Stack Information: It recovers information from an Exceed expectations record found at an indicated record way from my laptop.
Information Cleaning: The code guarantees information cleanliness by evacuating columns with lost values (NaN) within the “Inactivity” column.
Data Setup: After cleaning, the data is split into two parts:
Independent variables (X): These are features that might affect “Inactivity,” like “% Diabetes” and “% Obesity.”
Dependent variable (y): This is the variable we want to predict, which is “Inactivity.”
Linear regression model: The code creates a linear regression model, which is a mathematical formula that finds the relationship between the independent variables (diabetes and obesity percentage) and the dependent variable (inactivity percentage).
Model Training: a model is trained on data to learn how changes in the independent variables affect the dependent variable. It identifies the line of best fit that minimizes the difference between predicted and actual “activity” rates.
Print Results: The code displays the results of a linear regression analysis, including the intercept (where the line intersects the Y-axis) and the coefficients (the slope of each independent variable). These values help interpret the relationship between the variables.
Make predictions: Using the trained model, the code predicts the “inactivity” rate based on the new values of the independent variables (diabetes and obesity rates).
Plot Results: A scatterplot is created to visualize the performance of the model. It compares actual unemployment rates (X-axis) to predicted rates (Y-axis). A well-fitting model has points that are strictly aligned with the diagonal line.