🍽️ Restaurant Analytics Platform

A full-stack data analytics project that processes restaurant sales data (CSV/Excel) and generates automated insights such as profit margins, sales trends, and product performance.

30-Second Overview

Restaurant sales data often comes in messy spreadsheets that require manual cleaning and analysis. This project automates that workflow and presents insights through dashboards.

Live Demo

https://sensational-bunny-2c6120.netlify.app

📸 Strategic Insights Gallery

Click any image to view the Live Interactive Demo

1. Data Upload & Smart Mapping Center

*The core ingestion engine where inconsistent POS exports are automatically mapped and validated for analysis.*

2. Executive BI Dashboard

*A high-level oversight of profit trends, revenue distribution, and sales patterns to drive executive-level decision making.*

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3. Product Performance Classification (BCG-style Matrix)

*Rule-based classification logic that identifies Hidden Gems (high-margin opportunities) and Revenue Leakage items.*

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4. Revenue vs Profit Relationship Analysis

*Scatter plots with trend lines showing the relationship between revenue and net profitability.*

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5. Peak Hours & Operational Timing

*Visualizing peak revenue hours (e.g., 7:00 AM peak at ₹354) to enable precision kitchen staffing and resource allocation.*

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6. Deterministic Insights Engine

*Queries are resolved using keyword matching and intent rules mapped to pre-computed analytics outputs.*

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Key capabilities:

• Smart column mapping for inconsistent CSV/Excel data
• Rule-based analytics for profit, sales trends, and product performance
• Menu item classification (high-performing vs underperforming items)
• Interactive dashboards for decision support

Tech Stack

• Backend: Python, FastAPI
• Frontend: React, Plotly
• Data Processing: Pandas, NumPy
• Analytics: Rule-based logic

Engineering Notes (Optional)

• Handles messy real-world data using column synonym mapping
• Modular analyzers for profit, sales, and product insights
• Defensive data handling for missing or inconsistent values

What this project demonstrates

• End-to-end data analysis pipeline design
• Cleaning and normalizing real-world messy business data
• Translating raw data into business decisions
• Building production-style dashboards, not notebooks

Setup (Optional)

Backend: pip install -r requirements.txt uvicorn main:app --reload

Frontend: cd restaurant-analytics-frontend npm install npm start

Notes

This project was built as a hands-on analytics system focused on real-world data processing and business insights.

Contact & Connect

Author: Manikanta DM Email: manikantadm.25@gmail.com GitHub: @manikantadm09 LinkedIn: Connect for professional discussions