This repository contains code and guidelines for a machine learning project that aims to classify roads into clean or littered based on images. The project consists of several tasks, including data preprocessing, model implementation, and performance analysis.
The project is organized into the following sections:
Exploratory Data Analysis (EDA):
- Explanation of the problem and advantages of solving it without machine learning.
Image Acquisition and Enhancement:
- Code and results for image preprocessing and enhancement using R programming.
Image Data Modeling:
- Implementation of machine learning algorithms for image classification.
- Splitting data into training and testing datasets and analysis of data percentages used.
Performance Analysis:
- Application of evaluation techniques to assess model performance.
- Proposals to improve classification performance.