Udacity Data Scientist Nanodegree - Recommendations with IBM Watson

Table of Contents

1. Project Overview
2. Libraries
3. File Description
4. Notebook Contents
5. Authors, Acknowledgements

Project Overview

For this project I will analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles you think they will like. Below you can see an example of what the dashboard could look like displaying articles on the IBM Watson Platform.

Libraries

• pandas
• numpy
• matplotlib
• pickle
• seaborn
• sklearn

File Descriptions

|-- README.md-------------------------#Readme File
|-- Recommendations_with_IBM.html-----#HTML Converted Project
|-- Recommendations_with_IBM.ipynb----#Project in Jupyter Notebook
|-- data------------------------------#Necessary Data Files in .csv
|-- img-------------------------------#images for readme file
|-- project_tests.py------------------#Test for rubrics
|-- top_10.p--------------------------#Pickled Test File
|-- top_20.p--------------------------#Pickled Test File
|-- top_5.p---------------------------#Pickled Test File
|-- user_item_matrix.p----------------#Pickled User Item Matrix

Notebook Contents

I. Exploratory Data Analysis
II. Rank Based Recommendations
III. User-User Based Collaborative Filtering
IV. Matrix Factorization

Authors, Acknowledgements

Thanks to Udacity for providing this project to me. All codes are written by Udacity and A. Uygur Yiğit