Table of Contents

1. Introduction

2. Project Overview

3. Project Structure

4. Step-by-Step Implementation

• 1. Data Exploration and Preprocessing

• 2. Feature Engineering

• 3. Model Training and Evaluation

• 4. Building the Streamlit Web Application

• 5. Running the Application

• 6. Deployment Considerations

5. Conclusion

6. Future Enhancement

7. References

1. Introduction

The rise of online food delivery platforms has revolutionized the way we enjoy our meals, bringing convenience and a wide variety of choices to our fingertips. However, one challenge that persists is the accuracy of delivery time predictions. Accurate predictions are crucial for both customer satisfaction and operational efficiency. This blog post delves into a machine learning project designed to predict food delivery times, providing a detailed overview of the project’s structure, methodology, and implementation.

2. Project Overview

The core objective of this project is to develop a machine learning model that predicts the delivery time of food orders based on various features. These features might include the restaurant’s location, delivery distance, weather conditions, traffic data, and more. The model is trained on historical data and is deployed as a web application, where users can input relevant details and receive an estimated delivery time.

3. Project Structure

The project is organized into several key files, each serving a distinct purpose:

• app.py: This is the main entry point of the project, hosting the Streamlit web application. The Streamlit app allows users to input delivery details and receive predictions. It handles HTTP requests and responses, integrating the trained machine learning model to provide real-time predictions.

• functions.py: This file contains a collection of utility functions used throughout the project. These functions are responsible for data preprocessing, feature engineering, and the prediction process. The modular approach in this file ensures that the code is reusable and maintainable.

• Food-Delivery-Predicting.ipynb: This Jupyter notebook is the heart of the data science process in the project. It contains the entire workflow of the project, from data exploration and cleaning to model training and evaluation. The notebook format allows for an interactive approach to model development, making it easier to visualize data and understand the model's performance.

• Dataset : The dataset used for training the machine learning model. It includes various features that potentially influence delivery time, such as the distance between the restaurant and the delivery address, weather conditions, order time, and more.

4. Step-by-Step Implementation

Let’s walk through the key steps involved in developing the food delivery time prediction model.

1. Data Exploration and Preprocessing

Data exploration is the first step in any machine learning project. The dataset (train.csv) is loaded into a Pandas DataFrame, and the initial analysis is conducted to understand the data's structure. This step includes checking for missing values, identifying outliers, and understanding the distribution of various features.

Next, data preprocessing is performed. This involves cleaning the data, handling missing values, and transforming categorical variables into numerical ones through techniques like one-hot encoding. Feature scaling is also applied to ensure that all features contribute equally to the model’s predictions.

2. Feature Engineering

Feature engineering is the process of creating new features from existing ones to improve the model’s performance. In this project, several new features were engineered, such as:

• Distance Categories: Categorizing delivery distances into bins to help the model better understand short vs. long deliveries.

• Time of Day: Creating features that capture the time of day, such as morning, afternoon, evening, and night, to account for variations in traffic and restaurant operation speeds.

• Weather Conditions: Incorporating weather data, which can significantly impact delivery times due to factors like rain or extreme temperatures.

These features were carefully selected and transformed based on domain knowledge and exploratory data analysis (EDA) results.

3. Model Training and Evaluation

With the data prepared, the next step is model training. Several machine learning models were considered, including linear regression, decision trees, and gradient boosting algorithms. After comparing their performance using cross-validation and metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), the best-performing model was selected.

Hyperparameter tuning was conducted to optimize the model’s performance further. This process involves adjusting the model’s parameters to find the best combination that minimizes the error on unseen data.

4. Building the Streamlit Web Application

Once the model was trained and evaluated, it was integrated into a streamlit web application (app.py). Streamlit is a lightweight web framework in Python that allows for rapid development of web applications. In this project, Streamlit was used to create an interface where users can input delivery details and receive a predicted delivery time.

The application flow in app.py is straightforward:

1. User Input: The user provides input through a web form, including details like restaurant location, delivery distance, and weather conditions.

2. Prediction: The input data is passed to the prediction function, which preprocesses the input and feeds it into the trained model.

3. Output: The predicted delivery time is displayed to the user on the web page.

The Streamlit application is designed to be user-friendly and responsive, providing real-time predictions to enhance the user experience.

5. Running the Application

To run the application locally, users need to set up their environment by installing the required dependencies. This can be done using pip and the requirements.txt file, which lists all necessary Python packages.

Once the environment is set up, the application can be started by running app.py. The Streamlit server will start, and users can access the application via their web browser at http://127.0.0.1:5000/.

6. Deployment Considerations

While this project currently runs locally, the next logical step would be to deploy it to a cloud platform like AWS, Heroku, or Google Cloud. Deployment would make the application accessible to a broader audience, enabling real-time predictions for actual delivery operations.

5. Conclusion

Predicting food delivery times with machine learning is a practical application of data science that can have a significant impact on the food delivery industry. By accurately predicting delivery times, businesses can improve customer satisfaction, optimize delivery logistics, and reduce operational costs.

This project showcases the end-to-end process of developing a machine learning model, from data exploration and feature engineering to model training and deployment. By following the outlined steps, you can create a robust predictive model and integrate it into a web application, providing valuable insights and enhancing the user experience.

6. Future Enhancements

There are several areas where this project can be expanded or improved:

• Incorporating Real-Time Data: Integrating real-time traffic and weather data can improve the accuracy of predictions.

• Model Improvement: Exploring more advanced models, such as deep learning, or using ensemble methods could enhance performance.

• Scalability: Deploying the model on a scalable cloud platform would allow for handling a large volume of requests, making the solution viable for commercial use.

By continuing to iterate on this project, you can build a powerful tool that not only predicts delivery times but also drives business growth and customer satisfaction.

This technical blog provides an in-depth look at the process and considerations involved in predicting food delivery times using machine learning. It aims to guide developers, data scientists, and enthusiasts through the journey of creating a similar project, from data exploration to deployment.

7. References

1. My github repo

2. Model referred