This repository contains a data science portfolio project analyzing the delivery times of online shopping packages. The project uses data extracted from email notifications to uncover patterns and answer the question:
When is it safest to leave home without missing a delivery?
This project focuses on:
- Validating and cleaning the raw email data.
- Analyzing weekly and hourly delivery patterns.
- Calculating key percentiles (2.5th and 97.5th) to define safe time windows for leaving home.
- 📁 images/: Contains visualizations generated during the analysis, along with a few supporting illustrations.
- Delivery Times - Online Shopping.ipynb: The main Jupyter Notebook containing the complete analysis.
- LICENSE.txt: The project license.
- README.md: This file.
- environment.yml: A file to recreate the conda environment with all required packages.
- mercadolivre_emails.txt: Text file containing the filenames of MercadoLivre delivery notifications.
- shopee_emails.txt: Text file containing the filenames of Shopee delivery notifications.
Follow these steps to set up the environment and run the notebook:
-
Clone the Repository:
git clone https://github.com/MathRC/DeliveryTimes.git cd delivery-times-online-shopping -
Create the Conda Environment:
Ensure that Conda is installed on your system. Then run:
conda env create -f environment.yml
-
Activate the Environment:
conda activate DeliveryTimes
-
Launch Jupyter Notebook:
jupyter notebook
-
Open and Run the Notebook:
In Jupyter, open
Delivery Times - Online Shopping.ipynband execute the cells to reproduce the analysis.
- The data used in this project is based on delivery notifications extracted from emails.
- This project demonstrates practical data analysis techniques.
- For reproducibility, the environment file lists only the necessary packages:
pandas,numpy,matplotlib,seaborn, andjupyter.
This project is licensed under the MIT License. See the LICENSE file for details.
- Special thanks to the Anaconda community and the creators of the tools used in this analysis.