Exercice 1 - Write a Data Science blog post

Udacity Data Scientist Nanodegree Project.

Table of contents

1. Introduction
2. Project Motivation
3. How to use
4. Results
5. Licensing, Authors, and Acknowledgements

Introduction

This repository solves the first exercise of the Data Science Nanodegree from Udacity called Write a Data Science Blog Post.

This post will analyse this data set which contains information of 97029 BMW cars like price, transmission, mileage, fuel type, road tax, miles per gallon (mpg), and engine size

Project Motivation

As a BMW insider, it would be nice to do this first project using a data set directly correlated to my day-to-day business activities.

This course is my first touch at data science. It's good to develop and learn the concepts and apply this to something correlated to my job and possible future activities.

With that in hand, I started to search for BMW related data set and found a list of BMW cars that had the potential to answer some good questions like:

1. Which is the most desirable model that BMW produce?

A series 3 model is one of the BMW most sold cars. Is this true for all years? To answer this question I need to understand which is the most desirable car produced every year.

2. What model lose more market value?

It's also common sense that a used car loses value every year, but which car lost the most from a distance of one year. Is good to know this information because it can help later to understand, for instance, if is there something wrong with a specific unity produced or to start a further investigation that could improve the quality of future cars.

3. What model gain more market value?

At the same time that some cars might lose market value, some may gain. It might happen due to a change of market desired, to a movie that shows a specific model, etc. So it would be nice to know which car grow the most market value from a distance of one year.

How to use

This project only depends on two libraries:

1. pandas
2. matplotlib

Before running the project, please install then.

As this project was made using Notebook technologies, just use any notebook client to run it.

Results

The results of this study can be found in the blog post created.

Licensing, Authors, and Acknowledgements

The data set is public and can be found here

The author of this project is Rodrigo Sabben

The code is licensed under MIT License.