Nitesh-Pant / Netflix-UserBase-Data-Analysis

Exploratory Data Analysis on Netflix Userbase Dataset

Introduction

Welcome to my repository showcasing the results of my Exploratory Data Analysis (EDA) on the Netflix userbase. As part of my journey into the world of data science, I took a deep dive into Netflix's user data, and the results are both insightful and eye-opening. 📊💡

Analysis Questions

• General questions
  1. Find number of males and females?
  2. What devices are used the most by users?
  3. Which are the most comman plans subscribed by users?
  4. Find the number of users joined Netflix by year?
  5. What devices are used by which plans?
• Countries related questions
  1. Find number of users by countries?
  2. Find number of males and females by countries?
  3. which devices are used by each countries?
  4. Which plans are most used by each country?
• Revenue related questions
  1. Find the total revenue of 2022?
  2. Find total revenue of 2022 by gender?
  3. Find total revenue of 2022 by each country?
  4. Find total revenue of 2022 by Subscription Type (Plan)?
  5. Find total revenue of 2022 by devices (Laptop, Smartphones, Tablet etc)?
• Age related questions
  1. Find the number of user age wise?
  2. What are the subscription type by age?

Data Sources

The analysis was conducted using Netflix dataset containing relevant information about user interactions, genres, ratings, and more. The dataset was preprocessed to ensure accuracy and usability.

How to Explore

1. Clone this repository to your local machine using git clone.
2. Navigate to the project directory.
3. Open the Jupyter Notebook (netflix-userbase-data-analysis.ipynb) to access the analysis and findings.
4. Feel free to interact with the notebook to explore visualizations, insights, and code details.

Dependencies

The analysis was performed using Python and Jupyter Notebook. The following libraries were used:

• Pandas
• Matplotlib
• WordCloud

Further Discussion

If you're interested in diving deeper into the analysis or have any questions related to data science, EDA, or the insights discovered, feel free to reach out by creating an issue or directly messaging me.

Acknowledgments

I would like to express my gratitude to the open-source community for providing the dataset used in this analysis. The findings provided valuable insights and contribute to understanding user behavior on streaming platforms.

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Feel free to star ⭐️ this repository if you find the analysis interesting or helpful. Your feedback and engagement are greatly appreciated!