Overview / Usage

Abstract: Predicting accurate shipping costs is a crucial aspect of logistics management, impacting both businesses and customers. Traditional methods of estimating shipping costs often fall short, especially when faced with the complexities of diverse shipment details, geographical variances, and fluctuating economic factors. This research article presents PriceAI, an advanced AI-driven solution designed to predict and forecast shipping costs using machine learning techniques. Leveraging Intel ONE API and Intel OPENVINO, this comprehensive framework integrates real-world datasets, accounting for multiple parameters to deliver precise cost predictions. The solution also includes a user-friendly interface and API integrations for enterprise use. This paper explores the methodology, tools, and processes that make PriceAI a scalable and effective solution for shipping cost prediction.

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Introduction

The logistics industry is constantly evolving, with businesses requiring efficient, accurate, and real-time predictions for shipping costs to ensure cost-effectiveness and customer satisfaction. Shipping costs are influenced by various factors such as shipment weight, distance, fuel prices, geopolitical factors, and seasonal demand. Traditional methods often rely on static models that fail to incorporate dynamic, real-world data. The need for a robust AI-based model has become paramount in delivering more accurate shipping cost predictions.

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In this research, we introduce PriceAI, a machine learning model designed to predict shipping costs with high accuracy. Using Intel ONE API and Intel OPENVINO, PriceAI leverages the computational power and efficiency of Intel's AI suite to create a highly optimized and scalable model.

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Problem Definition

Shipping cost estimation is a complex problem affected by:

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Shipment Details: Weight, volume, dimensions, and type of goods.

Geographical Data: Origin and destination coordinates, transportation routes, and cross-border factors.

Economic Factors: Fuel prices, taxes, tariffs, inflation, and global economic trends.

The challenge lies in dynamically integrating these parameters into a predictive model that not only provides accurate estimates but can also be adapted to different markets and real-time data.

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Objective

The primary objective of this research is to develop PriceAI, a comprehensive machine learning model capable of:

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Predicting Shipping Costs: Based on historical and real-time data for various domestic shipping scenarios.

Forecasting Shipping Trends: Using economic indicators and shipment patterns.

Scalable Framework: Allowing for easy customization and adaptation across different regions and markets.

Interactive User Interface: Offering a platform where users can input shipping details and receive cost predictions.

API Integration: Allowing enterprises to incorporate PriceAI's model into their existing logistics and supply chain solutions.

Tools and Technologies

1. Intel ONE API

Intel ONE API provides a unified programming model that simplifies the development of high-performance applications across different computing architectures, including CPUs and GPUs. This toolkit ensures that AI models like PriceAI can be optimized for maximum performance, accelerating computation-intensive tasks such as training and prediction.

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Key Features:

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Cross-Architecture Support: Enables seamless deployment across diverse hardware environments.

Optimized Libraries: Supports advanced data analysis, matrix manipulation, and AI algorithm acceleration.

1. Intel OPENVINO

Intel OPENVINO (Open Visual Inference and Neural Network Optimization) toolkit enhances deep learning model inference. It allows for faster execution of neural networks, making PriceAI’s predictions more efficient, particularly in environments with low latency requirements.

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Key Features:

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Inference Optimization: Boosts the performance of deep learning models on Intel hardware.

Edge Deployment: Suitable for edge-based computing in logistics centers and warehouses.

Parallelization: Accelerates model performance by running in parallel on multiple cores.

Solution: PriceAI

PriceAI is an AI model framework designed to predict shipping costs, leveraging real-world data sets and multiple quantitative variables. The model uses supervised learning algorithms to predict costs, with an emphasis on regression models for accurate pricing.

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Key Features of PriceAI:

Data-Driven Model: PriceAI is trained on large-scale datasets that include shipment history, geographical routes, and market conditions.

Dynamic Variables: Incorporates factors like shipment size, distance, fuel cost, and economic trends.

Interactive User Interface: A dashboard where users can input shipment parameters and obtain real-time cost estimates.

APIs for Enterprise Integration: Enables enterprises to integrate PriceAI's predictions into their logistics systems for seamless operation.

Scalability: Designed to handle diverse markets and geographical regions with ease, making it applicable for both domestic and international shipping.

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Results and Performance

In tests, PriceAI has demonstrated high accuracy in predicting domestic shipping costs, outperforming traditional methods. When integrated with real-time data, the model dynamically adjusts predictions based on current fuel prices, economic trends, and geographical factors, leading to more accurate and reliable cost estimates.

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Performance Metrics:

Mean Absolute Error (MAE): Achieved low MAE in cost prediction compared to traditional models.

Latency: OPENVINO optimized inferences for sub-second prediction times.

Scalability: Demonstrated the ability to scale across different geographies and markets without significant re-training.

Conclusion

PriceAI is a robust and scalable AI solution for predicting shipping costs, capable of handling real-world data complexities. By leveraging Intel ONE API for optimization and Intel OPENVINO for inference acceleration, PriceAI sets a new standard for efficiency and accuracy in logistics. This comprehensive solution provides not only a predictive model but also an interactive user interface and enterprise-ready APIs, making it a valuable tool for modern logistics.

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Future Work

Further research will focus on expanding PriceAI to handle international shipments, incorporating more economic indicators, and enhancing the user interface for better user experience. Additionally, ongoing work will aim to integrate more real-time data sources to improve prediction accuracy and enable better forecasting capabilities.

Methodology / Approach

Methodology

1. Data Collection

PriceAI requires a comprehensive dataset comprising:

Shipment details: Weight, dimensions, type of goods, origin, and destination.

Geographical information: Routes, zones, and traffic data.

Economic indicators: Fuel prices, tariffs, taxes, inflation rates.

Historical data: Past shipping costs and trends.

Real-world datasets from various logistics companies and public datasets (e.g., government or economic databases) are used to train the model.

1. Model Selection

Using regression techniques, PriceAI builds multiple models, selecting the best fit based on cross-validation and accuracy metrics. Common models used include:

Linear Regression: For basic cost estimation.

Random Forest: For handling non-linear relationships in the data.

Gradient Boosting: To enhance model accuracy by learning from prediction errors iteratively.

1. Training and Optimization

Intel ONE API and Intel OPENVINO are used to optimize the training process and ensure the model is highly efficient, reducing latency during prediction and allowing real-time analysis in production environments.

1. Deployment

PriceAI is deployed on cloud and edge devices, utilizing OPENVINO’s inference capabilities for low-latency predictions. The model is accessible via an interactive interface and APIs that enable real-time integration with logistics systems.

Technologies Used

• pythonanywhere
• MySQL
• sqlalchemy
• Python
• Flask
• pandas
• numpy
• Xgboost
• JavaScript
• Bash
• Jupyter
• Machine Learning
• artificial intelligence

Documents and Presentations

Repository

https://github.com/Apollo9999/AI-MODEL-for-Price-Prediction/tree/main