Netflix-Movies_TV-Shows-Clustering__Unsupervised-ML-

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Project Summary -

The project aims to analyze and cluster a Netflix dataset, consisting of attributes like: title, genre, release year, etc. Initial steps involve preprocessing, handling missing values, and transforming categorical variables. Exploratory Data Analysis (EDA) techniques will provide insights into variable distributions and relationships. Subsequently, clustering algorithms like k-means will group similar shows and movies, determining optimal clusters. The results will be evaluated to understand common characteristics within each group, aiding Netflix in content categorization and recommendation systems. Findings will be summarized concisely, using visualizations to communicate effectively. Recommendations may suggest improvements for user experience and content offerings. Ultimately, the project will enhance understanding of Netflix's content landscape, facilitating informed decision-making for the company. In conclusion, this project aims to analyze a Netflix dataset, perform clustering techniques to group similar shows and movies together, and provide insights and recommendations based on the clustering results. The project will contribute to a better understanding of Netflix's content landscape and aid in decision-making processes for the company.

In this project, you are required to do

1. Exploratory Data Analysis

2. Understanding what type content is available in different countries

3. Is Netflix has increasingly focusing on TV rather than movies in recent years.

4. Clustering similar content by matching text-based features

Attribute Information

1. show_id : Unique ID for every Movie / Tv Show

2. type : Identifier - A Movie or TV Show

3. title : Title of the Movie / Tv Show

4. director : Director of the Movie

5. cast : Actors involved in the movie / show

6. country : Country where the movie / show was produced

7. date_added : Date it was added on Netflix

8. release_year : Actual Releaseyear of the movie / show

9. rating : TV Rating of the movie / show

10. duration : Total Duration - in minutes or number of seasons

11. listed_in : Genere

12. description : The Summary description