Arduino+ DecisionTree from Python to C: Real-time Machine Learning on Embedded Systems
Learn how to create a decision tree model in Python using Scikit-learn and convert it to C code for efficient deployment on embedded devices. This tutorial uses MPU6050 sensor data to build a practical example and demonstrates the entire process, from data preprocessing to C code generation and testing. Ideal for engineers looking to implement machine learning on resource-constrained hardware.
This video is about decision tree algorithm python sklearn example where we will demonstrate rule based decision tree python code to train a decision tree model with MPU6050 dataset where the data have the following columns
import pandas as pd
Define column names
column_names = ['TimeStamp', 'Ax', 'Ay', 'Az', 'Gx', 'Gy', 'Gz']
Read the data into a DataFrame
data = pd.read_csv("normal_sample.csv", delimiter=",", names=column_names)
We also convert that decision tree and print that into text then use the ChatGPT to convert that decision tree into the C function and then run a complete C code to test the trained model This is best example of decision tree algorithm in machine learning python and how to implement and trasfer it to C code so that it could be used with any Embedded system like Arduino or STM32 and could be an easy alternative to tinyml machine learning models required to be done on edge devices or embedded systems.
