Bhagyashree Deshmukh
Published © GPL3+

Polynomial Regression to predict light trend using Bolt

To collect light intensity data and applying polynomial regression and ML algorithms to predict the light intensity trend using Bolt.

BeginnerFull instructions provided1 hour99
Polynomial Regression to predict light trend using Bolt

Things used in this project

Hardware components

Bolt WiFi Module
Bolt IoT Bolt WiFi Module
×1
LDR, 5 Mohm
LDR, 5 Mohm
×1
Resistor 10k ohm
Resistor 10k ohm
×1
USB-A to Mini-USB Cable
USB-A to Mini-USB Cable
×1
Android device
Android device
You can use ioS device as well instead of an android device
×1

Software apps and online services

Bolt Cloud
Bolt IoT Bolt Cloud

Story

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Schematics

Android App and Hardware

The device is connected as there is a green dot in front of the name of the device and in the hardware, green and blue lights are onn indicating that the connection is formed.

Final Project Video

In this video, I have shown the hardware , the mobile app as well as the product configuration. I have shown deployment of data and the visualization of the graph. Note that better recording the video I had already deployed a number of light values so as to get better prediction.

Code

Code for prediction graph

JavaScript
You can name your graph as per your convenience. In this code, I have named my graph as 'Polynomial Regression on LDR'.
setChartLibrary("google-chart");
setChartTitle('Polynomial Regression on LDR');
setChartType('predictionGraph');
setAxisName('time_stamp','light');
plotChart('time_stamp','light');

Credits

Bhagyashree Deshmukh

Bhagyashree Deshmukh

1 project • 0 followers

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