Skip to content

NLP to find the degree of similarity between movies based on their plots available on IMDb and Wikipedia.Used Tokenization, Stemming and created TfidVectorisor. Created clusters by importing Kmeans and calculated similarity distance. Imported Matplotlib, Dendrograms and Linkage to find most similar movies.

Notifications You must be signed in to change notification settings

simranmakandar/Movie-similarities-using-NLP

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 

Repository files navigation

Movie-similarities-using-NLP

NLP to find the degree of similarity between movies based on their plots available on IMDb and Wikipedia.Used Tokenization, Stemming and created TfidVectorisor. Created clusters by importing Kmeans and calculated similarity distance. Imported Matplotlib, Dendrograms and Linkage to find most similar movies.

Natural Language Processing (NLP) is an exciting field of study for data scientists where they develop algorithms that can make sense out of conversational language used by humans. In this Project, you will use NLP to find the degree of similarity between movies based on their plots available on IMDb and Wikipedia.

The dataset contains the titles of the top 100 movies on IMDb as well as each movie's plot summary from both IMDb and Wikipedia.

About

NLP to find the degree of similarity between movies based on their plots available on IMDb and Wikipedia.Used Tokenization, Stemming and created TfidVectorisor. Created clusters by importing Kmeans and calculated similarity distance. Imported Matplotlib, Dendrograms and Linkage to find most similar movies.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published