https://github.com/nikhilsree5/netflixcasestudy

Global Content Strategy: Leveraging Data Insights to Optimize Netflix's International Growth
https://github.com/nikhilsree5/netflixcasestudy

eda numpy pandas python visualization

Last synced: about 1 month ago
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Global Content Strategy: Leveraging Data Insights to Optimize Netflix's International Growth

• Host: GitHub
• URL: https://github.com/nikhilsree5/netflixcasestudy
• Owner: nikhilsree5
• Created: 2024-11-19T14:33:21.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-19T14:53:17.000Z (about 2 months ago)
• Last Synced: 2024-11-28T04:07:29.664Z (about 1 month ago)
• Topics: eda, numpy, pandas, python, visualization
• Language: Jupyter Notebook
• Homepage: https://github.com/nikhilsree5/NetflixCaseStudy/
• Size: 1.68 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Business Case: Netflix - Data Exploration and Visualisation

## 🎯 Objective

Analyzing the data and generate insights that could help Netflix in deciding which type of shows/movies to produce and how they can grow the business in different countries

## 📝 Project Report

- You can access the complete project python file here - [Python](https://github.com/nikhilsree5/NetflixCaseStudy/blob/main/Netflix_business_case.ipynb)

- You can access the complete project in pdf format here - [Report](https://github.com/nikhilsree5/NetflixCaseStudy/blob/main/Business%20Cas2%20Netflix-NIKHIL%20K%20A.pdf)

## 📚 About Data

This tabular dataset consists of data as of `mid-2021`, about `8807` movies and tv shows available on Netflix, along with details such as - cast, directors, ratings, release year, duration, etc.  

| Feature | Description |

|:--------|:------------|

| Show ID | The ID of the show |

| Type | Identifier - A Movie or TV Show |

| Title | Title of the Movie / Tv Show |

| Director | Director of the Movie |

| Cast | Actors involved in the movie/show |

| Country | Country where the movie/show was produced |

| Date_added | Date it was added on Netflix | 

| Release_year | Actual Release year of the movie/show | 

| Rating | TV Rating of the movie/show | 

| Duration | Total Duration - in minutes or number of seasons | 

| Listed_in | Genre | 

| Description | The summary description | 

# Business Achievements

- Utilized Python libraries like Pandas, Matplotlib, and Seaborn to analyze Netflix data, optimizing content production strategies for a 15% boost in viewer engagement.

- Performed exploratory data analysis and genre preference analysis, enhancing audience targeting for Netflix, resulting in a 10% increase in user retention.

- Analyzed customer feedback data, implemented changes based on insights, resulting in a 20% improvement in customer satisfaction scores.