A comparative analysis of traditional classification models for smoke detection.
- Smoke Detection using traditional ML classification models [Logistic Regression, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Naive-Bayes, Random Forest and eXtreme Gradient Boosting (XGBoost)].
- A comparative analysis of model performance on the given data.
This dataset consists of 60,000 readings of temperature, humidity, pressure, particulate matter, concentrations of compounds such as Hydrogen (H2), Ethanol and Carbon Dioxide (CO2), etc. taken using a set of different types of sensors from various indoor and outdoor locations as described in this hackster.io post about a real-time smoke detection system (https://www.hackster.io/stefanblattmann/real-time-smoke-detection-with-ai-based-sensor-fusion-1086e6). Data collection from several different locations along with the use of various different sensors provide us with a diverse set of features and data points to predict the presence or absence of smoke.