arpitvashisht

arpitvashisht / Electric-Vehicle-Data-

Public

Electric Vehicle Data Analysis project using Python. Includes data cleaning, EDA, dashboard-style visualizations, and linear regression for insights.

11
1
100% credibility
Found Apr 27, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This repository provides a Python script for loading, cleaning, and visualizing electric vehicle population data with charts on manufacturers, trends, distributions, and a simple prediction model.

How It Works

1
🔍 Discover EV Trends Project

You hear about electric cars booming and find this friendly project that uncovers hidden stories from car registration data.

2
📥 Get the Files

Download the simple analysis tool and the car data file to your computer.

3
⚙️ Prepare Your Data

Place the car data where the tool expects it, like in your downloads folder.

4
🚀 Start the Magic

Run the tool once, and it cleans up the data while creating exciting pictures automatically.

5
📊 View Dashboards

Watch colorful charts pop up showing top car makers, growth over years, and range distributions.

6
📈 Spot Key Insights

Notice Tesla leads, newer cars travel farther, and electric cars surged after 2020.

✅ EV Expert Achieved

Celebrate understanding EV adoption trends, predictions, and visualizations ready to share.

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Star Growth

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AI-Generated Review

What is Electric-Vehicle-Data-?

This Python project loads an electric vehicle dataset, cleans it by handling missing values and duplicates, then runs exploratory data analysis with dashboard-style visualizations like bar charts for top manufacturers, pie charts for BEV vs PHEV splits, trend lines for EV growth, heatmaps for correlations, and boxplots for outliers. It wraps up with a linear regression model predicting electric range from model year, outputting polished plots ready for reports on electric vehicle population data. Users get instant insights into EV adoption trends, battery performance, and market leaders without building from scratch.

Why is it gaining traction?

It stands out as a complete electric vehicle data analysis project with multi-panel dashboards that combine categorical breakdowns, distributions via KDE plots, and regression visuals—perfect for quick electric vehicle data check or electric vehicle data kaggle explorations. Developers grab it for the turnkey setup using Pandas, Matplotlib, Seaborn, and Scikit-learn, skipping boilerplate to focus on electric vehicle battery trends or charging station insights from real-world electric vehicle data sets.

Who should use this?

Data science students tackling electric vehicle dataset github repos for EDA practice. EV analysts reviewing electric vehicle population data github for manufacturer dominance or growth post-2020. Market researchers needing fast viz on electric vehicle data australia-style registrations or model year vs range correlations.

Verdict

Skip for production—11 stars and 1.0% credibility score signal it's an early-stage learning tool, with solid docs but no tests or extensibility. Fork it to build your own electric vehicle data logger or advanced models; great starter for portfolio EV projects.

(198 words)

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