Project Title: Development of a Random Forest Model in R Studio for Vehicle Selection and an Interactive Graphic Interface

Description: In this project, I designed and built a Random Forest model using R Studio to assist users in making informed decisions when purchasing a vehicle. The main objective was to provide a tool that takes critical variables such as safety, maintenance, and cost into account and helps users make informed and suitable choices.

Objective: The central aim of the project was to create a machine learning model that effectively evaluated available vehicle options based on multiple factors, including safety, maintenance cost, and purchase price. Additionally, the project aimed to simplify data input and result visualization through a user-friendly graphic interface.

Scope: The project encompassed various stages, from data collection and preparation to the design and training of the Random Forest model. To address data imbalance, preprocessing and class balancing techniques were applied. Subsequently, an interactive graphic interface was developed using R's Shiny library to allow users to input their preferences conveniently and receive real-time recommendations.

Key Achievements:

• Successful creation of a Random Forest model that provides vehicle purchase recommendations based on safety, maintenance, and cost criteria.

• Effective resolution of data imbalance through preprocessing techniques.

• Development of an interactive and user-friendly graphic interface with Shiny, facilitating user interaction with the model.

• Streamlining vehicle purchase decision-making by providing clear and personalized results.

This project combines the power of machine learning algorithms with the convenience of a user interface, offering vehicle buyers a valuable tool to choose the right vehicle based on their needs and preferences.