Cinemate is a Movie Recommendation application with a rich integrated UI which helps you to find best movies of your choice . Developed during my menteeship at Microsoft Engage 2022, it implements the following features :
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Content-Based-Filtering :
In this system, keywords are used to describe the items and a user profile is built to indicate the type of item this user likes. In other words, these algorithms try to recommend items that are similar to those that a user liked in the past, or is examining in the present. It does not rely on a user sign-in mechanism to generate this often temporary profile. In particular, various candidate items are compared with items previously rated by the user and the best-matching items are recommended. This approach has its roots in information retrieval and information filtering research.
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Genre-based filtering :
In this kind of filtering, user can select the genere and then click on the button, recommandation based on the tags will appear.
- Dark theme
- Light theme
- Fully Responsive UI
- Minimalist UI
$ git clone https://github.com/FirdausJawed/engage-project.git
$ git checkout -b Branch_Name
$ git add .
$ git commit -m 'message'
$ git push -u origin Branch_Name
$ git fetch --all --prune
$ git checkout master
$ git reset --hard upstream/master
$ git push origin master
- For more please refer to my Jupyter notebook file for detailed explanation.
- Numpy
- Pandas
- Scikit-learn
- NLTK
- Streamlit
- Bag of words
- Count vectorizer
- Cosine Similarity
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Bags of word model : Bag of words model helps convert the text into numerical representation (numerical feature vectors) such that the same can be used to train models using machine learning algorithms.
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Scikit Learn : Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.The library is focused on modeling data. It is not focused on loading, manipulating and summarizing data.
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CountVectorizer : The CountVectorizer provides a simple way to both tokenize a collection of text documents and build a vocabulary of known words, but also to encode new documents using that vocabulary.
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Cosine Similarity : Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. The cosine of 0° is 1, and it is less than 1 for any angle in the interval (0, π] radians
- Streamlit Docs
- Rapid APIs
- CampusX YouTube numpy playlist
- CampusX YouTube pandas playlist
- Python basics Datacamp
- Stanford course by Andrew ng
Sinemate is released under the MIT License.
Drop by and say hello!
