In this project, we embarked on a comprehensive journey through the world of Machine Learning and model evaluation. Our primary goal was to develop a Tkinter GUI and assess various Machine Learning models on a given dataset to identify the best-performing one. This process is essential in solving real-world problems, as it helps us select the most suitable algorithm for a specific task. By crafting this Tkinter-powered GUI, we provided an accessible and user-friendly interface for users engaging with Machine Learning models. It simplified intricate processes, allowing users to load data, select models, initiate training, and visualize results without necessitating code expertise or command-line operations. This GUI introduced a higher degree of usability and accessibility to the Machine Learning workflow, accommodating users with diverse levels of technical proficiency. We began by loading and preprocessing the dataset, a fundamental step in any Machine Learning project. Proper data preprocessing involves tasks such as handling missing values, encoding categorical features, and scaling numerical attributes. These operations ensure that the data is in a format suitable for training and testing Machine Learning models. Once our data was ready, we moved on to the model selection phase. We evaluated multiple Machine Learning algorithms, each with its strengths and weaknesses. The models we explored included Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Decision Trees, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and Support Vector Classifier (SVC).