Netflix Data Analysis Cleaning Analysis And Visualization Ms Excel

Abstract

1. Introduction

Welcome to the Netflix Data Analysis project, a comprehensive exploration of Netflix's content catalog. In this project, i meticulously examined Netflix's raw data, employed rigorous analytical process to ensure accuracy and reliability. From data extraction, cleaning to visualization, every step I've performed with precision and attention to detail.

2. Objective

Welcome to the Netflix Data Analysis project, a comprehensive exploration of Netflix's content catalog. In this project, we meticulously examine Netflix's raw data, employing a rigorous analytical process to ensure accuracy and reliability. From data extraction to visualization, every step is performed with precision and attention to detail.

This project demonstrates how modern tools can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/piokerich/Netflix-Data-Analysis-Cleaning-Analysis-and-Visualization-Ms-Excel.git
cd Netflix-Data-Analysis-Cleaning-Analysis-and-Visualization-Ms-Excel

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by piokerich and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.