Statistics in Python

Inspiration

This project is inspired by statistical relationships and demonstrates regression techniques making it easier to understand data trends and predictions.

What it does

This project performs Linear Regression and Polynomial Regression.The linear regression fits a line of best fit through a set of (x, y) data points and plots the result. The Polynomial Regression uses a 2nd degree polynomial to better fit the data.

How we built it

To build this we used the Libraries matplotlib for plotting, scipy for statistical calculations, numpy for array manipulation, and sklearn for creating polynomial features and performing regression. For the Linear Regression we implemented it using scipy.stats.linregress to compute the slope, intercept, and other statistical parameters for the line of best fit. For the polynomial Regression ewe Utilized sklearn to generate polynomial features and fit a linear model to capture non-linear trends.

Challenges we ran into

The main challenge we had was handling the varying data. The polynomial regression needed adjusting to capture the correct trends. Also making sure that both the original data points and the fitted models were being visualized properly on the same plots.

Accomplishments that we're proud of

We are proud of this because we were able to successful implement multiple regression techniques. while also showing a good and clear visual which helps the user better understand.

What we learned

We learned the importance of visualizing statistical trends. and how to fit non-linear models by combining tools from multiple libraries.

What's next for Statistics in Python

Next we want to implement additional metrics like mean squared error or R² scores for a high level understanding. Also it will be beneficial for the user to interact with the graph more.

Built With

• python