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https://github.com/mayankyadav23/shipment-pricing-prediction

Shipment Pricing Prediction 📦🔍 is a machine learning project that forecasts shipment prices based on various supply chain factors. Using advanced regression models, it provides valuable insights 📊 to optimize pricing strategies in the supply chain analytics domain.
https://github.com/mayankyadav23/shipment-pricing-prediction

data-visulization flask ineuron-ai machine-learning python scikit-learn shipment-and-pricing

Last synced: 7 days ago
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Shipment Pricing Prediction 📦🔍 is a machine learning project that forecasts shipment prices based on various supply chain factors. Using advanced regression models, it provides valuable insights 📊 to optimize pricing strategies in the supply chain analytics domain.

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README 

        
# Shipment Pricing Prediction



## 📦 Project Overview

The **Shipment Pricing Prediction** project aims to predict shipment prices based on various factors in the supply chain domain using machine learning techniques. This project addresses the growing need for accurate predictions in the rapidly evolving supply chain analytics market.



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## 🚀 Technologies Used

- **Machine Learning**: Various regression models

- **Python**: Programming language for data analysis and modeling

- **Libraries**: 

  - Pandas

  - NumPy

  - Scikit-learn

  - Matplotlib

  - Seaborn

  - Flask



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## 🔍 Problem Statement

The supply chain analytics market is projected to grow significantly, with organizations needing to optimize pricing strategies. This project focuses on predicting shipment pricing using available data to help supply chain leaders make informed decisions.



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## 🔄 Architecture & Process Flow

### Process Flow

![Screenshot 2024-10-21 192106](https://github.com/user-attachments/assets/4769bfdb-4777-4845-833b-b0405f3447eb)

 



### Data Validation and Transformation

- **Missing Values**: All missing values were replaced with the mode (most frequent value).

- **Numerical Columns**: Standardized to prevent data leakage using pipelines.

- **Categorical Columns**: Encoded using either label encoding or one-hot encoding.



### Model Training

![Screenshot 2024-10-21 192043](https://github.com/user-attachments/assets/b38f85b8-5d8c-4688-b1ef-20f8042852a3)

  

- Accumulated data was exported to Python and read using Pandas.

- Performed exploratory data analysis (EDA) to identify distributions, outliers, and trends.

- Checked for null values; if present, they were imputed.

- Encoded categorical values into numeric values and scaled numerical features using StandardScaler.

- New features were created to enhance model building based on business insights.



### Prediction

- Optimized the model for accuracy with a training R-squared of 0.998273 and a test R-squared of 0.991598.

- Key features: `Days to Process`, `Line Item Insurance`, `Shipment Mode`, `Freight Cost`.



### Project Output Result

![Screenshot 2024-10-21 192322](https://github.com/user-attachments/assets/5eef75a0-e9b0-4001-b154-20d5e9dddf64)



- Training R²: 0.998273

- Test R²: 0.991598

- Important features identified: `Days to Process`, `Line Item Insurance`, `Shipment Mode`, `Freight Cost`.



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## 📊 Dataset

- **Dataset Link**: [Click here!](https://www.kaggle.com/datasets/divyeshardeshana/supply-chain-shipment-pricing-data)



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## 🛠️ Installation

To run this project, you will need Python and the required libraries. Set up a virtual environment and install dependencies using pip:



```bash

pip install pandas numpy scikit-learn matplotlib seaborn flask