Knowing the time taken to boil water is useful in the kitchen, chemistry lab and more. In this blog post, I show how I used machine learning to build a model that estimates the time taken to boil a defined volume of tap water in coastal areas using an electric stove.
Gathering The Data
I began by gathering real-world data of the time (in minutes) taken to boil certain volumes (in liters) of tap water in coastal areas.
Visualizing The Data
I then used Python in a Kaggle notebook to determine some important metrics of my data and to plot a scatter plot showing the relationship between the volume of water in liters and the time taken in minutes. Since the R-Squared values were close to 1, this means the relationship between volume and time is essentially linear.
Using The Linear Regression Algorithm
The last step was to use the linear regression algorithm found in the sklearn Python module to determine the parameters necessary for a working model. The parameters and visual representation of the model are seen below.
As you can see above, building a model that estimates the time taken to boil a defined volume of tap water in coastal regions using an electric stove – with some level of accuracy – is possible. I hope that this blog post inspires you to use machine learning.
It is worth noting that more investigation is needed to ensure the best results.
* Since technology is continually developing, by the time you read this blog the products used may have changed.