Food Delivery Time Prediction

This project implements multiple linear regression models to predict food delivery times based on various factors like distance, weather, traffic conditions, and courier experience.

Features

• Data Loading: Robust CSV data loading with error handling
• Data Cleaning: Missing value imputation and categorical encoding
• Multiple Models: Linear, Ridge, and Lasso regression
• Evaluation: Comprehensive metrics (RMSE, MAE, R²)
• Modular Design: Clean separation of concerns

Dataset

• Food_Delivery_Times.csv: 1000 delivery records
• Target: Delivery_Time_min (delivery time in minutes)
• Features: Distance, Weather, Traffic, Time of Day, Vehicle Type, etc.

Installation

pip install -r requirements.txt

Usage

cd src
python main.py

Project Structure

food-delivery-linear-regression/
│
├── data/
│   └── Food_Delivery_Times.csv
│
├── src/
│   ├── data_loader.py      # Data loading utilities
│   ├── data_cleaner.py     # Data preprocessing
│   ├── regression_models.py # ML models
│   └── main.py            # Training pipeline
│
├── requirements.txt
└── README.md

Results

The pipeline evaluates three regression models and selects the best performing one based on R² score.

Future Improvements

• Add hyperparameter tuning
• Include more advanced models
• Add visualization capabilities
• Deploy as web service