Food Delivery Pattern Analysis & Al Recommendation System

Inspiration

Food delivery platforms generate huge amounts of customer and transaction data every day. We wanted to leverage AI and Data Science techniques to uncover hidden patterns, understand customer behavior, and provide smarter recommendations.

What it does

The platform analyzes food ordering data using multiple AI and analytics techniques including association rule mining, graph analysis, PageRank, and BERT sentiment analysis.

It identifies frequently ordered meal combinations, ranks popular meals, analyzes customer reviews, and generates intelligent recommendations.

How we built it

We developed the solution using Python and machine learning libraries. Data mining techniques were applied to discover hidden ordering patterns, graph algorithms were used to analyze meal relationships, and BERT was utilized for sentiment analysis of customer reviews.

Challenges we ran into

• Handling multiple analytical techniques within one workflow.
• Combining graph analysis with recommendation systems.
• Processing and interpreting customer review sentiment.
• Building meaningful recommendations from complex datasets.

Accomplishments that we're proud of

• Built a complete end-to-end analytics pipeline.
• Implemented Association Rule Mining and PageRank analysis.
• Integrated BERT-based sentiment analysis.
• Generated intelligent recommendation outputs.

What we learned

We gained practical experience in Data Mining, NLP, Graph Analytics, Recommendation Systems, and Machine Learning deployment.

What's next

Future improvements include:

• Real-time recommendation generation.
• Integration with food delivery APIs.
• Personalized customer recommendation engines.
• Advanced predictive analytics.
• Interactive dashboards for business insights.

Built With

• bert
• matplotlib
• mlxtend
• networkx
• numpy
• pandas
• python
• scikit-learn
• seaborn
• streamlit
• transformers