doordash-delivery-predictor

almador2002/doordash-delivery-predictor

doordash-delivery-predictor is a tabular classification model from almador2002. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

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Updated May 10, 2026

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At a glance

Task: Tabular Classification
License: mit
Access: Public
Created: May 2, 2026
Updated: May 10, 2026
SHA: e6bbfda6

Model card

From the Hugging Face model README

<div align="center"> <video width="100%" controls autoplay loop muted> <source src="https://huggingface.co/almador2002/doordash-delivery-predictor/resolve/main/d08aecd4dc994e8b9fae392148fa0c6d.mp4" type="video/mp4"> Your browser does not support the video tag. </video> </div>

🚀 DoorDash Delivery Time Predictor

📋 Project Overview

This project analyzes and predicts DoorDash delivery times using historical order data. We tackle the problem as both a regression task (predict exact duration) and a classification task (predict Fast / Normal / Slow delivery).

📦 Dataset

Property	Value
Source	Kaggle — DoorDash Historical Delivery Data
Rows	197,428 orders
Features	16 columns (numeric + categorical)
Target	`delivery_duration_seconds`

Key Features:

total_onshift_dashers — how many dashers are available
total_busy_dashers — how many dashers are currently occupied
total_outstanding_orders — current order backlog
store_primary_category — type of restaurant
estimated_store_to_consumer_driving_duration — estimated driving time

EDA

🔍 Research Question

Can we predict how long a DoorDash delivery will take based on order details, restaurant category, and real-time dasher availability?

⚙️ Feature Engineering

We created 9 new features from the raw data:

dasher_util_ratio — how busy are available dashers (busy/onshift)
order_pressure — outstanding orders per available dasher
is_peak_lunch / is_peak_dinner — peak hour flags
is_weekend — weekend flag
log_subtotal — log-transformed order value
price_spread — range between cheapest and most expensive item
dasher_idle — dashers available but not working
cluster — KMeans cluster ID (5 clusters)

🤖 Models

Regression

Model	MAE	RMSE	R²
Baseline Linear Regression	11.2 min	14.7 min	0.233
Linear Regression (Engineered)	11.2 min	14.7 min	0.240
Random Forest	10.8 min	14.1 min	0.294
Gradient Boosting ✓	10.5 min	13.9 min	0.319

Classification (Fast / Normal / Slow)

Model	Macro F1
Logistic Regression	0.502
Random Forest	0.518
Gradient Boosting ✓	0.533

🏆 Winners

Regression: GradientBoostingRegressor — R² = 0.319
Classification: GradientBoostingClassifier — Macro F1 = 0.533

📁 Repository Files

File	Description
`regression_model.pkl`	Trained GradientBoostingRegressor + scaler + features
`classification_model.pkl`	Trained GradientBoostingClassifier + thresholds
`notebook.ipynb`	Full analysis, EDA, training and evaluation code

🏷️ Classification Strategy

Delivery times split into 3 equal quantiles:

🟢 Fast (0): under 38.1 minutes
🟡 Normal (1): 38.1 – 51.4 minutes
🔴 Slow (2): over 51.4 minutes

🛠️ Tech Stack

Task: Tabular Classification
License: mit
Languages: en
Created: May 2, 2026
Updated: May 10, 2026