[final-project] Minerva Liste

.gitignore

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README.md

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+<img src="https://bit.ly/2VnXWr2" alt="Ironhack Logo" width="100"/>
+
+# To read, not to read... what to read?
+*[Minerva Liste Calleja]*
+
+*[DAFT, Ironhack - Mar'20]*
+
+## Content
+- [Project Description](#project-description)
+- [Dataset](#dataset)
+- [Links](#links)
+
+## Project Description
+
+The project aims to explore different approaches currently used to build recommender systems. In a world where the amount of options available is increasingly growing (no matter if we are talking about clothes, films or restaurants), being able to provide curated selections is more and more important every day. My goal is to understand the possibilities currently available and create some models that  help me grasp how these existing possibilities work and how they can be improved.
+
+Though the project is based on the recommendation of books, other types of products could also be considered.
+
+
+## Dataset
+
+The dataset used in this project is the goodbooks-10k:
+
+@article{goodbooks2017,
+    author = {Zajac, Zygmunt},
+    title = {Goodbooks-10k: a new dataset for book recommendations},
+    year = {2017},
+    publisher = {FastML},
+    journal = {FastML},
+    howpublished = {\url{http://fastml.com/goodbooks-10k}},
+}
+
+
+
+## Links
+
+The complete analysis and the code for the book recommender system can be found in [this link](https://github.com/minervaliste/Project-Week-8-Final-Project/blob/master/An%20approach%20to%20book%20recommendations.ipynb) 
+
+Moreover, full access to the dataset and the working code is provided through [my Github repository](https://github.com/minervaliste/Project-Week-8-Final-Project) 
+
+This project was developed during the Data Analytics Full Time Bootcamp at Ironhack Barcelona (Jan-Mar 2020). The slides of the project presentation can be seen [here](http://bit.ly/book_recommender)
+
+