ddhruv-iot / ev-hackathon

Welcome to my data analysis project on Electric Vehicles (EVs)! 🚗💨 In this project, I performed an in-depth exploration of a comprehensive dataset related to EVs. I aimed to gain insights into the EV landscape, understand their distribution across various parameters, and uncover intriguing trends within the industry.

Exploring Electric Vehicles - Data Analysis Project

Overview

Welcome to my data analysis project on Electric Vehicles (EVs)! 🚗💨 In this project, I performed an in-depth exploration of a comprehensive dataset related to EVs. I aimed to gain insights into the EV landscape, understand their distribution across various parameters, and uncover intriguing trends within the industry. The project was carried out using Python programming language, and I utilized various libraries, including Pandas and Plotly, for data manipulation and visualization.

Dataset Information

The dataset used for this analysis comprises detailed information about Electric Vehicles, including their make, model, year, electric range, pricing, and more. The dataset was provided by Innomatics Labs.

Exploratory Data Analysis (EDA)

The Exploratory Data Analysis phase involved a comprehensive study of the dataset. I performed various univariate, bivariate, and multivariate analyses to gain insights into the distribution, correlations, and patterns present in the data. The key visualizations used during the EDA process included bar charts, line plots, scatter plots, choropleth maps, and more.

Key Findings

The analysis yielded several key findings:

• A significant rise in the adoption of Electric Vehicles over the years, with a diverse range of models from various manufacturers.
• Geographic distribution of EVs, with notable hotspots of EV popularity in specific regions.
• Positive correlation between Electric Range and Base MSRP, indicating the relationship between vehicle capabilities and pricing.
• Insights into the eligibility of Clean Alternative Fuel Vehicles and the significance of environmentally conscious transportation options.

Lessons Learned

This data analysis project provided valuable lessons:

• Enhanced proficiency in Python programming and the usage of data manipulation libraries like Pandas.
• Improved skills in data visualization using Plotly, creating insightful visualizations for data exploration.
• In-depth understanding of Electric Vehicles and their implications for sustainable transportation.
• Explored bar_chart_race module and FFMPEG for video streaming.

Project Duration

The project was completed over the course of less than a day. It encompassed data preprocessing, exploratory analysis, visualization creation, and documentation.

Conclusion

Exploring the world of Electric Vehicles through data analysis has been an exciting journey. As the EV industry continues to evolve, data-driven insights play a crucial role in shaping the future of sustainable transportation. I am thrilled to have contributed to this area and look forward to more projects that enable me to leverage data for positive change.

Acknowledgments

I want to express my gratitude to the Innomatics Labs for making this dataset available for analysis. Their efforts in collecting and maintaining this data have enabled meaningful exploration and insights.

Contact Information

For any questions or collaborations related to this project, feel free to contact me:

• Name: Ddhruv Arora
• LinkedIn: https://www.linkedin.com/in/ddhruv-arora/
• Email: [email protected]

Thank you for joining me on this data analysis journey! 🌟 Together, let's drive a sustainable future with Electric Vehicles!

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Demo/Output

Static Output file for easy understanding

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