what is machine-learning-late-delivery-risk-prediction fr?

jacqlinecpe/machine-learning-late-delivery-risk-prediction — explained in plain English

Analysis updated 2026-07-25

★ 0Jupyter NotebookAudience · dataComplexity · 2/5Setup · easy

vibe map

mindmap
  root((repo))
    What it does
      Predicts late deliveries
      Early warning system
      Reduces delivery costs
    Tech stack
      Python
      Jupyter Notebook
      Pandas and NumPy
      Scikit-learn
    Models
      Logistic Regression
      K-Nearest Neighbors
      Decision Tree
    Evaluation
      ROC-AUC metric
      Cross-validation
      Hyperparameter tuning
    Use cases
      Logistics optimization
      Risk mitigation
    Audience
      Data scientists
      Supply chain teams

mindmap root((repo)) What it does Predicts late deliveries Early warning system Reduces delivery costs Tech stack Python Jupyter Notebook Pandas and NumPy Scikit-learn Models Logistic Regression K-Nearest Neighbors Decision Tree Evaluation ROC-AUC metric Cross-validation Hyperparameter tuning Use cases Logistics optimization Risk mitigation Audience Data scientists Supply chain teams

what do people make with this?

VIBE 1

Predict which orders are at risk of arriving late before shipping them out.

VIBE 2

Compare multiple machine learning models to find the most accurate delivery risk predictor.

VIBE 3

Learn the standard data science workflow from data cleaning to model evaluation.

what's the stack?

PythonJupyter NotebookPandasNumPyScikit-learnMatplotlib

how it stacks up fr

	jacqlinecpe/machine-learning-late-delivery-risk-prediction	agostynah/distributed-vector-memory-routing	akashsingh3031/python-libraries
Stars	0	0	—
Language	Jupyter Notebook	Jupyter Notebook	Jupyter Notebook
Last pushed	—	—	2020-12-03
Maintenance	—	—	Dormant
Setup difficulty	easy	moderate	easy
Complexity	2/5	3/5	1/5
Audience	data	researcher	vibe coder

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · easy time til it works · 30min

Requires downloading the public DataCo Supply Chain Dataset and having a Python environment with common data science libraries installed.

The explanation does not mention a license, so it is unclear how this code can be used or shared.

in plain english

This project builds a machine learning model that predicts whether a delivery will arrive late, before the shipment even leaves the warehouse. The goal is to give logistics teams an early warning so they can take action on high-risk orders and reduce delays. Late deliveries hurt customer satisfaction and increase costs, so catching problems early has real business value. The project uses a public dataset called the DataCo Supply Chain Dataset. It walks through a standard machine learning process: loading the data, cleaning it, filling in missing values, converting categories into numbers a model can understand, and scaling numerical values. The data is then split into training and test sets so the model can learn on one portion and be evaluated on another. Three different models are built and compared: Logistic Regression, K-Nearest Neighbors, and a Decision Tree. Each model is judged using a metric called ROC-AUC, which measures how well the model can tell the difference between a late delivery and an on-time one. A score of 0.5 means the model is guessing randomly, while a score of 1.0 means it is perfect. The project also tunes the Logistic Regression model by testing different settings to find the best configuration. Logistic Regression achieved an ROC-AUC score of about 0.73 before tuning, which the project describes as a good ability to distinguish between late and on-time deliveries. After comparing all three models using cross-validation, a final model is selected. The README does not specify which model won the final comparison, so the best overall performer is not named. The project is written in Python using Jupyter Notebook. It relies on common data science tools including Pandas, NumPy, Scikit-learn, and Matplotlib. The README is straightforward and covers the problem, the data, the workflow, and the results, though it is light on implementation details.

prompts (copy fr)

prompt 1

Help me run the Jupyter Notebook for the late delivery risk prediction project step by step and explain what each cell does.

prompt 2

Write Python code to load the DataCo Supply Chain Dataset and prepare it for training a Logistic Regression model to predict late deliveries.

prompt 3

Create a Python script that compares Logistic Regression, K-Nearest Neighbors, and Decision Tree models using ROC-AUC and cross-validation on a delivery dataset.

prompt 4

Explain how to tune a Logistic Regression model using Scikit-learn to improve its ROC-AUC score for predicting late deliveries.

Frequently asked questions