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Fuel Consumption Analysis using Linear Regression

Overview

This repository delves into fuel consumption data analysis using both simple and multiple linear regression models. We aim to uncover the relationships between various factors (independent variables) and fuel consumption (dependent variable).

Data

The provided dataset, fuel_consumption.csv, includes the following features:

  • MODELYEAR: Model year of the car
  • ENGINESIZE: Engine size of the car
  • CYLINDERS: Number of cylinders
  • TRANSMISSION: Type of transmission
  • FUELTYPE: Type of fuel
  • DRIVEMODE: Type of drive mode
  • CITYMPG: Fuel consumption in the city (miles per gallon)
  • HWYMPG: Fuel consumption on the highway (miles per gallon)
  • COMBMPG: Combined fuel consumption (miles per gallon)
  • CO2EMISSIONS: CO2 emissions (grams per mile)

Analysis and Modeling

  1. Data Exploration and Cleaning

    • Load and explore the dataset for initial understanding.
    • Address missing values and outliers as needed.
    • Visualize the distribution of numerical variables.
    • Check for potential correlations between variables.

  2. Simple Linear Regression

    • Build a simple linear regression model to predict CO2EMISSIONS using ENGINESIZE as the predictor.
    • Evaluate model performance using R-squared, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
    • Visualize the regression line and scatter plot.

  3. Multiple Linear Regression

    • Construct a multiple linear regression model to predict CO2EMISSIONS considering multiple features like ENGINESIZE, CYLINDERS, FUELTYPE, and DRIVEMODE.
    • Use similar performance metrics for evaluation.
    • Analyze the model's coefficients to understand the impact of each feature on CO2EMISSIONS.

Code Structure

  • data: This directory stores the fuel_consumption.csv dataset.
  • notebooks: Jupyter Notebooks for data exploration, visualization, and model building.
  • src: Python scripts for data preprocessing, model training, and evaluation.

Requirements

  • Python (version 3.x)
  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-learn

Getting Started

  1. Clone this repository.
  2. Install required libraries: pip install -r requirements.txt.
  3. Run Jupyter Notebooks in the notebooks directory for data exploration and model building.
  4. Execute Python scripts in the src directory to run the analysis and generate results.

Additional Notes

  • Experiment with different feature combinations and model hyperparameters for potential performance improvements.
  • Consider feature engineering and regularization techniques to enhance model accuracy and generalization.
  • Visualize results to gain insights into relationships between variables and model predictions.

Feel free to give feedback and suggestions for improvement!