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🤵 En nettside som bruker maskinlæring til å gjette om du er mann eller kvinne basert på hva du skriver 💃

Home Page: https://mannellerkvinne.lblend.moe

License: GNU General Public License v3.0

Shell 2.28% Python 35.03% Dockerfile 0.60% PureBasic 62.09%
gender gender-classification machine-learning naive-bayes-classifier norge norsk norwegian reccurent-neural-network

mann-eller-kvinne's Introduction

Morn 👋

Hi there! I go by the name LBlend on the internet. I'm a student from Norway who is currently an undergraduate at the University of Oslo. I'm pursuing a degree in informatics, also known as computer science, and I'm looking to hone my skills and create cool/interesting projects!

Website (WIP) | LinkedIn | Discord

🇳🇴 Norwegian - Native
🇬🇧 English - Fluent
🇰🇷 Korean - A2/B1
🇪🇸 Spanish - A1/A2

Technologies

Familiar with

Technologies I have used extensively

Have dipped my toes into

Technologies that I have used, but not extensively

Scheme

Learning

Technologies that I am in the process of learning or want to do a project in

Potential future endeavors

Technologies that I want to take a look at in the future

APL

Config

Check out my dotfiles repo

mann-eller-kvinne's People

Contributors

lblend avatar marksverdhei avatar

Stargazers

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mann-eller-kvinne's Issues

Replace all NLTK with scikit-learn

NLTK is an old fashioned, awkward (imo). I think we should opt for a more modern classifier API. scikit learn is more modern and elegant. I think we should use this for the naive bayes (and possibly other) classifiers

Use more semantic variable names and field names

Change names like "clf", "M", and "F" to more semantic and easy-to-understand names. It's not obvious to neither people reading the code nor consumers of the API what the values mean.

If we are going through with this, I want keep this change on hold until we've fully rewritten the backend. This makes it so that the finished rewrite will be a 3.0 release and this change the 4.0 release. This creates a consistent correlation between the version numbers of the frontend and the backend. Though, not a requirement it is good to keep it this way for now.

Validate classifiers

Estimate classifiers performance on development and train set using various metrics.
This can be done e.g. using a jupyter notebook. Results can be presented in readme.
Suggestions about how it should be done or presented are very welcome

Translate README into english

Offer an english version of the README along with the norwegian version in order to make the code more accessible to people.

This should apply to all documents in the repo. This means that at the time of writing this, the contribution guide and the backend README needs to be translated as well.

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