https://github.com/2000pawan/content_based_movie_recommendation_system

A Content-Based Movie Recommendation System built using Streamlit, scikit-learn, and OMDb API, which recommends movies based on their descriptions. Posters are fetched live using the OMDb API.
https://github.com/2000pawan/content_based_movie_recommendation_system

ai ml movies nlp omdb-api pandas pillow python recommender-system sklearn streamlit

Last synced: about 1 month ago
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A Content-Based Movie Recommendation System built using Streamlit, scikit-learn, and OMDb API, which recommends movies based on their descriptions. Posters are fetched live using the OMDb API.

• Host: GitHub
• URL: https://github.com/2000pawan/content_based_movie_recommendation_system
• Owner: 2000pawan
• License: mit
• Created: 2025-06-15T21:22:44.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-06-15T22:43:06.000Z (about 1 year ago)
• Last Synced: 2025-06-15T23:14:42.669Z (about 1 year ago)
• Topics: ai, ml, movies, nlp, omdb-api, pandas, pillow, python, recommender-system, sklearn, streamlit
• Language: Python
• Homepage: https://movie-recommender-system-pawan.streamlit.app/
• Size: 679 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# 🎬 Movie Recommendation System

A **Content-Based Movie Recommendation System** built using **Streamlit**, **scikit-learn**, and **OMDb API**, which recommends movies based on their descriptions. Posters are fetched live using the OMDb API.

Web-link:- https://movie-recommender-system-pawan.streamlit.app/

---

## 🚀 Features

- 🔍 Search and select a movie

- 🎯 Content-based recommendations using TF-IDF + Nearest Neighbors

- 🖼 Automatically fetch movie posters and plots via OMDb API

- 🧠 Uses cosine similarity on TF-IDF vectors

- 📦 Clean and responsive UI built with Streamlit

- 🛡 Graceful handling of missing posters using placeholders

---

## 📊 Demo Screenshot

![App Screenshot](img.jpg)

---

## 🧠 How It Works

- The dataset contains movies and their descriptions.

- The `TfidfVectorizer` converts descriptions into numerical vectors.

- The `NearestNeighbors` model finds the most similar descriptions to the selected movie.

- For each recommended title, the OMDb API provides a poster and plot.

- If the poster is not found, a placeholder is shown.

---

## 🛠 Tech Stack

- [Python](https://www.python.org/)

- [Pandas](https://pandas.pydata.org/)

- [scikit-learn](https://scikit-learn.org/)

- [Streamlit](https://streamlit.io/)

- [OMDb API](https://www.omdbapi.com/)

---

## 📁 Project Structure

    ├── app.py # Main Streamlit app

    ├── img.jpg # Banner image for the UI

    ├── movies_content.csv # Movie dataset with titles and descriptions

    ├── requirements.txt # Python dependencies

      └── README.md # Project documentation

---

## 🔧 Installation

1. **Clone the repository**:

       git clone https://github.com/2000pawan/Content_Based_Movie_Recommendation_System.git

       cd Content_Based_Movie_Recommendation_System

2. Install dependencies:

       pip install -r requirements.txt

       Run the app:

       streamlit run app.py

---

## 🔑 OMDb API Key

You must have a valid OMDb API key.

Replace the default key in app.py:

api_key = "your_omdb_api_key_here"

## 📌 Sample Dataset Format

title	description

Inception	A thief who steals corporate secrets...

The Dark Knight	Batman raises the stakes in his war...

Make sure your CSV has a title and description column.

### 📬 Contact

Pawan Yadav

yaduvanshi2000pawan@gmail.com

### 📄 License

This project is licensed under the MIT License.