Movie Recommendation system is a Content-Based Filtering Movie Recommender. This is built on a [flask app] using Python programming language and JavaScript programming language. Also, using the concept of CB, the project uses two snippets of code. The first one is in Python programming language using the package “scikit-learn”. And the second snippet of code is in the JavaScript programming language which uses no packages and operates based on logic. Here, feature extraction methods and distance metrics are utilized to generate recommendations.
About This Movie Recommendation System In Python
Talking about the project, the project uses content based filtering algorithm where feature extraction method is use for calculating similarity between each item available. Also the project uses cosine similarity algorithm as distance metric. This computes the similarity of items by measuring the cosine of the angle between two vectors projected in a multidimensional vector space. The python code in app.py will generate a list of movie recommendations provided that the user entered a valid movie name. When the movie name matches with a movie name in the dataset, it will generate the recommendations according to the soup column (all details concatenated into one string) of each movie.
Similarly, when the user enter an invalid movie name, the project will show the JavaScript code from notfound.html.
Also, this set of codes will return movie titles that are similar to the input that the user has entered, if applicable. The system will check the data against all existing movie names to find the most similar movie names.
How To Run Movie Recommendation System In Python ?
To run this project, you must have installed Python on your PC. After downloading the project, follow the steps below:
Step1: Extract/Unzip the file
Step2: Go inside the project folder, then open cmd.
Step 3: Activate the environment and install requirements (windows) as shown below:
python -m venv venv .\venv\scripts\activate python -m pip install -r requirements.txt
Step 4: Run flask app as shown below:
set FLASK_APP=app.py
set FLASK_ENV=development
flask run
