A Supervised Machine Learning model that gives prediction for the percentage of marks a student will secure given the number of hours they have studied.

Introduction

An overview on how machine learnig algorithims could be effectively implemented in creating a simple Linear Regression models that is capable of making some basic predictions.

Steps Involved

• Loading libraries and data is always the first step,

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• To see if there exist any pattern between the number of hours studied and the marks obtained a graphical evaluation approach is adopted.

• Once the correlation study and visualisation is carried out, it is further required to split the data into two parts: one part for training and the other part for testing.

• A linear regression model is then called upon and is fed with the training data which trains the model.

• After training the created model, it is finally ready to make predictions.

• The predicted scores are then displayed along with the actual scores to get a glimpse of the model performance.

• At this stage our trained machine learning model is asked to make prediction for a sample question- eg: What will be the predicted score if a student studies for 9.25 hrs in a day?

• Towards the end, the model accuracy is tested by calculating Mean Absolute Error and Root Mean Square Error.

Conclusion

A machine learning model is acceptable if both the values of Mean Absolute Error and Root Mean Square Error are minimum.