This project aims to develop a robust machine learning model that can accurately predict a vehicle's fuel efficiency or miles per gallon (mpg) based on various features such as engine displacement, horsepower, weight, and acceleration. By leveraging advanced regression techniques and analyzing historical data, this tool can uncover the intricate relationships between a car's characteristics and its mileage, enabling users to make informed decisions when purchasing a vehicle.
The primary objective of this project is to build a mileage prediction model using multiple linear regression analysis. The model will estimate the miles per gallon (mpg) of vehicles based on their features, including engine displacement, horsepower, vehicle weight, and acceleration. By establishing a statistical relationship between these variables and the corresponding mpg values, the model will enable accurate predictions of fuel efficiency for new or hypothetical vehicles.
The project utilizes a dataset containing information about various vehicles, such as their engine displacement, horsepower, weight, acceleration, and observed miles per gallon (mpg). This dataset will be used for training and evaluating the regression model.
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Data Preprocessing: The dataset will undergo thorough exploratory data analysis (EDA) to identify potential issues, outliers, or missing values. Necessary data cleaning and preprocessing steps will be performed to ensure the data's quality and suitability for modeling.
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Feature Engineering: Relevant features will be selected, and appropriate transformations or encoding techniques will be applied to enhance the model's performance.
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Model Training: A multiple linear regression model will be trained on the preprocessed data, establishing the relationship between the selected features and the target variable (mpg).
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Model Evaluation: The trained model's performance will be evaluated using appropriate metrics, such as mean squared error (MSE), root mean squared error (RMSE), and R-squared (R²) value. Cross-validation techniques may be employed to ensure the model's robustness and generalization capabilities.
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Deployment: The final model will be deployed as a user-friendly tool or application, allowing users to input vehicle specifications and obtain accurate mileage predictions.