In todays competition, the author will present you a dataset. This dataset contains a total of 8 attributes and concerns over predicting the fuel consumption of various vehicles rode in cities. Therefore, this is a job of regression with multiple variables.
The project is about constructing a machine learning model that accurately predicts the fuel consumption from the provided dataset.
1. Python 2. Numpy 3. Pandas 4. Matplotlib 5. Scipy 6. Scikit-learn 7. Statsmodel
From the dataset give to us, I have concluded that:
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Car weight and displacement have the strongest inverse correlation with mileage. Lines up well with intuition that the more weighted cars isn’t the most efficient user of gasoline.
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Horsepower and number of cylinders are also strongly inversely correlated with mileage meaning a fast car needs more number of cylinders.
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MPG is also the primary measurement of a car's fuel efficiency.
Keeping these three factors in mind, I have designed a multiple linear regression ML model to make the predictions.
Multiple Linear Regression is one of the important regression algorithms which models the linear relationship between a single dependent continuous variable and more than one independent variable.
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http:https://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
https://www.kaggle.com/code/rinichristy/best-predictors-for-fuel-consumption-of-vehicles