🚦 Traffic Data Analysis & Prediction

📌 Introduction

This project focuses on analyzing traffic data collected over a span of two months. Using data visualization and machine learning models, the goal is to uncover traffic patterns and build predictive models to better understand situation and traffic density.

🎯 Objectives

• Perform exploratory data analysis (EDA) on traffic data.
• Visualize patterns with Matplotlib, Seaborn, and Plotly.
• Apply feature preprocessing (scaling, encoding).
• Train regression models (RandomForest Classifier) to predict traffic values.
• Evaluate model performance with accuracy_score metrics.

📂 Dataset

• File Used: TrafficTwoMonth.csv
• The dataset contains traffic-related information including:
  • Hourly data
  • Vehicle counts (car, bus, truck, bike and total)
  • Traffic situation categories
  • Other feature like Time of that particular date is present.

🛠️ Tools & Libraries

• Python
• Pandas & NumPy → Data manipulation & analysis
• Matplotlib & Seaborn → Normal visualizations
• Plotly → Interactive visualizations
• Scikit-learn → Data preprocessing, RandomForest Classifier, pipelines , ColumnTransformer

🔎 Exploratory Data Analysis

• Initial data inspection (head(), info(), describe())
• Traffic trend analysis across different hours and dates
• Distribution plots for vehicle counts
• Interactive plots for comparative analysis

📊 Visualizations

• Traffic patterns per weekend
• Count comparisons between different vehicle types per Hour
• Interactive hist plot for vehicles per hour for week days
• Line plot for average vehicle per hour
• Visualizations combining total trafffic by category
• Line plot for average vehicle accordance with the date of the month

🤖 Machine Learning Models

• Preprocessing:

  • Standard scaling
  • One-hot encoding for categorical features

• Models Used:

  • Random Forest Classifier

• Evaluation:

  • Accuracy_score
  • Classification_report

📈 Results

• RandomForest Classifier models provide an estimation of traffic levels.
• Visualization highlights peak hours and traffic conditions.