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information from the **Zepto** dataset using SQL.\nIt provides actionable insights into **pricing**, **stock availability**, **discount strategies**, and **category-level performance**.\n\n📂 **Dataset Source:** The dataset was obtained from **Kaggle**, contributed by **Palvinder**.\n\n---\n\n## 📊 Key Objectives\n\n* Explore and clean raw product data to ensure accuracy and consistency.\n* Analyze discount trends, pricing strategies, and stock status.\n* Derive insights on product performance, revenue, and value metrics.\n\n---\n\n## 🧩 SQL Operations Performed\n\n### 1️⃣ Table Creation \u0026 Data Exploration\n\n* Created the `zepto` table with detailed product-level fields.\n* Verified null values, duplicates, and anomalies.\n* Checked product availability (in-stock vs out-of-stock).\n\n### 2️⃣ Data Cleaning\n\n* Removed invalid records where MRP = 0.\n* Converted price data from **paise to rupees** for consistency.\n\n### 3️⃣ Data Analysis \u0026 Insights\n\n| Query  | Description                                                  |\n| :----- | :----------------------------------------------------------- |\n| **Q1** | Top 10 best-value products based on discount percentage.     |\n| **Q2** | High-MRP products that are out of stock.                     |\n| **Q3** | Estimated revenue generated by each category.                |\n| **Q4** | Premium products (MRP \u003e ₹500) with minimal discounts (\u003c10%). |\n| **Q5** | Top 5 categories offering the highest average discounts.     |\n| **Q6** | Price-per-gram calculation to determine best-value items.    |\n| **Q7** | Weight-based classification: *Low*, *Medium*, *Bulk*.        |\n| **Q8** | Total inventory weight per category.                         |\n\n---\n\n## 💡 Key Insights\n\n* **High discounts** highlight best-value products that attract customers.\n* **Premium items (\u003e₹500)** typically offer **lower discounts**, maintaining brand value.\n* **Bulk and medium-weight** items dominate total inventory weight.\n* **High-MRP products** going out of stock indicate **strong customer demand**.\n\n---\n\n## 🧠 Tech Stack\n\n* **Language:** SQL\n* **Database:** PostgreSQL\n* **Focus Areas:**\n\n  * Data Cleaning\n  * Aggregation\n  * Analytical Querying\n  * Business Insight Generation\n\n---\n\n## 🧾 Example Queries\n\n```sql\n-- Q1: Top 10 Best-Value Products\nSELECT DISTINCT name, mrp, discountPercent\nFROM zepto\nORDER BY discountPercent DESC\nLIMIT 10;\n\n-- Q3: Estimated Revenue by Category\nSELECT category,\nSUM(discountedSellingPrice * availableQuantity) AS total_revenue\nFROM zepto\nGROUP BY category\nORDER BY total_revenue;\n```\n\n## 🧑‍💻 Author\n\n**Narasimman S**\n📍 Chennai | Data Analyst | SQL \u0026 Data Science Enthusiast\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs-narasimman%2Fzepto_inventory_sql_data_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fs-narasimman%2Fzepto_inventory_sql_data_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fs-narasimman%2Fzepto_inventory_sql_data_analysis/lists"}