⚡ Zepto Quick-Commerce SQL Analysis

A data analysis project focused on Zepto operational data. We use SQL for cleaning, organizing, and analyzing the dataset to derive key business insights for the quick-commerce industry.

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📁 Project Structure

Path	Focus
datasets/zepto_v2.csv	Raw quick-commerce inventory data.
sql/01_zepto_data_preparation.sql	Data Cleaning: Schema setup, data quality checks, and unit conversion.
sql/02_zepto_analysis_queries.sql	EDA: Calculating key metrics, value ranking, and strategic insights.
LICENSE	Project license.

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📈 Key Insights & Analysis Highlights

This project addresses crucial business questions by segmenting the data and calculating specific metrics:

Data Preparation Focus (01_zepto_data_preparation.sql)

• Unit Conversion: Crucial transformation to convert all price fields (paise to rupees) for accurate financial analysis.
• Data Integrity: Identifying and correcting/removing invalid entries (e.g., zero prices) and checking for data quality issues.

Analysis & Strategy Focus (02_zepto_analysis_queries.sql)

• Value Assessment: Calculated Price per Gram to standardize product value and identify best-value items.
• Revenue & Discounts: Estimated total revenue per category and determined the highest average discount percentage offered.
• Inventory Segmentation: Used CASE statements to categorize products (e.g., Low, Medium, Bulk) for optimized inventory planning.

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🛠️ Core Skills Demonstrated

This project showcases core proficiency in the data analysis workflow using SQL:

• Data Preparation & Transformation: Standardizing data integrity, including crucial unit conversion (paise to rupees).
• Foundational Querying: Writing efficient SQL using GROUP BY and Aggregate Functions to summarize large datasets.
• Business Metric Calculation: Calculating key performance indicators (KPIs) like estimated revenue, price per gram efficiency, and discount averages.
• Conditional Logic: Employing CASE statements for data segmentation and custom reporting.

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🚀 Getting Started

To replicate this analysis:

1. Setup: Import the datasets/zepto_v2.csv file into your SQL database environment (e.g., MySQL, PostgreSQL).
2. Run Scripts: Execute the SQL scripts in numerical order:
   • Start with sql/01_zepto_data_preparation.sql to clean and transform the data.
   • Then run sql/02_zepto_analysis_queries.sql to generate the insights.

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