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GitHub / aliceagrawal / HM-Recommender-System-App
Built a collaborative filtering and content-based recommendation/recommender system specific to H&M using the Surprise library and cosine similarity to generate similarity and distance-based recommendations.
JSON API: https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aliceagrawal%2FHM-Recommender-System-App
Stars: 6
Forks: 4
Open Issues: 0
License: None
Language: Jupyter Notebook
Repo Size: 130 MB
Dependencies:
34
Created: about 2 years ago
Updated: 5 months ago
Last pushed: over 1 year ago
Last synced: 4 months ago
Topics: cosine-similarity, jupyter-notebook, modeling, non-negative-matrix-factorization, python, python-3, python3, rfm-analysis, surprise-python, svd, svdpp
Files
Dependencies
- absl-py ==0.10.0
- astunparse ==1.6.3
- cmdstanpy ==0.9.5
- gast ==0.3.3
- geojson ==2.5.0
- google-auth-oauthlib ==0.4.1
- google-pasta ==0.2.0
- h5py ==2.10.0
- kaggle ==1.5.12
- keras ==2.4.3
- keras-preprocessing ==1.1.2
- markdown ==3.3.1
- mrjob ==0.7.4
- numpy ==1.22.3
- oauthlib ==3.1.0
- opt-einsum ==3.3.0
- packaging ==21.3
- patsy ==0.5.2
- pmdarima ==1.8.5
- python-slugify ==6.1.1
- pyzipcode ==3.0.1
- requests-oauthlib ==1.3.0
- scikit-learn ==1.0.2
- statsmodels ==0.13.2
- tensorboard ==2.3.0
- tensorboard-plugin-wit ==1.7.0
- tensorflow ==2.3.1
- tensorflow-estimator ==2.3.0
- termcolor ==1.1.0
- text-unidecode ==1.3
- werkzeug ==1.0.1
- wrapt ==1.12.1
- yapf ==0.32.0
- yellowbrick ==1.4