🛍️🌐 Predicting Delivery Times Using A Real, Commercial Brazilian E-Commerce Dataset 🇧🇷💰

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In this project, we develop a regression model to predict how long it'll take for an order to be delivered for using the Olist Ecommerce Dataset available on Kaggle.

Summary

• 👉 Original Kaggle Dataset 🔗
• 👉 Polars 🔗

For this project, we decided to push the limits of data processing.

Using the Polars library, in combination with Pandas, we were able to clean, transform, join, and reshape 8 files containing anywhere from [20 to 100K records] in less than 5-6 min.

Leveraging the Power of Z BY HP to make your data science work 🤯mindblowingly🤯 fast.

(And decreasing the amount of time staring at the screen waiting for your training job to finish.⏳)

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Instructions

1. Clone this Git repository using the following command: git clone https://github.com/MMBazel/Kaggle-Brazilian-Ecommerce-Prediction.git
2. Using the terminal or command prompt, cd into Kaggle-Brazilian-Ecommerce-Prediction
3. Check if you have Python installed on your machine by typing python --version in your terminal or command prompt window. If Python is not installed, download and install Python from the official website.
4. Create a virtual environment for the project using the following command:
   • python -m venv <YOUR_ENV_NAME>
5. Activate the virtual environment using the following command:
   • On Windows:
     • If you're using command line: <YOUR_ENV_NAME>\Scripts\activate.bat
     • If you're using PowerShell: <YOUR_ENV_NAME>\Scripts\Activate.ps1
   • On Linux/Mac: source <YOUR_ENV_NAME>/bin/activate
6. Install the required packages using pip by running the following command:
   • pip install -r requirements.txt
7. Manually (or programatically) unzip the different data sources in /data/raw/ and /data/backup/.
8. Make sure you're in the root folder. In your terminal run the command:
   • python3 src/main_script.py
9. If the program has run successfully, a streamlit dashboard should open in a browser window.
10. When you're finished, hit Ctrl+C to exit streamlit.

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Project Organization

├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── raw       <- Data needed for the pipeline. Make sure everything has been unzipped. 
│   └── backup            <- Backup files for the Streamlit dashboard. Makesure they're unzipped. 
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   │
│   └── main_script.py  <- The only script that needs to be run with `python3 src/main_script.py`.
│                          Make sure you're in the right folder. 
├── dashboard.py          <- Script for the Streamlit dashboard.

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Running The Script

📹 Video 📹

• 👉 Watch my screenshare of the Streamlit dashboard running here:YOUTOUBE VIDEO HERE: Brazilian Ecommerce: Running The Script - Part 1
• 👉 Watch my screenshare of the Streamlit dashboard running here:YOUTOUBE VIDEO HERE: Brazilian Ecommerce: Running The Script - Part 2

Screenshots

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Playing With The Dashboard

📹 Video 📹

• 👉 Watch my screenshare of the Streamlit dashboard running here:YOUTOUBE VIDEO HERE: Dashboard - Tour of Brazilian Ecommerce Prediction Streamlit Dashboard

Screenshots