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learning_helpdesk

Helpdesk Modulo for learning proposites

module-1

Ficheros del módulo 1. Ironhack Data Analytics

movie-time

A collaborative recommender system based on Machine Learning using the MovieLens Dataset. Using Scikit-Learn for both Clustering and user rating prediction. The feature extraction and Machine Learning process are written in Pandas and Dask for full scalability. The data is stored on a MongoDB Atlas and is accessible through a bottle based API deployed in Heroku.

movie-time-api

neumorphic-calculator

nightfox.nvim

🦊A highly customizable theme for vim and neovim with support for lsp, treesitter and a variety of plugins.

office-location-finder

Analysis and ranking of the most optimal places to locate a company office using Google Places API to locate desired near places, Kaggle's Crunchbase dataset to consider nearer companies, MongoDB to store the data and Folium for the spatial representation of the results.

openweather-fastapi

pandas-data-cleaning

Cleaning and analysis of the "Global Shark Attack Incidents" dataset using python3 and pandas.

politweets-workshop

prework-datamad-apocalypsis-exercise

prework-datamad-no-solutions

qmk_firmware

Open-source keyboard firmware for Atmel AVR and Arm USB families

readme-template

README.md template for your open-source project

resultados_electorales_espanya

scrapy-sqlitem

Scrapy extension to write items using sqlalchemy models

startup-investments

tech-trends

theprimeagen-algorithms

Implementations from The last Algorithms Course You'll Need https://frontendmasters.com/courses/algorithms/

twitter-analysis-app

This is a standalone and serverless app, that helps you find people around you that migth be interested in your content. It uses NLP to compare your content with your followers' followers and provides a similarity score between 0 and 1. It builds a sqlite database, so you can connect it to your favorite Data Visualization tool.

vim-odoo-snippets

Vim snippets for Odoo

webinar-data-visualization-chartjs

Webinar | Visualización de datos con ChartJS | Materiales