Fuel Economy Data Analysis 📊

Project Description:

Data Analysis with Pandas, Numpy and Matplotlib 📈 about a fuel economy data 💸 from US car models 🚗 from 2008 and 2018

Reason we're studying this dataset:

I have some questions regarding greenhouse effect 🌳 and how the cars are related to this effect, therefore I created this 3 questions:

Q1: Are more unique models using alternative fuels in 2018 compared to 2008? By how much 🤨?

Q2: How much have vehicle classes improved in fuel economy 📉 (increased in miles per gallon)?

Q3: For all of the models that were produced in 2008 that are still being produced now, how much has the miles per gallon improved and which vehicle improved the most 🚗?

Want to know more about data analysis process 🤔?

Step 1: Assessing Data 🔍:

Click Here if you want to know more about how to assess data in Pandas.

Step 2: Data Cleansing 🧹:

Click Here if you want to know more about how to clean data, this is a highly demand skill, because you will always encounter data quality issues.

Step 3: Fixing Datatypes 🔨:

Click Here if you want to know more about how to fix datatypes using Jupyter Notebook.

Step 4: Exploring your data using visualizations 📈:

Click Here if you want to know how to create visualizations using matplotlib.

Step 5: Conclusions 💡:

Click Here if you want to know what were the answers to the questions proposed to this project.

Additional Step: Merging data frames to answer more questions 🔄:

Click Here if you want to know the importance of merging datasets together and review the last question of this project to know which car was the most fuel efficient in the United States in 2018.

Credits:

Data Analyst Udacity Nanodegree Course