Movie Recommendation System ppt final.pptx

Ppt on movie recommendations system

1. Movie Recommendation System Presented by : - Dolly(2131750 )

2. 1.Problem statement 2.Introduction 3.Objective 4.Project Requirements 5.Design and Diagram 6.Data set 7.Data Analysis 8.Approaches Used 9.Coding Screenshots 10.Result screenshots 11 .Conclusion 12.References TABLE OF CONTENT

3. Aim: Build a movie recommendation system based on ‘ Kaggle ’ dataset using machine learning. We wish to integrate the aspects of personalization of user with the overall features of movie such as genre, popularity etc. PROBLEM STATEMENT

4. A recommendation system or recommendation engine is a model used for information filtering where it tries to predict the preferences of a user and provide suggests based on these preferences. Movie Recommendation Systems helps us to search our preferred movies among all of these different types of movies and hence reduce the trouble of spending a lot of time searching our favorable movies. Recommendation systems have several benefits, the most important being customer satisfaction and revenue. INTRODUCTION

5. The goal of our project is to develop a movie recommendation system for binge watchers to help and recommend them good quality of movies. The Objectives Are : → Improving the Accuracy of the recommendation system Improve the Quality of the movie Recommendation system → Improving the Scalability. Enhancing the user experience OBJECTIVE

6. Hardware Requirements A PC with Windows/Linux OS Minimum of 8gb RAM 2gb Graphic card Software Requirements Text Editor (VS-code) Streamlit Dataset Jupyter (Editor) Python libraries PROJECT REQUIREMENTS

7. DESIGN AND DIAGRAM

8. The ‘TMDB 5000 Movie Dataset’ is taken into consideration for movie recommendation purposes in this research work. This dataset is available on kaggle.com. The dataset is composed of 2 CSV files - ‘ tmdb_5000_movies.csv’and‘tmdb_5000_credits.csv ’ DATA SET

9. Statistical data about ‘tmdb_5000_movies.csv’ dataset

10. Statistical data about ‘tmdb_5000_credits.csv’ dataset

11. DATA ANALYSIS Genres distribution in data Number of ratings per user

12. APPROACHES USED

13. CODING SCREENSHOTS

14. FINAL RESULT SCREENSHOTS

15. • In this project, to improve the accuracy, quality and scalability of movie recommendation system . • The Proposed system will recommends good movies according to user's choice. • Bring interests and make users happy. CONCLUSION

16. [1] chen , hung- chen , and arbee L. P. Chen. “A music recommendation system based on music data grouping and user interests.” Proceedings of the tenth international conference on information and knowledge management - CIKM01, 2001, doi:10.1145/502585.502625. [2] ahmed , muyeed , et al. “TV series recommendation using fuzzy inference system, k-means clustering and adaptive neuro fuzzy inference system.” 2017, pp. 1512–1519. [3] park, moon- hee , et al. “Location-based recommendation system using bayesian user‟s preference model in mobile devices.” Ubiquitous intelligence and computing lecture notes in computer science, pp. 1130–1139., Doi:10.1007/978-3-540-73549-6_110 REFERENCES