🚦 Traffic Pattern Analysis

📋 Project Overview

This project analyzes vehicle traffic data collected hourly over 7 days to identify peak traffic periods, detect anomalies, and predict future congestion. Using visualizations like heatmaps, line plots, and bar charts along with linear regression modeling, it provides actionable insights for smart city traffic management and planning.

✨ Features

• 📈 Visualize average vehicle counts per hour across days
• 🔥 Heatmap showing traffic density by day and hour
• ⚠️ Detect sudden traffic anomalies (spikes or drops)
• 🤖 Linear regression to predict traffic volume at different hours
• 📊 Trend analysis of daily average traffic patterns
• 🚦 Identify top 3 peak traffic hours

📂 Dataset

• File: traffic_data_7days.csv
• Columns:
  • Day (1 to 7) — Day of the week
  • Hour (0 to 23) — Hour of the day
  • Vehicle_Count — Number of vehicles recorded per hour

💻 Dependencies

• Python 3.x
• pandas
• matplotlib
• seaborn
• scikit-learn

🤝 About

Developed by Aarohi Singh
RISE Internship Program – Tamizhan Skills

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This project aims to contribute towards smart and efficient city traffic management using data-driven analysis and machine learning.