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Movie Recommendation System Using Python, SQL, and Statistics

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Last date : 27-Oct-2026

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Movie Recommendation System Using Python, SQL, and Statistics


Amisha Dixit



Amisha Dixit "Movie Recommendation System Using Python, SQL, and Statistics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Smart Innovations in Computer Science and Applications, March 2026, pp.761-770, URL: https://www.ijtsrd.com/papers/ijtsrd101658.pdf

Recommendation systems have become an essential component in various industries such as e-commerce and OTT-platforms. These systems use various algorithms to recommend the most relevant data to the user. Movie recommendation systems, in particular, suggest movies based on the user's interests, thus saving time and effort for the user in searching through a large list of movies to watch. The aim of this project was to develop a movie recommendation system using cosine similarity algorithm. The system is designed to provide personalized movie recommendations based on the user's movie preferences. The project began with data collection from various sources, including movie reviews, ratings, and user preferences. The collected data was preprocessed and transformed into a structured format suitable for analysis. The development of a cosine similarity algorithm comes next. This algorithm is used to compare two sets of vectors. The technique was used to assess how well the films in the dataset fit the user's preferences. The method for proposing films was built using a web-based interface. The interface allows users to enter their film tastes and receive suggestions based on what they say. The suggestions are presented in descending order of similarity, with the most comparable films at the top. Even films that the user has never heard of could be suggested by the system. Giving users a wide range of recommendations that are tailored to their particular preferences is made possible thanks to this capability. In conclusion, the project's goal of developing a cosine similarity-based movie recommendation system was accomplished. Users could easily access and interact with the system because of its web-based interface, and it was quite accurate at providing individualized recommendations.

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IJTSRD101658
Special Issue | Smart Innovations in Computer Science and Applications, March 2026
761-770
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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