nikithakolipakula

nikithakolipakula / Traffic-Crash-Analysis

Public

Traffic crash analysis and prediction using data analysis, visualization and machine learning

14
0
100% credibility
Found Apr 27, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An interactive dashboard for analyzing traffic crash data through visualizations of patterns like speed and time impacts, plus a tool to predict injury severity.

How It Works

1
🔍 Discover the Tool

You find a handy dashboard for exploring traffic crash patterns and predictions on a sharing site.

2
💾 Get the Files

Download the project files to your computer to start using it right away.

3
🚀 Launch the Dashboard

Start the app with a simple action, and it opens in your web browser like magic.

4
🎛 Adjust Filters

Slide controls for speed range, crash times, and data amount to focus on what interests you.

5
📊 View Insights

Watch interactive charts, trend lines, heatmaps, and a map of crash locations come alive with your filters.

6
🔮 Make Predictions

Slide values for speed, lanes, and hour to instantly see predicted number of injuries.

🛡️ Understand Road Safety

You now see clear patterns in crashes, helping make roads safer with data-driven insights.

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Star Growth

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AI-Generated Review

What is Traffic-Crash-Analysis?

This Python project delivers an interactive Streamlit dashboard for traffic crash analysis using Chicago's public crash reports. Dive into traffic crash data analysis with filters for speed limits, crash hours, and sample sizes, revealing patterns like hourly trends, speed vs. injury correlations, and geographic hotspots on a map. It wraps EDA visuals—heatmaps, histograms, scatters—and a linear regression predictor for estimating injuries from speed, lanes, and time, solving quick insights for road safety without setup hassle.

Why is it gaining traction?

It stands out as a ready-to-run traffic crash analysis solution: pip install deps, streamlit run, and get github traffic insights on real data instantly—no data wrangling needed. The hook is its polished UI with live predictions and dynamic plots, beating static Jupyter notebooks for sharing traffic crash report findings. Devs grab it for fast prototypes in traffic crash simulation or urban analytics.

Who should use this?

Data analysts prototyping traffic safety dashboards, urban planners mapping high-risk zones from traffic crash reports, or ML students applying regression to public datasets like Chicago crashes. Ideal for quick traffic crash analysis with point of interest spatial clustering via maps, not heavy production pipelines.

Verdict

Skip for production—1.0% credibility score, 14 stars, and academic roots mean thin tests and docs—but grab it as a learning template for Streamlit ML apps. Fork and extend for your traffic crash data analysis needs.

(178 words)

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