NETFLIX-MOVIES-AND-TV-SHOWS-CLUSTERING

1) Problem Statement

This dataset consists of tv shows and movies available on Netflix as of 2019. The dataset is collected from Flixable which is a third-party Netflix search engine.

In 2018, they released an interesting report which shows that the number of TV shows on Netflix has nearly tripled since 2010. The streaming service’s number of movies has decreased by more than 2,000 titles since 2010, while its number of TV shows has nearly tripled. It will be interesting to explore what all other insights can be obtained from the same dataset.

2) 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

3) Attribute Information

• show_id : Unique ID for every Movie / Tv 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 Releaseyear of the movie / show
• rating : TV Rating of the movie / show
• duration : Total Duration - in minutes or number of seasons
• listed_in : Genere
• description: The Summary description

4. Data Visualization

1) Analyze the type of content available on Netflix

• It is evident that there are more movies on Netflix than TV shows.

• Netflix has 5377 movies, which is more than double the quantity of TV shows.

2) Countplot for movie and Tv show ratings

• The 'TV-MA' rating is used in the majority of the film. The TV Parental Guidelines provide a "TV-MA" classification to a television programme that is intended solely for mature audiences.

• The second largest is 'TV-14,' which stands for content that may be inappropriate for minors under the age of 14.

• The third most common is the extremely popular 'R' rating. The Motion Picture Association of America defines an R-rated film as one that contains material that may be inappropriate for children under the age of 17; the MPAA states that "Under 17 requires accompanying parent or adult guardian."

3) Growth in content over the years

• The number of movies on Netflix is growing significantly faster than the number of TV shows.

• In both 2018 and 2019, approximately 1200 new movies were added.

• We saw a huge increase in the number of movies and television episodes after 2014.

• Because of covid-19, there is a significant drop in the number of movies and television episodes produced after 2019.

• It appears that Netflix has focused more attention on increasing Movie content that TV Shows. Movies have increased much more dramatically than TV shows.

4) Netflix Content for different age groups in top 15 countries

• It is also interesting to see parallels between culturally comparable nations - the US and UK are closely aligned with their Netflix target ages, but radically different from, example, India or Japan!

• Also, Mexico and Spain have similar content on Netflix for different age group

5. Conclusion:

1)Data set contains 7787 rows and 12 columns in that cast and director features contains large number of missing values so we can drop it and we have 10 features for the further implementation

2)We have two types of content TV shows and Movies (30.86% contains TV shows and 69.14% contains Movies)

3)By analysing the content added over years we get to know that in recent years netflix is focusing movies than TV shows (movies is increased by 80% and TV shows is increased by 73% compare to 2016 data)

4)The most number of the movies and TV shows release in 2017 and 2020 respectively and united nation have the maximum content on netflix

5)On Netflix, Dramas genre contains the maximum content among all of the genres and the most of the content added in december month and less content in february

6)By applying the silhouette score method for n range clusters on dataset we got best score which is 0.348 for 3 clusters it means content explained well on their own clusters, by using elbow method after k = 3 curve gets linear it means k = 3 will be the best cluster

7)Applied different clustering models Kmeans, hierarchical, Agglomerative clustering on data we got the best cluster arrangments

8)By applying different clustering algorithms to our dataset .we get the optimal number of cluster is equal to 3