Smart-Delivery-Optimization-Platform

🚚 Smart Delivery Optimization Platform

A full-stack logistics analytics solution designed to forecast delivery delays, optimize routes, and enhance operational efficiency across regions, modes, and weather conditions. This project analyzes 200,000+ delivery records to empower supply chain teams with predictive insights and interactive dashboards.

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🚗 GitHub Project Repository

🔗 Click to view Smart-Delivery-Optimization-Platform

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🧠 Project Overview

Delivery reliability is critical for customer satisfaction and cost control. This project delivers an end-to-end analytics platform that enables:

• 📊 Delay prediction across regions and delivery modes
• 🌦️ Weather impact analysis on delivery performance
• 🚚 Route optimization and time deviation tracking
• 📈 Executive dashboards for logistics planning

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🎯 Key Objectives

• Clean and preprocess delivery data
• Engineer features for delay modeling and dashboarding
• Build classification models to predict delivery status
• Deploy interactive dashboards for stakeholder decision-making

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📁 Project Structure

File Name	Description
delivery_data.csv	Raw dataset with 200K+ delivery records
cleaned_delivery_data.csv	Preprocessed dataset with engineered features
delivery_delay_model.pkl	Trained model for predicting delivery delays
smart_delivery.sql	SQL queries for delivery filtering and aggregation
sqlconnect.py	Python script for SQL database connection
app.py	Streamlit app for model testing and backend logic
smart_delivery.ipynb	Jupyter notebook with EDA, modeling, and insights
Smart Delivery Optimization Platform dashboard.pbix	Power BI dashboard visualizing delivery KPIs and trends

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🧹 Data Preprocessing

• Converted start_time and end_time to datetime format
• Calculated delivery duration and delay metrics
• Encoded categorical features (region, delivery_mode, weather, status)
• Removed duplicates and ensured type consistency
• Normalized distance_km and estimated_time_min for modeling

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📈 Exploratory Data Analysis

• 📍 Delay trends by region and delivery mode
• 🌦️ Weather impact on delivery performance
• ⏱️ Distance vs. time deviation analysis
• 📊 Status distribution across delivery types
• 📅 Hourly and daily delivery patterns

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🤖 Modeling Approach

• Target Variable: status (Delivered, Delayed, Failed)
• Algorithms Used: Random Forest, XGBoost, Logistic Regression
• Evaluation Metrics: Accuracy, F1 Score, Confusion Matrix
• Top Features: distance_km, estimated_time_min, weather, region

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📊 Dashboard Overview

🔷 Power BI Dashboard

Visualizes delivery performance and predictive insights:

• 📍 Regional delivery metrics
• 🚚 Mode-wise delay analysis
• 🌦️ Weather impact visualization
• ⏱️ Estimated vs. actual time comparison
• 📊 Status breakdown and forecasting

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🟢 Streamlit App

Interactive dashboard for real-time delivery insights:

• 🚚 Input delivery attributes to predict status
• 📈 Feature importance visualization
• 📍 Filters by region, mode, and weather
• 📊 Delay forecasting and performance benchmarking

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🚀 Deployment

• Model serialized with joblib as delivery_delay_model.pkl
• Dashboard deployed via Streamlit Cloud
• SQL integration for dynamic delivery querying
• Power BI dashboard built from cleaned dataset
• Git LFS used for large file management

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🧠 Business Impact

• Predicts delivery delays for proactive planning
• Improves route and mode selection
• Enhances customer satisfaction through transparency
• Reduces operational inefficiencies and cost overruns

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🛠️ Tech Stack

• Python: Pandas, NumPy, Scikit-learn
• SQL: Data extraction and filtering
• Visualization: Power BI, Matplotlib, Seaborn
• Deployment: Streamlit Cloud, GitHub, Git LFS

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📌 Future Enhancements

• Integrate real-time traffic and weather APIs
• Add geospatial mapping for route optimization
• Enable alert system for high-risk deliveries
• Expand dashboard to include fleet performance metrics

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👤 Author

Anesh Raj
🔗 GitHub Profile