Fuel-Consumption-Prediction-Simple-Linear-Regression

Fuel Consumption Prediction by Simple Linear Regression

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Dataset:

https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv

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Understanding the Data

FuelConsumption.csv: We have downloaded a fuel consumption dataset, FuelConsumption.csv, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada.

MODELYEAR e.g. 2014

MAKE e.g. Acura

MODEL e.g. ILX

VEHICLE CLASS e.g. SUV

ENGINE SIZE e.g. 4.7

CYLINDERS e.g 6

TRANSMISSION e.g. A6

FUEL CONSUMPTION in CITY(L/100 km) e.g. 9.9

FUEL CONSUMPTION in HWY (L/100 km) e.g. 8.9

FUEL CONSUMPTION COMB (L/100 km) e.g. 9.2

CO2 EMISSIONS (g/km) e.g. 182 --> low --> 0

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Simple Regression Model

Linear Regression fits a linear model with coefficients B = (B1, ..., Bn) to minimize the 'residual sum of squares' between the actual value y in the dataset, and the predicted value yhat using linear approximation.

Coefficient and Intercept in the simple linear regression, are the parameters of the fit line. Given that it is a simple linear regression, with only 2 parameters, and knowing that the parameters are the intercept and slope of the line, sklearn can estimate them directly from our data.

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Evaluation

We compare the actual values and predicted values to calculate the accuracy of a regression model. Evaluation metrics provide a key role in the development of a model, as it provides insight to areas that require improvement.

There are different model evaluation metrics, lets use MSE here to calculate the accuracy of our model based on the test set:

Mean Absolute Error:

It is the mean of the absolute value of the errors. This is the easiest of the metrics to understand since it’s just average error.

Mean Squared Error (MSE):

Mean Squared Error (MSE) is the mean of the squared error. It’s more popular than Mean Absolute Error because the focus is geared more towards large errors. This is due to the squared term exponentially increasing larger errors in comparison to smaller ones.

Root Mean Squared Error (RMSE):

R-squared is not an error, but rather a popular metric to measure the performance of your regression model. It represents how close the data points are to the fitted regression line. The higher the R-squared value, the better the model fits your data. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).