🌧 Weather-Based Delivery Optimization System

🔗 Live Demo: https://weather-delivery-optimization-system.streamlit.app/

Developed a data-driven decision system to quantify how rainfall impacts delivery demand and delays, identifying 40% increase in demand and 75% rise in delays during high rainfall conditions.

Analyzed 50,000+ weather and delivery records to quantify the impact of rainfall on logistics performance.

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📊 Problem Statement

Logistics and delivery companies face significant operational challenges during adverse weather conditions, especially rainfall.

Unexpected spikes in demand combined with delivery delays can lead to:

• Poor customer experience
• Operational inefficiencies
• Increased costs

This project aims to analyze weather patterns and quantify their impact on delivery performance to support better decision-making.

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⚙️ Solution Approach

• Collected and processed weather and delivery datasets
• Built and calibrated a Random Forest model with threshold tuning to improve prediction reliability and align outputs with business impact
• Integrated delivery data to analyze operational impact
• Identified correlations between rainfall, demand, and delivery delays
• Applied threshold tuning to improve classification performance and align predictions with business impact
• Developed a system to translate predictions into actionable insights

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🛠 Tech Stack

• Python (Pandas, NumPy, Scikit-learn)
• Machine Learning (Random Forest)
• Data Analysis & Visualization
• Streamlit (for interactive app)

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📈 Key Insights

• High rainfall conditions led to ~40% increase in order demand
• Delivery delays increased by ~75% during peak rainfall
• Demand spikes and delays occur simultaneously, stressing operations
• Weather conditions are a strong external factor affecting logistics performance

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🚀 Business Impact

• Enables delivery companies to anticipate demand surges
• Helps optimize workforce and delivery capacity planning
• Supports proactive decision-making during adverse weather
• Reduces delays and improves customer satisfaction

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🧠 System Workflow

Weather Data → ML Prediction → Demand & Delay Analysis → Business Insights → Operational Decisions

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📱 Application (Streamlit)

• Built an interactive dashboard to:
  • Input weather conditions
  • View predicted rainfall impact
  • Analyze delivery performance metrics

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📸 Application Preview

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🎯 Operational Recommendations

• Increase delivery fleet capacity during high rainfall periods to handle ~40% demand surge
• Allocate buffer time and dynamic routing to mitigate ~75% delay increase
• Prioritize high-value orders during peak weather disruptions

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📂 Project Structure

Weather-Delivery-Optimization-System/
├── app.py                     # Streamlit app (UI + predictions + insights)

├── data/                      # Datasets
│   ├── Rainfall.csv
│   └── delivery_data.csv

├── models/                    # Trained ML artifacts
│   ├── best_model_calibrated.pkl
│   ├── scaler.pkl
│   └── optimal_threshold.txt

├── notebooks/                 # Development & analysis
│   ├── weather_prediction_model_pipeline.ipynb
│   └── delivery_impact_analysis.ipynb

├── images/                    # UI assets & screenshots
│   ├── background.png
│   └── image.png

├── requirements.txt           # Dependencies
├── runtime.txt                # Python version (deployment)
├── Dockerfile                 # Containerization setup
├── .gitignore
├── LICENSE
└── README.md

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🚀 Future Improvements

• Integrate real-time weather API
• Deploy system on AWS for scalability
• Automate end-to-end data pipeline

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Author

Sujal Gupta