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D A T E : 2 1 S T S E P T , 2 0 2 3
Movie
Recommendation
System
Recommendation System :
Discussed what kind of data is
needed for the project. What are the
features that are required for the
project
Introduction
Discussed what kind of data is
needed for the project. What are the
features that are required for the
project
Introduction
Collected suitable data which is
required
Data collection
Discussed what kind of data is
needed for the project. What are the
features that are required for the
project
Introduction
Collected suitable data which is
required
Data collection
We have seen the data contains
nulls, So we removed and performed
a basic EDA.
Data Cleaning
Discussed what kind of data is
needed for the project. What are the
features that are required for the
project
Introduction
Collected suitable data which is
required
Data collection
We have seen the data contains
nulls, So we removed and performed
a basic EDA.
Data Cleaning
Worked on building recommendation
system
Recommendation
System
Link to the dataset : click here
We have removed the null values and irrelevant
values.
As well as few columns which are not necessary
for the movie recommendation system
27
17
15
15
15
Steven Spielberg
Clint Eastwood
Martin Scorsese
Renny Harlin
Ridley Scott
Directors Vs Movies
62653
59771
45533
43339
36157
Christopher Nolan
Steven Spielberg
Peter Jackson
Quentin Tarantino
Ridley Scott
Top 5 Directors vs Votes
• Steven Spielberg leads with 27 movies, closely followed by Clint Eastwood, Martin
Scorsese, Renny Harlin, and Ridley Scott, each with 15 films.
• In terms of votes, Christopher Nolan stands out with 62,653 votes, followed by
Steven Spielberg, Peter Jackson, Quentin Tarantino, and Ridley Scott, all contributing
significantly to the vote count.
243
221
118
114
100
Drama
Comedy
Drama Romance
Comedy Romance
Comedy Drama
Genres Vs Movies
122050
119471
61310
59791
57475
Comedy
Drama
Adventure Action Science Fiction
Adventure Fantasy Action
Drama Romance
Top 5 Genres vs Votes
• Drama and Comedy are the most prolific genres, with 243 and 221 movies,
respectively.
• Comedy also tops the vote count with 122,050 votes, followed closely by Drama with
119,471 votes.
Movie Description Based Recommender:
The "Movie Recommendation System" represents the Movie Description Based
Recommender, which uses the descriptions or plots of movies to recommend films that match a
person's preferences. It's like having a magical guide to help you discover the perfect movie for any
mood or occasion.
For more information about the model, link is provided below :
click here
Pros :
Personalized User Experience: :
Recommendation systems provide users
with personalized content suggestions
based on their preferences, improving the
overall user experience.
Increased Engagement: By suggesting
relevant movies or content, these systems
keep users engaged and can lead to longer
viewing or interaction times.
Discovery of New Content : Users can
discover new movies and genres they may
not have considered before, broadening
their horizons.
Cons :
Cold Start Problem : Recommending
content for new users or new items can be
challenging when there is limited data
available.
Privacy Concerns: Recommendation
systems often rely on user data, raising
privacy concerns if this data is mishandled
or misused.
Content Overlooked: Less popular or niche
content may be overlooked if the system
heavily promotes mainstream or trending
items.
Movie Recommendation System_final.pptx
Movie Recommendation System_final.pptx
Movie Recommendation System_final.pptx

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Movie Recommendation System_final.pptx

  • 1. D A T E : 2 1 S T S E P T , 2 0 2 3 Movie Recommendation System
  • 3. Discussed what kind of data is needed for the project. What are the features that are required for the project Introduction
  • 4. Discussed what kind of data is needed for the project. What are the features that are required for the project Introduction Collected suitable data which is required Data collection
  • 5. Discussed what kind of data is needed for the project. What are the features that are required for the project Introduction Collected suitable data which is required Data collection We have seen the data contains nulls, So we removed and performed a basic EDA. Data Cleaning
  • 6. Discussed what kind of data is needed for the project. What are the features that are required for the project Introduction Collected suitable data which is required Data collection We have seen the data contains nulls, So we removed and performed a basic EDA. Data Cleaning Worked on building recommendation system Recommendation System
  • 7.
  • 8.
  • 9. Link to the dataset : click here We have removed the null values and irrelevant values. As well as few columns which are not necessary for the movie recommendation system
  • 10. 27 17 15 15 15 Steven Spielberg Clint Eastwood Martin Scorsese Renny Harlin Ridley Scott Directors Vs Movies 62653 59771 45533 43339 36157 Christopher Nolan Steven Spielberg Peter Jackson Quentin Tarantino Ridley Scott Top 5 Directors vs Votes • Steven Spielberg leads with 27 movies, closely followed by Clint Eastwood, Martin Scorsese, Renny Harlin, and Ridley Scott, each with 15 films. • In terms of votes, Christopher Nolan stands out with 62,653 votes, followed by Steven Spielberg, Peter Jackson, Quentin Tarantino, and Ridley Scott, all contributing significantly to the vote count.
  • 11. 243 221 118 114 100 Drama Comedy Drama Romance Comedy Romance Comedy Drama Genres Vs Movies 122050 119471 61310 59791 57475 Comedy Drama Adventure Action Science Fiction Adventure Fantasy Action Drama Romance Top 5 Genres vs Votes • Drama and Comedy are the most prolific genres, with 243 and 221 movies, respectively. • Comedy also tops the vote count with 122,050 votes, followed closely by Drama with 119,471 votes.
  • 12. Movie Description Based Recommender: The "Movie Recommendation System" represents the Movie Description Based Recommender, which uses the descriptions or plots of movies to recommend films that match a person's preferences. It's like having a magical guide to help you discover the perfect movie for any mood or occasion. For more information about the model, link is provided below : click here
  • 13. Pros : Personalized User Experience: : Recommendation systems provide users with personalized content suggestions based on their preferences, improving the overall user experience. Increased Engagement: By suggesting relevant movies or content, these systems keep users engaged and can lead to longer viewing or interaction times. Discovery of New Content : Users can discover new movies and genres they may not have considered before, broadening their horizons. Cons : Cold Start Problem : Recommending content for new users or new items can be challenging when there is limited data available. Privacy Concerns: Recommendation systems often rely on user data, raising privacy concerns if this data is mishandled or misused. Content Overlooked: Less popular or niche content may be overlooked if the system heavily promotes mainstream or trending items.