🎬 Movie Recommendation System

This is a Content-Based Movie Recommender System that suggests movies similar to the one you select. It uses Natural Language Processing (NLP) techniques to analyze movie metadata and return recommendations based on cosine similarity.

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🔍 Types of Recommender Systems

This project focuses on the Content-Based recommendation method. Below are the common types of recommender systems:

1. Content-Based Filtering – Recommends items similar to those the user liked in the past.
2. Collaborative Filtering – Recommends items liked by similar users.
3. Hybrid System – Combines both content-based and collaborative filtering.

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🧠 Project Flow

1. Data Collection
   Utilized datasets from TMDB (The Movie Database).

2. Preprocessing
   Cleaned the data and created a combined metadata string for each movie using features like genres, cast, crew, and keywords.

3. Model Building

   • Used CountVectorizer to convert text data into a 5000-dimensional numerical vector.
   • Calculated cosine similarity between movie vectors to determine closeness.

4. Website Deployment
   Frontend built using Streamlit and deployed via local/server environment using PyCharm.

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🧰 Technologies Used

• Frontend: Streamlit
• Backend: Python (Jupyter Notebook / PyCharm)
• Libraries:
  • Pandas
  • Scikit-learn
  • Pickle
  • Requests (for fetching movie posters from TMDB API)
• Modeling:
  • CountVectorizer (from sklearn)
  • Cosine Similarity

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📁 Files in This Repository

File/Folder Name	Description
App.py	Main Streamlit app code to run the recommender UI
Movie Recommendation System.ipynb	Jupyter notebook for data exploration, preprocessing, and model building
Image_of_the_system.png	Screenshot of the application for preview
README.md	Project documentation

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