Auto MPG EDA

Service:

Data Analysis

Client:

Auto MPG EDA

Field:

Python, Pandas, NumPy, MatPlotLib, Seaborn, Data Visualization, Data Cleaning,

Year:

2023

Client:

View website

This project involves an exploratory data analysis (EDA) of the Auto_MPG dataset from UCI Machine Learning Repository, which contains technical specifications of cars, originally sourced from the UCI Machine Learning Repository. The analysis focuses on understanding city-cycle fuel consumption in miles per gallon (MPG) in relation to various attributes.

Dataset Description

The dataset consists of 398 records and 9 columns, which include:

mpg: Miles per gallon (continuous variable)
cylinders: Number of engine cylinders (multi-valued discrete variable)
displacement: Engine displacement (continuous variable)
horsepower: Engine horsepower (continuous variable)
weight: Car weight (continuous variable)
acceleration: Acceleration (continuous variable)
model year: Year of car release (1970 to 1982) (multi-valued discrete variable)
origin: Car manufacturing place (1 -> USA, 2 -> Europe, 3 -> Asia) (multi-valued discrete variable)
car name: Unique car model name

Findings and Insights

MPG Trends: Notable increase in MPG over the years with Asian cars leading in efficiency.
Cylinders vs. MPG: Higher cylinder counts correlate with lower MPG.
Origin Insights: USA produces the most cars but has lower MPG compared to Europe and Asia.

Conclusion

This EDA provides valuable insights into car specifications and their relationship with fuel efficiency. The findings can guide future automotive designs towards improved fuel economy.

Dataset Description

The dataset consists of 398 records and 9 columns, which include:

mpg: Miles per gallon (continuous variable)
cylinders: Number of engine cylinders (multi-valued discrete variable)
displacement: Engine displacement (continuous variable)
horsepower: Engine horsepower (continuous variable)
weight: Car weight (continuous variable)
acceleration: Acceleration (continuous variable)
model year: Year of car release (1970 to 1982) (multi-valued discrete variable)
origin: Car manufacturing place (1 -> USA, 2 -> Europe, 3 -> Asia) (multi-valued discrete variable)
car name: Unique car model name

Findings and Insights

MPG Trends: Notable increase in MPG over the years with Asian cars leading in efficiency.
Cylinders vs. MPG: Higher cylinder counts correlate with lower MPG.
Origin Insights: USA produces the most cars but has lower MPG compared to Europe and Asia.

Conclusion

This EDA provides valuable insights into car specifications and their relationship with fuel efficiency. The findings can guide future automotive designs towards improved fuel economy.

Dataset Description

The dataset consists of 398 records and 9 columns, which include:

mpg: Miles per gallon (continuous variable)
cylinders: Number of engine cylinders (multi-valued discrete variable)
displacement: Engine displacement (continuous variable)
horsepower: Engine horsepower (continuous variable)
weight: Car weight (continuous variable)
acceleration: Acceleration (continuous variable)
model year: Year of car release (1970 to 1982) (multi-valued discrete variable)
origin: Car manufacturing place (1 -> USA, 2 -> Europe, 3 -> Asia) (multi-valued discrete variable)
car name: Unique car model name

Findings and Insights

MPG Trends: Notable increase in MPG over the years with Asian cars leading in efficiency.
Cylinders vs. MPG: Higher cylinder counts correlate with lower MPG.
Origin Insights: USA produces the most cars but has lower MPG compared to Europe and Asia.

Conclusion

This EDA provides valuable insights into car specifications and their relationship with fuel efficiency. The findings can guide future automotive designs towards improved fuel economy.