Project Title: Electric Vehicle Data Analysis with Plotly
Introduction: This project is a data analysis and visualization endeavor focused on exploring electric vehicle (EV) data using the Plotly library. The goal of the project is to gain insights into EV adoption, popular EV makes, electric range, and the distribution of EV vehicles across different locations.
Project Description: In this project, I analyze a dataset containing information about various electric vehicles.I utilize Python and its powerful data manipulation library, Pandas, to clean and preprocess the data. The data is then visualized using Plotly and its expressive visualization library, Plotly Express, to create interactive and insightful visualizations.
Data Source: The dataset used in this project is sourced from Hackthon team . It includes details about EV models, makes, electric range, pricing, and location-based information.
Features:
- Exploratory Data Analysis: We conduct univariate and bivariate analysis to understand the distribution and relationships between different variables in the dataset.
- Choropleth: A choropleth map is used to display the number of EV vehicles based on location, allowing us to identify regions with higher EV adoption.
- Racing Bar Plot: We create a racing bar plot to visualize the evolution of EV makes and their counts each year, providing insights into changing trends in the EV market.
EDA - Insights: During our exploratory data analysis, we identified several key insights:
- The trend of EV adoption has shown significant growth in recent years, particularly between 2010 and 2020.
- Tesla emerged as the most popular EV manufacturer, dominating the market in terms of the number of electric vehicles produced.
- Battery Electric Vehicles (BEV) tend to have a higher electric range compared to other electric vehicle types.
Dependencies: To run this project, you will need Python 3.x and the following Python packages:
- pandas
- plotly & plotly.express