This project aims to classify volatile gases based on their concentrations under different environmental conditions. The dataset used in this project contains diverse gas concentrations recorded over time. By developing a gas classification model, we can effectively identify and analyze gas levels, contributing to environmental monitoring and safety measures.
- Utilized Python, including NumPy, Pandas, Matplotlib, and Seaborn, for data analysis and visualization.
- Implemented various classification models, including SVM, Decision Tree, Random Forest, AdaBoost Classifier, and Gradient Boost Classifier.
- Conducted thorough testing and evaluation, measuring accuracy, precision, recall, and F1-score for each model.
- Selected the best-performing model and saved it as a pickle file for future use.