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Technical & Functional Requirements

Project

Company: Caterpillar

Subject group: Industrial Engineering

The objective of this project is to predict the price a supplier will quote for a given tube assembly. Like snowflakes, it's difficult to find two tubes in Caterpillar's diverse catalogue of machinery that are exactly alike. Tubes can vary across a number of dimensions, including base materials, number of bends, bend radius, bolt patterns, and end types. Currently, Caterpillar relies on a variety of suppliers to manufacture these tube assemblies, each having their own unique pricing model.

The data are certified valid and are found here.

Technologies

In this section, we write the list of languages and softwares that will be used for this project. We give also other important details like the configuration of softwares, computer details and the server where the project is stored.

Languages

  • MySQL 5.5.44 to store the data since the entire dataset is small enough (approximatively 6.8 Mb)
  • R version 3.2.1 (2015-06-18) to produce the analysis code and graphics
  • LaTeX with TeX Live 2013/Debian

Softwares

  • MySQL WorkBench version 6.0.8.11354
  • RStudio Version 0.99.465
  • LibreOffice Calc Version 4.2.8.2
  • Microsoft Excel 2010

Configuration details

The configuration details for MySQL will be set considering we will create tables once and we will insert a lot of data. Once it will be done, all queries will be selections of data. Since the data integrity is very important, MyISAM engine cannot be used. Therefore, we will use the InnoDB engine.

R main Packages used for this project are:

  • ggplot2
  • lubridate
  • randomForest
  • dplyr
  • RMySQL
  • R6
  • sqldf
  • xtable
  • knitr
  • stringr

If they are not installed, then use install.packages("PackageName") to get the package PackageName installed.

Operating Systems used

  • Linux Mint 17.1
  • Microsoft Windows 7, 8, 8.1 and 10

Project Server

The project files will be stored in github under the folder CaterpillarTubePricing

Versioning System

We will use GIT version 1.9.1 as our versioning system.

Once a functional requirement is done (means it is tested and considered as valid), we will create a tag with a valid name (refer to the Naming Convention section below). Therefore, the master branch will be updated with the changes from the new tag.

We will create a branch named Development where we will put reproductible analysis and all documents and codes used for the projects. The master branch must contain the documents representing the functional requirements (see last section of this document) and other important documents like the license for example.

Functional Requirements

  • F-01: Write a Code Book describing the dataset and its files and variables used from the dataset received as input.
  • F-02: Prepare and clean the dataset by creating a database containing the dataset cleaned. Get the script of the database and tables (and views) creation with the data inserted.
  • F-03: Identify variables needed for the analysis and explain why they will be more important for the analysis (independant and dependant variables).
  • F-04: Following the goal provided in the project summary, identify possible methods or models that can be applied to solve the problem and why they are considered.
  • F-05: Apply the suitable machine learning algorithms on the train and test sets and show every steps done.
  • F-06: Test the algorithm and show all results and improvements.
  • F-07: Compare results with the goal to reach and explain why the model succeed or needs improvements.
  • F-08: Show visually which features were important and improved the model.
  • F-09: Conclude by recommanding the successful model and explain the advantages for the company to use it.