Car Classification Models

This repository contains Python code for classifying car types based on various features using different machine learning models. The dataset used for training and testing consists of car attributes like buying price, maintenance cost, number of doors, number of persons, trunk size, and safety ratings. Three different classifiers, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree, are implemented to classify car types based on the provided features.

Steps:

1. Load and preprocess the dataset.
2. Split the dataset into training and testing sets.
3. Train the KNN classifier and evaluate its performance.
4. Train the SVM classifier and evaluate its performance.
5. Train the Decision Tree classifier and evaluate its performance.

Requirements

• Python 3.x
• numpy
• pandas
• scikit-learn

Results

• KNN Classifier Accuracy: 84.104046 %
• SVM Classifier Accuracy: 93.063289%
• Decision Tree Classifier Accuracy: 96.242775%