This project aims to analyze food delivery data to enhance operational efficiency and customer satisfaction. An interactive Streamlit app is developed to manage orders, customers, restaurants, and deliveries, with robust SQL database operations.
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Restaurant Performance Analysis
Revenue & Sales Insights: Analyze sales trends across different restaurants and regions.
Menu Performance: Identify high-demand and low-performing menu items.
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Market Expansion Strategy
Competitor Benchmarking: Assess market saturation and performance of competitors to optimize new restaurant openings.
Customer Demographics: Analyze user preferences and demographic trends in different cities.
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Marketing and Promotions Optimization
Campaign Effectiveness: Measure the success of marketing campaigns by tracking engagement, conversions, and reviews.
Targeted Promotions: Tailor promotional offers based on user dining patterns and spending behavior.
Dataset Creation: Generate synthetic data using Python (Faker).
Database Design: Create normalized tables for scalability.
Streamlit App: Interactive UI for data entry and insights.
Insights Extraction: Use SQL and Python for analysis.
✔️ Python scripts for data generation and management.
✔️ 20 SQL queries for analysis.
✔️ Streamlit app for data visualization.
✔️ Project documentation.
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Python
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SQL
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Database Creation
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Streamlit
This project provides a scalable solution for managing food delivery operations, offering valuable business insights and a user-friendly interface.