Predict the estimated food delivery time using real-world features such as traffic, weather, rider experience, and more — powered by Deep Learning!
This project aims to solve the real-world problem of food delivery time estimation using a deep learning model built with Keras. By analyzing various features (traffic conditions, weather, time of day, etc.), the model predicts how long it will take for food to be delivered.
This project aims to predict food delivery time using a regression-based deep learning model. The goal is to estimate how long it will take for a delivery to be completed based on multiple real-world factors like traffic, weather, and rider experience.
- 🚗 Vehicle Type
- 🌦️ Weather Conditions
- 🚦 Traffic Conditions
- ⏱️ Preparation Time
- 📍 Delivery Distance
- 🧍 Rider Experience
- 🕓 Order Time
- 🧾 Multiple Categorical + Numerical Features
- Framework: Keras (TensorFlow backend)
- Model Type: Deep Neural Network (Regression)
- Activation Functions:
- Hidden Layers:
ReLU - Output Layer:
Linear
- Hidden Layers:
- Loss Function:
Mean Squared Error (MSE) - Optimizer:
Adam
A simple feedforward neural network was used with multiple dense layers to capture feature relationships and provide accurate delivery time estimates.