Data-Science-Project

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Data-Science-Project

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This project focuses on analyzing electric vehicle adoption trends, policy eligibility, and regional distribution using publicly available registration data from the Washington State Department of Licensing (wa.gov). The goal is to uncover insights that can inform future transportation policies, urban planning, and environmental impact assessments

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Electric Vehicle Population Data Analysis
This project analyzes electric vehicle (EV) registration data to uncover trends and insights about EV adoption across different regions and vehicle types. The analysis is performed using Python, leveraging libraries such as pandas, seaborn, and matplotlib for data manipulation and visualization.

Key Objectives & Visualizations
Top Cities and ZIP Codes by EV Count
Identifies and visualizes the top 10 cities and ZIP codes with the highest number of registered electric vehicles.
Most Popular EV Makes
Shows the top 10 EV manufacturers using a pie chart to illustrate their market share.
EV Growth Over Model Years
Analyzes the trend of EV registrations by model year, highlighting growth patterns over time.
City vs. EV Type Distribution
Compares the distribution of different types of electric vehicles (e.g., BEV, PHEV) within the top 10 cities.
Top Cities and Counties by EV Registrations
Visualizes the leading cities and counties in terms of total EV registrations.
Policy Eligibility Influence
Explores the relationship between a vehicle’s model year and its eligibility for Clean Alternative Fuel Vehicle (CAFV) policies using boxplots.
EV Type Distribution in Top Cities
Presents a stacked bar chart to show how various EV types are distributed among the top 10 cities.

How It Works
The script loads the EV population dataset from a CSV file.
It performs data cleaning and transformation as needed.
For each analysis objective, it computes relevant statistics and generates clear, informative plots.
The visualizations help stakeholders and policymakers understand where EV adoption is strongest, which types and makes are most popular, and how policies may influence new EV purchases.

Requirements
Python
pandas
matplotlib
seaborn

Usage
Place your dataset (CSV file) in the specified path.

Run the script in your Python environment.

View the generated plots for insights into EV adoption.

Data-Science-Project

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