<I'm Çağlar Çakmak/>
Artifical Intelligence Developer
İzmir, Turkey
Successfully graduated from the Computer Programming Department at Ege University. Passionate and innovative Software Developer with a strong background in developing and deploying Destkop Applications, AI models for real-world applications. Expertise in machine learning, deep learning, and computer vision. Proficient in Python, C# and SQL with experience integrating AI models into web and mobile applications. Adept at turning raw data into actionable insights Motivated to leverage my technical skllis and AI knowledge to create advanced AI-driven solutions for complex business challenges.
Python, YOLO
Developing a system for Mercedes-Benz Authorized Service. System designed to detect vehicle license plates and features in real-time using +30 cameras. The system will leverage YOLO algorithm for image processing, CRNN architecture for plate recognition and data management, and it will feature a user-friendly interface to enable quick access for relevant departments. The goal of this system is to enhance operational efficiency and optimize vehicle tracking.
Python, YOLO, Supervision
Developing a real-time football analysis system using YOLO for object detection, tracking player positions, referee locations, ball possession percentages, mini radar-camera movement estimator, heatmap, player speeds and distances covered. This system provides real-time performance metrics to support tactical decision-making and team strategy optimization during matches.
Python, YOLO
Developed an object recognition model using YOLO algorithm as part of the company's R&D project, achieving an accuracy rate of 87%. This project significantly enhanced my expertise in object detection and provided valuable experience in teamwork and project management.
Python
Built a machine learning model with 97% accuracy to predict real estate prices in Bangalore, India. The model was developed using NumPy, Scikit-Learn, and Pandas for data preprocessing, and GridSearchCV for hyperparameter tuning, then deployed into a Flask-based web application.
Python
MySQL
Developed an Artificial Neural Network (ANN) model with 96% accuracy to predict customer churn for banks. This model analyzes various customer parameters to improve customer retention strategies, applying Exploratory Data Analysis (EDA) and data preprocessing techniques.
React
It aims to develop an application that can diagnose diseases from tomato plant leaves using Deep Learning and React Native. The application aims to speed up disease diagnosis in the agricultural sector, increasing farmers' productivity. The model, developed with TensorFlow, utilizes Convolutional Neural Network (CNN) architecture.