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MACHINE-LEARNING Project
Online Food Ordering Final Year Machine Learning Project Source Code
Get runnable Online Food Ordering Final Year Machine Learning Project Source Code with database files, project setup instructions, live demo options and installation support. This project resource helps students understand the implementation, modules, workflow and technical architecture of a complete MACHINE-LEARNING project.
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Project's Overview
Online Food Ordering Data Analysis & Machine Learning is a Python-based final year project developed for analyzing online food delivery order data using exploratory data analysis, visualization, and machine learning. This major project uses a real-world Kaggle dataset containing restaurant orders, subzones, order status, delivery distance, order items, bill subtotal, packaging charges, discounts, total amount, ratings, kitchen preparation time, rider wait time, and customer details. The notebook cleans the data, engineers useful features, creates 12 different visualization charts, and trains a Random Forest Regressor to predict kitchen preparation time in minutes. This online food ordering data analysis source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Python, data analytics, food delivery analytics, visualization, and machine learning.
Login Credentials
This project has no login credentials because it is not a web application.
Credential note:
No admin panel, user login, password, or database authentication is included. The project runs locally as a Python data analysis notebook.
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Project Features in this Final Year Project
- Online food ordering dataset analysis
- Food delivery order history analysis
- Kaggle dataset loading support
- Local CSV loading support
- Data cleaning and preprocessing
- Feature engineering
- Delivery distance parsing
- Line item count generation
- Order timestamp parsing
- Distance bucket creation
- Numeric field preparation
- Exploratory data analysis
- Summary statistics generation
- Correlation analysis
- Order total distribution analysis
- Restaurant-wise order analysis
- Subzone-wise order analysis
- Order status breakdown
- Kitchen preparation time analysis
- Rider wait time analysis
- Bill subtotal analysis
- Packaging charge analysis
- Discount and total amount analysis
- 12 visualization chart types
- Random Forest regression model
- Kitchen preparation time prediction
- Model evaluation using MAE, RMSE, and R²
- Predicted vs actual plot
- Feature importance visualization
- PNG chart export support
- Jupyter Notebook workflow
Data Analytics / EDA Features in this Final Year Project
- Dataset loading using
kagglehub -
Local fallback loading from:
data/order_history_kaggle_data.csv - Summary statistics
- Missing value inspection
- Restaurant distribution analysis
- Subzone distribution analysis
- Order status analysis
- Order amount distribution
- Distance-based order grouping
- Daily order volume analysis
- Kitchen preparation time distribution
- Rider wait time distribution
- Restaurant-wise amount comparison
- Subzone-wise KPT comparison
- Correlation matrix analysis
- Numeric feature preparation for ML
Visualization Features in this Final Year Project
The project includes 12 different visualization types for report comparison:
- Histogram — order total distribution
- Vertical bar chart — orders by subzone
- Horizontal bar chart — orders by status
- Pie chart — share by restaurant
- Box plot — kitchen preparation time by subzone
- Violin plot — total amount by restaurant
- Scatter plot — bill subtotal vs kitchen preparation time
- Heatmap — correlation matrix
- Line chart — daily order volume
- Count plot — orders by distance bucket
- KDE plot — kitchen preparation time vs rider wait distributions
- Stacked bar chart — average subtotal plus packaging by restaurant
Additional ML visualizations:
- Predicted vs actual KPT plot
- Feature importance plot
Output folder:
output/
Export format:
PNG images at 150 DPI.
Machine Learning Features in this Final Year Project
- Predicts kitchen preparation time
- Target variable: KPT duration in minutes
- Uses delivered orders with known KPT
- Random Forest Regressor model
- Feature preparation from order attributes
- Model training and testing workflow
- MAE evaluation
- RMSE evaluation
- R² score evaluation
- Predicted vs actual comparison
- Feature importance analysis
- Useful for understanding factors affecting preparation time
- Supports report-ready ML explanation
Dataset Details in this Final Year Project
Dataset name:
Food Delivery Order History Data
Dataset source:
Kaggle
Dataset author:
sujalsuthar
Dataset size:
Approximately 21,000+ order rows
Dataset focus:
Online food delivery / ordering data from multiple restaurants and subzones in Delhi NCR.
Typical fields include:
- Restaurant
- Subzone
- Order time
- Order status
- Delivery distance
- Items
- Bill subtotal
- Packaging charges
- Discounts
- Total amount
- Ratings
- Kitchen preparation time
- Rider wait time
- Customer ID
Primary CSV file:
order_history_kaggle_data.csv
Files / Modules Included in this Final Year Project
online_food_ordering_analysis.ipynb— full Jupyter Notebook analysisrequirements.txt— Python dependenciesREADME.md— project documentationdata/order_history_kaggle_data.csv— optional local dataset pathoutput/— generated PNG chart output folder
Output Files
All generated figures are exported to:
output/
Expected exported files are named from:
01_...png
to:
13_...png
The output files include EDA charts and machine learning result charts, making them useful for project reports, presentations, and viva demonstration.
Option 1: Run with automatic Kaggle download
-
Open terminal in the project folder:
cd "online-food-ordering-data-analysis-v2" -
Create virtual environment:
python -m venv .venv -
Activate virtual environment on Windows:
.\.venv\Scripts\Activate.ps1 -
Install dependencies:
pip install -r requirements.txt -
Start Jupyter Notebook:
jupyter notebook online_food_ordering_analysis.ipynb -
Or start JupyterLab:
jupyter lab online_food_ordering_analysis.ipynb -
Run all cells:
Kernel → Restart & Run All -
Check generated charts:
output/
Option 2: Run with manual CSV dataset
-
Download the CSV file:
order_history_kaggle_data.csv -
Create this folder structure:
online-food-ordering-data-analysis-v2/ data/ order_history_kaggle_data.csv online_food_ordering_analysis.ipynb requirements.txt README.md -
Open the project folder:
cd "online-food-ordering-data-analysis-v2" -
Install dependencies:
pip install -r requirements.txt -
Start notebook:
jupyter notebook online_food_ordering_analysis.ipynb - Run all cells.
-
View exported charts in:
output/
README Included
Setup Support Available
This project has no login credentials because it is not a web application.
Credential note:
No admin panel, user login, password, or database authentication is included. The project runs locally as a Python data analysis notebook.