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Problem Statement:

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.

The tools I used for this project are:

1. Python 2. Numpy 3. Pandas 4. Matplotlib 5. Scipy 6. Scikit-learn 7. Statsmodel

Concept and Model:

From the dataset give to us, I have concluded that:

  1. 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.

  2. Horsepower and number of cylinders are also strongly inversely correlated with mileage meaning a fast car needs more number of cylinders.

  3. 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.

Citations:

Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://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

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