/my-workcation-path

"GitHub project for finding campgrounds and remote workspaces. Discover, organize, and enhance your mobile experience!"

Primary LanguageJupyter NotebookGNU General Public License v3.0GPL-3.0


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🏕️ MY WORKCATION PATH 💻

In a world where vacations and work sometimes merge.
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Table of Contents
  1. About The Project
  2. Getting Started
  3. Roadmap
  4. EDA: A Path model to Encoding
  5. Model
  6. Recomender
  7. License
  8. Contact
  9. Acknowledgments

About The Project


During the summer of 2023 I left the screens and meetings to embark on a different professional experience: managing the opening of one of the glampings of an important company in Portugal. An incredible experience! People from all places, backgrounds and ages came to "my space". For everyone, I had some advice to give them to help them in their stay with us, families, couples, groups of friends... However, there was one question that always had a "half-answer":

  • I need to work. Where is there a good internet connection here?
  • There isn't, try a café into the village.

Business Idea:

In our ever more interconnected world, the lines between work and leisure are becoming increasingly blurred, particularly noticeable in remote work settings. Occasionally, we face challenges in locating suitable workspaces while travelling to different places. To streamline travel arrangements for working individuals, the concept of this app emerged—an application designed to suggest accommodations in natural settings, considering the proximity of coworking spaces.

Project Objective:

Our overarching goal is to create an interactive app where we can select certain parameters of the travel experience we want: proximity to an urban centre, proximity to a beach, rating, or some remote place. Depending on these parameters and the maximum distance we would like to travel to find a workspace, the app will give us different options in the form of related campgrounds and co-workings.

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Setup

This section should list any major frameworks/libraries used to bootstrap this project.

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Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

  pip install -U googlemaps
  pip install geopandas
  pip install geopy

or:

  conda install -U googlemaps
  conda install -U geopandas
  conda install -U googlemaps

Installation

  1. Clone the repo

     git clone https://github.com/OSCGRA/my-jobcation-path.git

  2. API Configuration:

    Create a .py file like this:

    api_key

    Into these folders:

    -  my-jobcation-path/01_data_mining_phase/scrapper_app/
-  my-jobcation-path/02_data_cleaning_phase/01_Preprocessing&Clean/

    Open it and write:

    api_key = GOOGLE_MAPS_API_KEY

    Example to call it:

     from api_key import GOOGLE_MAPS_API_KEY

     API_KEY = GOOGLE_MAPS_API_KEY

     gmaps = googlemaps.Client(key=API_KEY)

    (IMPORTANT: This step is optional, you can use the .csv provided. You will need it if you want to restart the data provided, retrieving new data from Google Maps.)

  3. Install packages:

    You can open each notebook in each folder and look for other installations and repeat point 1 with them, or do it directly in the corresponding notebook.

  4. Follow the Roadmap section in this README.

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Roadmap

  • Feature 1
  • Feature 2
  • Feature 3
    • Nested Feature

This section is under construction, thanks for your understanding.

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EDA: A Path Model to Encoding.

Considering that it was necessary to make a codification to cluster the grouping of campsites. I based certain conclusions on the EDA analysis of Our Bumble Dataset.

Our Bumble DataSET

This dataset includes the analysis of Craig and Joanna's trip, through more than 700 campsites, caravan parks and glamping all over Europe, as well as several columns with information and comments.

From the wordcloud analysis of those corresponding to the countries to be covered in our recommender, I was able to establish certain rules for the coding of our original dataset: wild (relative distance from a population centre.) and luxury concepts. (an ordinal category based on luxury accommodations.)

We also took inspiration from their category type to develop our great circles by classifying nearby population centres as cities, towns or villages.

MODEL

  • Feature 1
  • Feature 2
  • Feature 3
    • Nested Feature

This section is under construction, thanks for your understanding.

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RECOMMENDER

This section is under construction, thanks for your understanding.

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License

Distributed under the GPL-3.0 license. See LICENSE.txt for more information.

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Contact

Feel free to reach out to me via email, LinkedIn, or Reddit for any inquiries, contributions to projects, or potential job opportunities:

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Acknowledgments

  • To Ainara Guerra for her project, an inspiration for a "crazy" idea.
  • To Isi, my lead teacher data at Ironhack for all the knowledge provided.
  • To my classmates, for their support and kindness these months.
  • To Xisca for the SQL masterclass.
  • To my dog "Loja" who patiently waited every day longer than necessary to go for a walk.

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