Car Evaluation Database

Overview

This repository contains the Car Evaluation Database obtained from the UCI Machine Learning Repository. The main objective of this project is to apply Machine Learning algorithms to analyze and predict the evaluation of cars based on a categorical target variable.

Dataset

The dataset used in this project is the Car Evaluation Database from the UCI Machine Learning Repository. The dataset model bases its assessment of car acceptance on three concepts: cost, technical characteristics, and comfort.

1. buying: Buying price (vhigh, high, med, low)
2. maint: Price of maintenance (vhigh, high, med, low)
3. doors: Number of doors (2, 3, 4, 5, more)
4. persons: Capacity in terms of persons to carry (2, 4, more)
5. luggage boot: Size of luggage boot (small, med, big)
6. safety: Estimated safety of the car (low, med, high)

Algorithms Used

Three Machine Learning algorithms were employed to perform analysis and prediction on the car evaluation dataset:

1. k-Nearest Neighbors (kNN)
2. Support Vector Machine (SVM)
3. Random Forest

Documentation

The documentation for this assignment can be found here

Google Colab

This assignment was completed in Google Colab, an online platform for Python programming and Machine Learning.

License

This project is licensed under the MIT License.