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This study uses Python and Jupyter notebooks to compare how efficient vehicles manufactured in 2008 are compared to vehicles manufactured in 2018.

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Fuel Economy Data Analysis

This project analyzes and compares vehicle fuel efficiency between 2008 and 2018 using Python and Jupyter notebooks. The analysis focuses on determining improvements in fuel economy over the course of the decade.

Tools and Libraries:

  • Python: Core programming language for data processing.
  • Jupyter Notebooks: Interactive environment for writing and visualizing code.
  • Numpy and Pandas: Data manipulation, cleaning, and analysis.
  • Matplotlib: Data visualization through plots and graphs.

Data:

  • Two CSV datasets representing vehicle data from 2008 and 2018.
  • Data cleaning involved handling missing values, inconsistencies, and formatting issues to ensure accurate analysis.

Project Highlights:

  • Assessing and visualizing differences in fuel efficiency metrics.
  • Identifying key trends and insights across a 10-year span.
  • Producing visual representations to compare and present findings.

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This study uses Python and Jupyter notebooks to compare how efficient vehicles manufactured in 2008 are compared to vehicles manufactured in 2018.

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