Regression is an analytical technique used to make a relationship between input variables and continuous outcome variables.
A sample of data on cars is provided for this assignment. The file contains data on sales of used cars with 1436 records containing details. The attributes of the data include Age (Age in years), KM (Accumulated Kilometers on the odometer), HP (horsepower), CC (Cylinder Volume in cubic centimeters), Doors (Number of doors), Weight (Weight in Kilograms), and Price (Price of Cars). Run a multiple regression with the outcome variable Price and the predictor variables Age, KM, HP, CC, Doors, and Weight.
Before performing the analysis.
Perform descriptive analysis.
Perform correlational analysis and describe the analysis.
Apply standardization to the data.
Perform correlational analysis.
Split the data into a training set and a testing set of the data.
Perform multiple regression on the training set!
Perform
Explain the linear regression coefficient and intercept.
Specify the linear regression equation.
Perform model evaluation – Mean Squared Error (MSE)
Include a brief description of each step and the screen print of your Python codes in your paper (Word file).
Submit your assignment.
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