Project Title: PDT Breach Risk Mitigation

Overview

This project focuses on analyzing the supply chain and delivery times for a hypothetical e-commerce business. The main objective is to uncover patterns in delivery performance, identify bottlenecks, and predict future delivery times based on historical data. By applying data analysis techniques and machine learning algorithms, the project aims to provide actionable insights to enhance supply chain efficiency and improve customer satisfaction.

Project Objectives

• Analyze Delivery Times: Examine the delivery times for various products to identify patterns, delays, and potential bottlenecks in the supply chain.
• Product Performance Analysis: Evaluate the performance of different products across regions and time periods to identify trends and anomalies.
• Predict Future Delivery Times: Utilize machine learning models to predict future delivery times based on historical data and influencing factors.
• Optimize Operational Efficiency: Offer data-driven recommendations to improve inventory management, logistics, and overall supply chain operations.

Methodology

1. Data Preprocessing: Clean and prepare the data by handling missing values, removing irrelevant features, and ensuring it is ready for analysis.
2. Exploratory Data Analysis (EDA): Use visualizations to uncover trends in delivery times, analyze regional performance, and detect patterns or outliers.
3. Feature Engineering: Develop new features and transform existing ones to enhance model accuracy and robustness.
4. Model Building: Implement various machine learning models, including Neural Networks, Random Forest, and Support Vector Machines (SVM), to predict delivery times and identify factors contributing to delays.
5. Model Evaluation: Assess and compare model performance using metrics like recall, F1-score, and ROC-AUC to determine the best model for the task.

Why Use Neural Networks and Other Algorithms?

• Neural Networks: Ideal for capturing complex, non-linear relationships between factors such as location, order time, product type, and shipping mode. Neural networks are particularly useful when dealing with datasets containing numerous variables and intricate interactions, making them suitable for predicting delivery times in a dynamic e-commerce environment.
• Random Forest: An ensemble method that provides robust predictions and helps identify the most influential factors in delivery performance, such as product category, order date, and delivery region.
• Support Vector Machines (SVM): Effective in handling high-dimensional data and finding optimal decision boundaries. SVMs are useful for classifying delivery times into categories like on-time, delayed, or significantly delayed.
• Other Algorithms: Techniques such as logistic regression, decision trees, and gradient boosting were also explored to provide a comprehensive analysis and validate results across multiple models.

Key Findings

• Identified significant factors influencing delivery times, including product type, region, and order volume.
• Achieved high accuracy in predicting delivery times using a combination of machine learning models.
• Provided recommendations for optimizing inventory levels and adjusting logistics strategies to reduce delivery delays.

Requirements

To run this project, you will need the following libraries:

• Python 3.x
• Jupyter Notebook
• NumPy
• pandas
• matplotlib
• seaborn
• plotly
• scikit-learn
• TensorFlow
• statsmodels

How to Use

1. Clone or Download the Repository: Clone or download the repository to your local machine.
2. Open the Notebook: Open the main.ipynb notebook using Jupyter Notebook or JupyterLab.
3. Run the Notebook: Execute the cells step-by-step to preprocess data, visualize supply chain performance, and build predictive models:
   • Data Preprocessing: Handle missing data and prepare it for analysis.
   • Visualization: Use visual tools to explore and understand delivery patterns.
   • Model Building: Train multiple machine learning models to predict delivery times.
   • Evaluation: Evaluate and compare the performance of different models to select the best approach.