To improve R-squared, you can focus on including only the most relevant predictors that genuinely impact the outcome. Also, you can address “multicollinearity”. For those unaware, it represents a situation where two or more predictors (independent variables) in a model are too similar or highly correlated. You can address this problem by using techniques like variance inflation factor analysis.
Lastly, you can refine your model by exploring different variable relationships (such as non-linear or interaction terms) to better capture the patterns in the data.