This repository presents an in-depth analysis of vehicle fuel economy, examining various factors influencing fuel efficiency, insights derived from correlation analysis and visualization, and potential implications for consumers, policymakers, and the automotive industry.
The dataset contains a comprehensive set of attributes related to vehicle specifications and fuel economy metrics, including vehicle ID, year of manufacture, make, model, class, drive system, transmission type, engine details, fuel type, fuel economy scores, greenhouse gas emissions scores, and more.Link to dataset https://www.kaggle.com/datasets/harinir/petrol-consumption
- Correlation Analysis Negative Correlation: City MPG and highway MPG show a negative correlation with engine displacement and number of cylinders, suggesting that larger engines tend to have lower fuel economy.
- Visualization Scatter Plot Analysis: The scatter plot of city MPG vs. engine displacement validates the negative relationship, indicating lower city MPG for vehicles with larger engine displacements. Fuel Type Comparison: A bar plot comparing average city MPG across different fuel types highlights variations in fuel efficiency, with electric vehicles typically exhibiting higher MPG ratings.
- Machine Learning (Optional) Linear Regression Model: A linear regression model predicts city MPG based on engine displacement, providing insights into fuel efficiency prediction. Evaluation metrics such as mean squared error (MSE) and R-squared values assess the model's performance.
Factors Affecting Fuel Efficiency: Engine size (displacement) and number of cylinders significantly influence vehicle fuel economy, favoring smaller engines for higher MPG ratings. Impact of Fuel Type: Choice of fuel type plays a crucial role, with alternative fuels like electricity offering advantages in terms of higher MPG ratings. Consumer Considerations: Consumers seeking fuel-efficient vehicles should prioritize models with smaller engines and alternative fuel options, considering trade-offs between engine size, fuel type, and performance. Policy Implications: Insights can inform policies to incentivize fuel-efficient technologies and alternative fuel infrastructure, benefiting consumers and the environment. Industry Perspective: Automakers can align product development and marketing strategies with consumer preferences for fuel-efficient vehicles, investing in hybrid and electric vehicle research and development. Limitations and Assumptions Data Limitations: Reliance on dataset quality and completeness may impact analysis validity. Simplifying Assumptions: Linear regression assumptions may oversimplify complex interactions affecting fuel economy. Generalization: Findings may not universally apply to all vehicle types and driving conditions, considering factors like weight, aerodynamics, driving behavior, and maintenance.
Project Documentation: Detailed description of data preprocessing, statistical analysis, and machine learning techniques. Comprehensive Report: Summarizes approach, results, and conclusions with visualizations, code snippets, and interpretations for stakeholders' understanding. By leveraging these insights and considering implications, stakeholders can derive meaningful conclusions about vehicle fuel economy's broader implications for consumers, policymakers, and the automotive industry.
Feel free to adjust or expand upon this terms License This project is licensed under the MIT License. See the LICENSE file for details.