MOVIE RECOMMENDER SYSTEM

Overview

This project is a movie recommender system built using Streamlit. It suggests movies based on user preferences, aiming to provide personalized movie recommendations. The goal is to create a visually appealing and user-friendly application.

Features

1> Personalized Recommendations: Get movie suggestions based on user inputs

2>Interactive UI: Built with Streamlit for an intuitive and engaging user experience

Learning from the Project

Through developing this movie recommender system, I have gained valuable insights and skills in several areas:

Understanding Recommender Systems:

Learned the fundamentals of how recommender systems work and the different approaches such as collaborative filtering and content-based filtering.

Streamlit for Web Applications:

Gained hands-on experience with Streamlit, a powerful tool for creating interactive web applications in Python.

Data Processing and Analysis:

Improved skills in data cleaning, processing, and analysis to ensure accurate and relevant recommendations.

User Interface Design:

Enhanced my ability to design intuitive and user-friendly interfaces that improve the overall user experience.

            Techniques used  
            1>Stemming
            2>Removing Stop Words
            4>TOKENIZATION 
            3>Bag of Words Technique
            4>Cosine Similarity Score

Stemming: Applied stemming to reduce words to their base or root form, improving the consistency of text data.

Removing Stop Words: Removed common stop words to reduce noise and focus on the significant words in the dataset.

Bag of Words Technique: Utilized the bag of words technique to convert text data into numerical format, enabling the application of machine learning algorithms.

Cosine Similarity Score: Employed cosine similarity to measure the similarity between movies based on their textual features, enhancing the accuracy of recommendations.

Project Management:

Developed better project management skills by organizing code, managing dependencies, and maintaining clear documentation.

1> INFERFACE

2> MOVIES DROP BOX

3> RECOMMNMED 5 SIMILAR MOVIES FOR THE USER

Technologies Used

1> Python

2> Streamlit

3> Pandas

4> Matplotlib

5> Seaborn

6> NLP libraries (such as NLTK )