Movie Recommendation System using ml.pptx

ppt on movie recommendation system

1. Movie Recommendation System Presented by : - Dolly(2131750) Bipasha Luthra (2131748 )

2. 1.Problem statement 2.Introduction 3.Objective 4.Project Requirements 5.Data Analysis 6.Design And Diagram 7 .Data preprocessing 8.Approaches used 9.Conclusion 10.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. DATA ANALYSIS Genres distribution in data Number of ratings per user

9. Data Cleaning Data Integration Data Transformation Data Reduction DATA PREPROCESSING

10. To build recommendation system there are many approach that can be used to build good recommendation system Content based recommendation system collaborative filtering . Youtube also used content based recommended system , we also used content based recommendation system in our project and cosine similarity algorithm. Cosine Similarity Cosine similarity is used as a metric in different machine learning algorithms like the KNN for determining the distance between the neighbors, in recommendation systems, it is used to recommend movies with the same similarities and for textual data, it is used to find the similarity of texts in the document . APPROACHES USED

11. • 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

12. Krish naik ( Youtube ) https://www.javatpoint.com https://www.geeksforgeek.org https://www.tutorialspoint.com https://www.kaggle.com REFERENCES