Prediction using Supervised ML: R-pubs

 The task aims to predict the percentage of a student based on the number of study hours. It involves building a simple regression model with two variables. I used R programming to achieve this task.

Overview

 Each observation of the dataset consisting a tuple about no of hours a one spent studying and one’s percentage score. We can build a simple linear regression using the dependent and independent variables.

Procedure

 Package like rio and e1071 are used. Used different visualizations before you jumping to modeling. Build the model using built-in R functions and evaluated using various metrics such as R-squared, Adjusted R Squared, P-value, and measuring the goodness-of-fit by AIC and BIC.

Conclusion

 Some things you can explore are imputing more features such as minutes, seconds. Maybe there could be a different trend. You can also try other regression models.

References

• R basics by Statistical tools for high-throughput data analysis(STHDA)
• An introduction to simple linear regression by Rebecca Bevans,Scribbr
• 5 Types of Regression Analysis And When To Use Them by Appier
• 7 effective ways to handle small data by by Kateryna Koidan, hackernoon
• R Programming Tutorial - Learn the Basics of Statistical Computing by Freecodecamp