Vehicle Image Classifier

In this project, I applied machine learning techniques to classify images of six different vehicle types with an accuracy of 83% using a Scikit-learn SVM classifier. I worked with a dataset of 4,320 images, leveraging feature engineering and model tuning to optimize performance. The project demonstrates my proficiency in machine learning workflows, from data preprocessing to model evaluation. Additionally, I showcased my full-stack development skills by creating a dynamic web application with a Python Flask backend and a user-friendly interface built with JavaScript, HTML, and CSS. This project highlights my ability to deploy machine learning solutions in a production-ready environment and deliver impactful tools for image classification.