Inspiration

We wanted to make a more user-friendly visual to show the components of linear regression given any set of data points. Normal calculators used in schools don't show the labels of each component of linear regression, and we wanted to address that.

What it does

The user can input the number of coordinates they want to find the linear regression of and input the corresponding coordinates. The program will display the linear line of best fit as well as the slope, y-intercept, R-value, statistical significance (P-value), and standard error.

How we built it

We used different parts of the SciPy library to build a linear regression model. We also take in user input to dictate how many coordinates the user wants to be calculated. We also use the MatPlotLib library (which extends the NumPy library) to mathematically plot the coordinates given by the user.

Challenges we ran into

We didn't know enough machine learning or AI to code something at the beginning of this hackathon, and we also needed to relearn python so that we could submit something.

Accomplishments that we're proud of

We're proud of our understanding in importing libraries into our code, which is something that we haven't really used before in any program.

What we learned

How to use math libraries in python within our code.

What's next for Linear Regression Calculator

This calculator can be improved in that we can calculate other types of regression such as polynomial regression and exponential regression so that students can calculate the type of regression which best fits their needs.

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