Cars Evaluation Project

Project Description

This project (write a data science blog post) is a part of Udacity Data Scientist Nanodegree Program. In this project, I have analyzed data publicly available about Car Evaluation and was downloaded from the UC Irvine Machine Learning Repository. also I have used supervised machine learning algorithms Random Forest. in this project, I have tried to analyze and answer the following questions:

1. Is the very good car expensive?

2. Is the safety will be high on the expensive cars only?

3. Is the price of cars affect the price of maintenance?

Data:

The dataset consists of 1728 rows and 7 columns and you can find more information on the link below:

https://archive.ics.uci.edu/ml/datasets/Car+Evaluation

Featureset Exploration:

Class Values:

• unacc, acc, good, vgood

Attributes:

• buying: vhigh, high, med, low.
• maint: vhigh, high, med, low.
• doors: 2, 3, 4, 5more.
• persons: 2, 4, more.
• lug_boot: small, med, big.
• safety: low, med, high.

Software and Libraries:

• numpy
• pandas
• matplotlib
• sklearn

Results:

you can see my finds in the blog on the link below:

https://fatimammb.blogspot.com/2019/11/car-evaluation.html