Movie Recommendation System in Python with Source Code

Movie Recommendation System in Python

Overview

The Movie Recommendation System is a content-based filtering application that recommends movies to users based on their input. This system uses Python and JavaScript to deliver accurate and engaging recommendations. Powered by Flask, this web app showcases the integration of modern machine learning algorithms and web technologies.

The project utilizes the Scikit-learn library for similarity computations and employs custom JavaScript logic for handling specific scenarios like invalid user inputs. The heart of the system lies in its use of feature extraction techniques and distance metrics such as cosine similarity, which effectively matches movie preferences.

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Key Features

1. Content-Based Filtering

This recommendation system leverages content-based filtering. Based on user input, it evaluates the characteristics of films and recommends related titles.

2. Cosine Similarity Algorithm

Using cosine similarity, the system measures the angle between two feature vectors in a multidimensional space to determine similarity. This ensures precise recommendations that align closely with user preferences.

3. Robust Error Handling

• If the user inputs an invalid movie title, the system redirects to a JavaScript-powered notfound.html, providing meaningful feedback.
• For partial matches, it suggests similar movie titles from the dataset, ensuring a seamless user experience.

4. Soup Column for Feature Concatenation

The project combines all movie details into a “soup” column for efficient similarity computation. This approach enhances the system’s ability to find and recommend related movies.

How to Set Up and Run the Project

Prerequisites

To run the Movie Recommendation System, ensure Python is installed on your system.To get started, take the actions listed below:

Step 1: Download and Extract

• Extract the provided zip file into a folder on your system.

Step 2: Open Command Prompt

• Use the command prompt to navigate to the project folder.

Step 3: Set Up a Virtual Environment

Run the following commands:

python -m venv venv  
.\venv\scripts\activate  # For Windows  
python -m pip install -r requirements.txt  

Step 4: Launch the Flask App

Use the commands below to start the Flask development server:

set FLASK_APP=app.py  
set FLASK_ENV=development  
flask run  

Once the server is running, open your browser and visit http://127.0.0.1:5000 to access the application.

How the System Works

1. User Input
   The user enters a movie title in the search bar.
2. Similarity Computation
   • The backend Python code processes the input using Scikit-learn’s cosine similarity algorithm.
   • It compares the input movie against the “soup” column in the dataset to find the most similar titles.
3. Recommendations
   • If a match is found, the system generates a list of recommended movies.
   • If the input is invalid, the JavaScript code in notfound.html provides appropriate guidance.

Why Choose This Project?

This project demonstrates the practical application of machine learning and web development skills in solving real-world problems. It is perfect for students and developers looking to:

• Understand recommendation algorithms.
• Learn Flask and Scikit-learn integration.
• Enhance their portfolio with a hands-on, real-world project.