IBM Recommendation Engine

Table of contents

• Installation
• Instructions
• Project motivation
• File descriptions
• Results
• Creator
• Thanks

Installation

In order to be able to execute your own python statements it should be noted that scripts are only tested on anaconda distribution 4.5.11 in combination with python 3.6.6.

Two quick start options are available:

• Download the latest release.
• Clone the repo: git clone https://github.com/FrankTub/RecommendationEngine.git

Instructions:

TBC

Project motivation

For the second term of the nanodegree become a data scientist of Udacity I got involved in this project.

File descriptions

Within the download you'll find the following directories and files. TBC

RecommendationEngine/
├── README.md
├── ETL Pipeline Preparation.ipynb # Notebook to prepare cleaning function
├── ML Pipeline Preparation.ipynb # Notebook to test out machine learning algorithms
├── app/
    ├──	run.py # Flask file that runs app
    ├── templates/
    |       ├──	master.html  # main page of web app
    |       └── go.html  # classification result page of web app  
├── data/
    ├── categories.csv  # data to process
    ├──	messages.csv  # data to process
    ├── process_data.py
    └── DisasterResponse.db   # database to save clean data to
└── models/
    ├── train_classifier.py
    └──	classifier.pkl  # saved model, not stored in github repository due to size, run ML pipeline to create this model.

Results

TBC

Creator

Frank Tubbing

• https://github.com/FrankTub

Thanks

Thanks to Udacity for setting up the projects where we can learn cool stuff!

Thanks to IBM for providing cool data with which we can create a cutting edge project!