Y = B0 + B1X + E

where

• Y is the predicted value of the dependent variable (y) for any given value of the independent variable (x).
• B0 is the intercept, the predicted value of y when the x is 0.
• B1 is the regression coefficient – how much we expect y to change as x increases.
• X is the independent variable (the variable we expect is influencing y).
• E is the error of the estimate, or how much variation there is in our estimate of the regression coefficient

• Microsoft Excel for data manipulation and linear regression analysis.
• Google Colab for python modules and scripts and jupyter notebook
• Framework: Sci-kit learn for building Linear regression model
• Scientific computing Libraries: numpy, pandas
• Visualization: Microsoft Excel, Seaborn, Matplotlib
• Programming language: Python

The grade points of the students were used to predict their score in another course. The 25th student has probability of scoring 70 marks on their next course while 35th student has probability of 68 marks in their next course. The major assumption is that course unit determines student grade. Students put effort in courses with three units than two units or one-unit courses. The data was properly preprocessed and we built a Linear Regression model to predict future scores of the two students individually in their respective new courses.

All necessary files(jupyter notebooks, screenshot and prepocessed dataset) are attached to this repository.