https://github.com/tanay-dwivedi/netflix-dataset-data-analysis
This project involves analyzing a Netflix dataset to derive insights on show ratings, release trends, durations, geographical distribution, and content types, facilitating strategic decision-making and content curation efforts.
https://github.com/tanay-dwivedi/netflix-dataset-data-analysis
dataanalysis netflix python seaborn visualization
Last synced: 8 months ago
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This project involves analyzing a Netflix dataset to derive insights on show ratings, release trends, durations, geographical distribution, and content types, facilitating strategic decision-making and content curation efforts.
- Host: GitHub
- URL: https://github.com/tanay-dwivedi/netflix-dataset-data-analysis
- Owner: Tanay-Dwivedi
- Created: 2024-03-09T18:04:23.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-03-13T18:40:03.000Z (over 1 year ago)
- Last Synced: 2024-12-27T02:44:05.392Z (10 months ago)
- Topics: dataanalysis, netflix, python, seaborn, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 1.21 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Netflix Dataset Data Analysis
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## Problem Statement
The **problem statement** involves **analyzing** a **Netflix dataset** to **extract insights** using **visualizations**. Through **five distinct analyses**, including **rating distribution**, **release trends**, **duration distribution**, **country-wise show distribution**, and **content type distribution**, we aim to understand Netflix's **content landscape** for **strategic decision-making** and **content curation**.
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## Identify the Data
[Dataset](https://github.com/Tanay-Dwivedi/Netflix-Dataset-Data-Analysis/blob/master/netflix.csv)
The dataset comprises information on Netflix shows, including attributes like category, title, director, cast, country, release date, rating, duration, type, and description. Through analysis, we aim to uncover trends and patterns within this dataset to inform decision-making and content strategies.
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## Aim of the analysis
1. **Insight Generation**: Utilize visualization techniques to extract meaningful insights from the Netflix dataset, enabling informed decision-making regarding content curation and strategic planning.
2. **Trend Identification**: Identify trends and patterns related to show ratings, release dates, durations, geographical distribution, and content types to discern overarching trends within Netflix's content library.
3. **Content Strategy Enhancement**: Use the insights gained to refine content strategies, optimize content offerings, and enhance user engagement on the Netflix platform, ultimately improving the overall viewing experience for subscribers.
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