VS Code App - Analyze and Predict Temperature & Humidity

This section details the engineering of a real-time embedded system integrating ESP32 and environmental sensors for continuous data acquisition, development of a cross-platform data pipeline for live sensor streaming and analysis, and the design of interactive visualization tools and machine learning models for predictive analysis of environmental data.

The equipment before implementation

+) ESP32 Development Board

+) DHT11 Temperature and Humidity Sensor

+) Python Programming Language

+) Data Visualization Libraries (Matplotlib)

+) Machine Learning Libraries (Scikit-learn)

VS Code Setup

Figure 1: Data analysis and visualization of temperature and humidity trends using Python libraries.

Data Analysis: (download the code)

+) Developed real-time data analysis pipeline in Python (VS Code) for ESP32 sensor data

+) Implemented serial communication using PySerial for live data acquisition

+) Applied signal processing techniques (moving average filtering) to reduce noise

+) Built real-time visualization using Matplotlib for monitoring temperature and humidity

+) Performed data preprocessing and trend analysis on environmental data

VS Code Setup

Figure 2: Predictive analysis of future temperature and humidity values using a linear regression model.

Prediction: (download the code)

+) Developed predictive model using Scikit-learn (linear regression)

+) Modeled relationship between humidity and temperature for real-time estimation

+) Integrated prediction into live data stream for continuous comparison with actual values

+) Evaluated model performance using real sensor data

+) Demonstrated application of machine learning in embedded systems context