis (EDA) on a Netflix User Database using Python. The analysis focuses on understanding user demographics, subscription patterns, device usage, countries, and monthly revenue.

The project is implemented in a Jupyter Notebook using Pandas, NumPy, Matplotlib, and Seaborn.

Dataset

Dataset: Netflix Userbase Dataset.xlsm

The notebook analyzes a dataset containing 2,500 rows and 10 columns.

Key fields used in the analysis include:

• User ID
• Age
• Gender
• Country
• Subscription Type
• Monthly Revenue
• Device
• Plan Duration

Place Netflix Userbase Dataset.xlsm in the same directory as the notebook before running the project.

Objectives

• Understand the overall Netflix user base
• Analyze user demographics
• Calculate important business KPIs
• Compare subscription types
• Analyze monthly revenue
• Study country-wise users and revenue
• Understand device usage
• Explore revenue by gender and age
• Visualize subscription patterns

Key KPIs

The notebook calculates the following KPIs:

• Total Users
• Total Monthly Revenue
• Average Monthly Revenue
• Total Countries
• Number of Device Types
• Average/Most Common Plan Duration
• Male Users
• Female Users

Visualizations

The project includes visualizations such as:

1. Subscription Type Distribution
2. Revenue by Subscription Type
3. Gender Distribution
4. Age Distribution
5. Monthly Revenue Distribution
6. Revenue by Country
7. Users by Country
8. Device Usage
9. Revenue by Device
10. Revenue vs Age
11. Revenue by Gender
12. Country-wise Subscription Type
13. Gender vs Subscription Type

📸 Project Visualizations

Gender Distribution

Age Distribution

Monthly Revenue Distribution

Revenue by Country

Revenue by Device

Subscription Type by Country

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook
• Excel (.xlsm) dataset

Project Structure

Netflix-User-Database-Analysis/
│
├── Netflix_User_Database_Analysis.ipynb
├── Netflix Userbase Dataset.xlsm
├── README.md
├── requirements.txt
└── images/
    ├── gender_distribution.png
    ├── age_distribution.png
    ├── monthly_revenue_distribution.png
    ├── revenue_by_country.png
    ├── revenue_by_device.png
    └── subscription_by_country.png

Installation

1. Clone the repository

git clone https://github.com/Rahulnaik1817/Python-EDA--Netflix-User-Dataset.git
cd netflix-user-database-analysis

2. Create a virtual environment (recommended)

python -m venv venv

Activate it on Windows:

venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Launch Jupyter Notebook

jupyter notebook

Open the project notebook and run the cells.

How to Run

1. Download or clone this repository.
2. Keep Netflix Userbase Dataset.xlsm in the project folder.
3. Install the dependencies from requirements.txt.
4. Open the .ipynb file in Jupyter Notebook or JupyterLab.
5. Run the notebook cells from top to bottom.

Skills Demonstrated

• Data loading and inspection
• Data cleaning validation
• Descriptive statistics
• KPI calculation
• GroupBy analysis
• Aggregation
• Categorical analysis
• Data visualization
• Business-oriented data interpretation
• Python-based Exploratory Data Analysis

Project Outcome

This project demonstrates how Python can be used to transform a raw user dataset into meaningful business insights through KPI analysis, aggregation, and visualization.

Author

Rahul Naik

Aspiring Data Analyst | Python | SQL | Power BI | Data Visualization

License

This project is intended for educational and portfolio purposes.