Air Quality Data Analysis & Prediction using Machine Learning

The application provides a simple graphical interface built with Tkinter that allows users to upload a dataset and automatically perform data preprocessing, regression analysis, and classification. Main features of the system: • Upload CSV dataset for air quality analysis • Automatic data cleaning and preprocessing • Linear Regression model to predict CO (Carbon Monoxide) levels • Logistic Regression model to classify high CO levels • Automatic train/test data splitting • Performance evaluation using metrics such as: R² Score Mean Squared Error (MSE) Accuracy Classification Report The application also generates visualizations including: • True vs Predicted values plot for regression • Confusion Matrix visualization for classification Technologies used: Python Pandas NumPy Scikit-learn Matplotlib Seaborn Tkinter (for GUI) This project demonstrates practical skills in: Data preprocessing Machine learning modeling Model evaluation Data visualization Building interactive data science applications ثانياً: المهارات (Skills) حطي مثلاً: Python Data Analysis Machine Learning Scikit-learn Pandas Data Visualization Tkinter GUI