movie-recommender
world-languages
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| movie-recommender | world-languages | |
|---|---|---|
| 1 | 2 | |
| 6 | 0 | |
| - | - | |
| 6.1 | 0.0 | |
| over 3 years ago | about 3 years ago | |
| Python | Jupyter Notebook | |
| MIT License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
movie-recommender
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4 tips for creating an impressive data science portfolio on GitHub
For example, for my research project on psych-verbs I wrote a paper-like README targeted at academics/fellow linguists, whereas for my movie recommender system I wrote an informal short description and included a screencast, aimed at a general audience.
world-languages
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4 tips for creating an impressive data science portfolio on GitHub
So instead of investing more time on these datasets, pick a new one of your own interest, apply different models and answer questions that you'd find insightful. I personally focused on projects that reflect my interest in Linguistics and NLP – you can explore data related to your experience or the industry you'd like to work in.
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Data analysis of endangered languages with pandas
This exploratory analysis is only a starting point, there are many other questions you can explore from this dataset. For example, find what dialects are critically endangered, what is the geographic distribution of endangered languages, or maybe analyse and visualise the data with other libraries than pandas and matplotlib. Have a look at my Jupyter notebook and play around with the data!
What are some alternatives?
recommendation-algorithm - Collaborative filtering recommendation system. Recommendation algorithm using collaborative filtering. Topics: Ranking algorithm, euclidean distance algorithm, slope one algorithm, filtragem colaborativa.
apartment_recommender_streamlit_app - Streamlit App that recommends apartments in Seattle using the Airbnb kaggle dataset: https://www.kaggle.com/code/rdaldian/airbnb-content-based-recommendation-system/data?select=listings.csv
implicit - Fast Python Collaborative Filtering for Implicit Feedback Datasets
speech-emotion-recognition - A program that uses neural networks to detect emotions from pre-recorded and real-time speech
psych-verbs - Research experiment design and classification of Romanian emotion verbs
letterboxd_recommendations - Scraping publicly-accessible Letterboxd data and creating a movie recommendation model with it that can generate recommendations when provided with a Letterboxd username
cheatsheets - Official Matplotlib cheat sheets
LT-OCF - LT-OCF: Learnable-Time ODE-based Collaborative Filtering, CIKM'21
open_data_covid_analysis - Analysing Covid19 using publicly available datasets
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.