Electric-Vehicle-Population-Data-Analysis

Electric-Vehicle-Population-Data-Analysis

This project focuses on analyzing a real-world dataset titled "Electric Vehicle Population Data", provided by the State of Washington and hosted on Data.gov. The dataset contains detailed information about battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) registered in Washington State.

Objective

The goal of this project is to preprocess the dataset, conduct exploratory data analysis (EDA), and effectively communicate insights derived from the data. This project was completed as part of a group assignment.

Dataset Overview

Source

Electric Vehicle Population Data on Data.gov

Description

The dataset contains information on registered BEVs and PHEVs in Washington State, including:

Vehicle Identification Number (VIN)

County and city of registration

Make and model

Electric type and range

Dataset Features

Number of Features: 17

Temporal Scope: Data spans from model year 2013 to the current year, with regular updates.

Key Features of the Analysis

Data Cleaning and Feature Engineering

Missing Value Analysis

Identified missing data and documented its frequency and distribution.

Applied multiple strategies (e.g., mean/median imputation, dropping rows) and compared their impact.

Feature Encoding

Encoded categorical features (e.g., Make, Model) using one-hot encoding.

Normalization

Normalized numerical features for accurate analysis where necessary.

Exploratory Data Analysis (EDA)

Descriptive Statistics

Calculated summary statistics such as mean, median, and standard deviation for numerical features.

Spatial Distribution

Visualized the distribution of EVs across cities and counties using maps.

Model Popularity

Analyzed trends in the popularity of EV makes and models.

Correlation Analysis

Investigated relationships between numeric features and visualized results.

Visualization

Exploratory Visualizations

Created histograms, scatter plots, boxplots, and bar charts to explore feature relationships.

Comparative Visualizations

Compared the distribution of EVs across locations using bar charts and stacked bar charts.

Additional Analysis (Optional)

Temporal Analysis

Analyzed EV adoption rates and model popularity over time, if temporal data was available.