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DATA-ANALYTICS Project
Online Food Ordering Final Year Data Analytics Project Source Code
Get runnable Online Food Ordering Final Year Data Analytics 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 DATA-ANALYTICS project.
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Project's Overview
Online Food Ordering Data Analysis Project is a Python-based final year project developed for analyzing customer behavior in the online food ordering domain. This major project uses a Kaggle online food dataset containing customer demographic, income, education, family, location, feedback, and order-output details. The project performs exploratory data analysis, checks dataset shape, summary statistics, missing values, duplicates, and builds multiple visualizations to understand customer ordering patterns. It also trains machine learning models such as Random Forest and Logistic Regression to predict whether a customer will order food online again. 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, EDA, 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, database authentication, or dashboard login module is included. The project runs locally as a Python data analysis and machine learning script.
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Project Features in this Final Year Project
- Online food ordering customer behavior analysis
- Kaggle dataset support
- CSV dataset loading
- Exploratory data analysis
- Dataset shape inspection
- Dataset information summary
- Statistical summary generation
- Missing-value checking
- Duplicate record checking
- Customer demographic analysis
- Gender-wise order behavior analysis
- Marital-status-based behavior analysis
- Occupation-wise customer analysis
- Monthly-income-based order behavior comparison
- Feedback distribution analysis
- Correlation heatmap
- Machine learning classification
- Predicts whether customer will order again
- Random Forest model
- Logistic Regression model
- Model accuracy comparison
- Feature importance visualization
- Output plots saved to folder
- Command-line project execution
- Suitable for report writing and viva demonstration
Data Analytics / EDA Features in this Final Year Project
- Reads online food ordering dataset
- Supports Kaggle dataset download setup
-
Supports manual CSV placement in:
data/ - Checks total rows and columns
- Displays dataset information
- Displays summary statistics
- Checks missing values
- Checks duplicate records
- Analyzes age distribution
- Analyzes gender distribution
- Analyzes occupation distribution
- Analyzes monthly income and output relation
- Analyzes marital status and output relation
- Analyzes feedback and output relation
- Analyzes numeric feature correlations
- Supports report-ready EDA explanation
Visualization Features in this Final Year Project
The project generates visual outputs inside:
outputs/plots/
- Age Distribution
Chart type: Histogram
Shows the distribution of customer ages. - Gender vs Output
Chart type: Grouped bar chart
Compares ordering intention by gender. - Output Pie Chart
Chart type: Pie chart
Shows Yes/No distribution for whether customers will order again. - Occupation Distribution
Chart type: Horizontal bar chart
Shows customer occupation categories. - Monthly Income vs Output
Chart type: Stacked bar chart
Compares income groups with ordering intention. - Age Boxplot by Output
Chart type: Box plot
Shows age spread for customers who will or will not order again. - Feedback Distribution
Chart type: Count/distribution chart
Shows positive and negative feedback distribution. - Correlation Heatmap
Chart type: Heatmap
Shows numeric feature correlations. - Marital Status vs Output
Chart type: Grouped bar chart
Compares customer ordering behavior by marital status. - Feature Importance
Chart type: Feature ranking plot
Shows important features used by Random Forest.
Machine Learning Features in this Final Year Project
- Customer ordering behavior prediction
-
Target variable:
Output -
Predicts:
Will order again? Yes / No - Uses Random Forest classifier
- Uses Logistic Regression classifier
- Compares model accuracy
- Random Forest accuracy around 91%
- Logistic Regression accuracy around 85%
- Feature importance analysis
- Important predictors include:
- Feedback
- Age
- Family size
- Monthly Income
- Suitable for classification-based machine learning project
- Useful for explaining customer retention and online food ordering behavior
Dataset Details in this Final Year Project
Dataset source:
Kaggle - Online Food Dataset
Dataset rows:
388 records
Dataset columns:
AgeGenderMarital StatusOccupationMonthly IncomeEducational QualificationsFamily sizelatitudelongitudePin codeOutputFeedback
Target variable:
Output
Prediction objective:
Predict whether the customer will order food online again.
Manual dataset file names supported:
onlinefoods.csv
online_food.csv
Dataset folder:
data/
Files / Modules Included in this Final Year Project
online_food_ordering_analysis.py— main analysis and ML scriptrequirements.txt— Python dependency listdata/— dataset folderoutputs/plots/— generated visualization folder- Optional Kaggle configuration support using
kaggle.json
Output Files in this Final Year Project
The project generates outputs inside:
outputs/plots/
Expected outputs include:
- Age distribution graph
- Gender vs output graph
- Output pie chart
- Occupation distribution graph
- Monthly income vs output graph
- Age boxplot by output
- Feedback distribution graph
- Correlation heatmap
- Marital status vs output graph
- Feature importance graph
Console output includes:
- Dataset shape
- Dataset info
- Summary statistics
- Missing-value report
- Duplicate record report
- Model accuracy
- Machine learning result summary
-
Open terminal in the project folder:
cd "online-food-ordering-data-analysis-project" -
Install dependencies:
pip install -r requirements.txt - Optional Kaggle setup:
- Create a Kaggle account
- Download Kaggle API token
-
Place
kaggle.jsonin:~/.kaggle/ -
On Windows:
C:\Users\<username>\.kaggle\
- Alternative manual dataset setup:
- Download the dataset manually from Kaggle
-
Place CSV inside:
data/ -
Rename file as:
onlinefoods.csvor:
online_food.csv
-
Run the project:
python online_food_ordering_analysis.py -
Check generated plots:
outputs/plots/
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, database authentication, or dashboard login module is included. The project runs locally as a Python data analysis and machine learning script.