Online Food Ordering Data Analytics Final Year Project with Source Code

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/

1. Age Distribution
   Chart type: Histogram
   Shows the distribution of customer ages.
2. Gender vs Output
   Chart type: Grouped bar chart
   Compares ordering intention by gender.
3. Output Pie Chart
   Chart type: Pie chart
   Shows Yes/No distribution for whether customers will order again.
4. Occupation Distribution
   Chart type: Horizontal bar chart
   Shows customer occupation categories.
5. Monthly Income vs Output
   Chart type: Stacked bar chart
   Compares income groups with ordering intention.
6. Age Boxplot by Output
   Chart type: Box plot
   Shows age spread for customers who will or will not order again.
7. Feedback Distribution
   Chart type: Count/distribution chart
   Shows positive and negative feedback distribution.
8. Correlation Heatmap
   Chart type: Heatmap
   Shows numeric feature correlations.
9. Marital Status vs Output
   Chart type: Grouped bar chart
   Compares customer ordering behavior by marital status.
10. 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:

• Age
• Gender
• Marital Status
• Occupation
• Monthly Income
• Educational Qualifications
• Family size
• latitude
• longitude
• Pin code
• Output
• Feedback

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 script
• requirements.txt — Python dependency list
• data/ — dataset folder
• outputs/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.json in:

    ~/.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.csv

    or:

    online_food.csv

• Run the project:

  python online_food_ordering_analysis.py

• Check generated plots:

  outputs/plots/

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.