Strengths3
- End-to-end pipeline from raw data to deployed UI is present.
- Clear instructions for local execution and dependency management.
- Notebook demonstrates data preprocessing, feature engineering, and model persistence.
ML Application · Jupyter Notebook
Movie-Recommender
What it is
This repository provides a Streamlit-based movie recommender that loads precomputed TF-IDF similarity matrices and fetches movie posters from TMDB, accompanied by a notebook that demonstrates the data preparation and model generation steps.
Merit describes the code at this commit. Later pushes count once the owner rescans. How merit is scored
Where it counts
- Machine Learning90%
- Data Science40%
- NLP & LLMs10%
How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.
What the judge found
Read from the project's code at its scored commit.
Limitations
The owner keeps this project's limitations private.
Around it on the Machine Learning board
Whole board- 97, shared rankVISTAArchitecture Modification / Research · Jupyter Notebook48.5ImplementationSuceveanu Mircea Dan SanduMachine Learning
- 99, shared rankautomatos-voiceML Application · Python48.2ImplementationAutomatos AIMachine Learning
- 99, shared rankMovie-RecommenderML Application · Jupyter Notebook48.2Implementationkamran akmalMachine Learning
- 101Plant-Disease-classifierInference System · Jupyter Notebook48.0Implementationkamran akmalMachine Learning
- 102Safe-HomeML Application · HTML47.5ImplementationIrine MiltonMachine Learning
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