Google drive link for the report and presentation:https://drive.google.com/drive/folders/1cDGjProa1FSOgke5fb2ccFvhgEBnykIz?usp=sharing
In future, there is no doubt AI & IOT are going to be a revolution, which are making our life’s simpler and comfortable. Here we are mainly focusing on the implementation of AI models to control the devices by concerning the input from the IoT sensors. We concentrate on the decision making of inside environmental parameters like temperature and humidity. And external parameters such as Air quality, Light, the sound that causes different types of pollutions like Air pollution, sound pollution and the new emerging pollution in cities called Light pollution which causes uncomfortable feel to birds and humans. So, we designed and built an IoT device through Raspberry Pi and its different sensors that generate and store data related to the temperature, air quality, light, sound. And we apply the AI models in the prediction and decision making from the data generated and sends necessary control actions. We even apply the AI while storing the data by using some algorithms to take the unrepeated and useful data concerning time
Built a logistic regression model to build a machine learning model to open and close the windows using Logistic regression Model
here we built a logistic regression Machine Learning model using the SciKit Learn Library and regular python programming
accuracy=1.0(overfitting)
so lets try SVM
found that SVM give the best validation accuracy of 90%(0.9) so we are using this model in Raspberry PI