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This notebook uses the permutation importance method to assess feature significance in a regression model for vehicle fuel efficiency.

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Vehicle Fuel Efficiency Analysis & Prediction

This notebook analyses vehicle fuel efficiency using a dataset of cars with various attributes (e.g. engine size, weight, horsepower). The project involves:

  • Exploratory Data Analysis (EDA) to understand distributions and outliers
  • Data cleaning and handling missing values
  • Feature importance analysis using permutation importance
  • Building and evaluating regression models to predict fuel efficiency

By the end of this analysis, I identify key factors influencing fuel consumption and observe how model performance changes based on the features provided.

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This notebook uses the permutation importance method to assess feature significance in a regression model for vehicle fuel efficiency.

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