SQL Projects
SQL Projects
ScienceQtech Employee Performance Mapping Using SQL
I have generated comprehensive reports detailing employee information, performance metrics, and project specifics, facilitating in-depth analysis of our employee database. These reports are customized to extract specific data tailored to various requirements and insights needed for strategic decision-making.
Features : Generating performance reports and analyzing employee database to extract project-specific data for the annual appraisal cycle.
• Database design
• Data querying and analysis
• Data manipulation
• Quality assurance
• Performance optimization
Skills: MySQL , SQL Querying , Data Normalization , Data Modeling , Database Design and Management
Air Cargo, a leading aviation company, specializes in air transportation services for both passengers and cargo. Through strategic partnerships and alliances with other airlines, Air Cargo offers a diverse range of services utilizing its fleet of aircraft. To enhance the travel and booking experience for its customers, the company is focused on generating comprehensive reports that analyze regular passenger trends, busiest routes, ticket sales, and various other key scenarios.
Features :
Data Preparation: Collected and cleaned air cargo data from multiple sources; designed database schema.
Descriptive Analysis: Analyzed cargo volume, weight, and frequent flight routes.
Performance Metrics: Measured on-time performance, delay causes, and capacity utilization.
Geographical Analysis: Identified busiest airports and efficient cargo routes.
Financial Insights: Calculated revenue, costs, and profit margins for different routes.
Temporal Trends: Identified seasonal patterns and peak times for cargo transport.
Operational Efficiency: Evaluated turnaround time, handling efficiency, and maintenance impact.
Predictive Analysis: Forecasted future demand and planned capacity needs.
Visualizations: Created dashboards and reports using SQL queries and visualization tools.
Skills: MySQL ,SQL Querying , Data Normalization , Data Modeling , Database Design and Management
This project dives deep into the analysis of pizza sales data using SQL, focusing on uncovering trends, customer preferences, and revenue patterns to drive business insights. The dataset, sourced from Kaggle, includes comprehensive information on orders, pizza categories, customer behaviors, and pricing. By analyzing sales trends, identifying peak ordering times, and exploring product performance, this project aims to deliver actionable insights for optimizing sales strategies, improving customer satisfaction, and maximizing revenue.
Features :
Comprehensive Data Insights: In-depth analysis of order volumes, revenue, pricing, and customer preferences.
Revenue Trends: Identification of high-performing pizza types based on revenue contribution and pricing.
Customer Behavior Analysis: Understanding preferences, including pizza sizes, categories, and peak ordering hours.
Category Performance: Insights into pizza categories, variety, and distribution.
Time-Based Trends: Analysis of sales patterns over time, highlighting growth trends and peak order periods.
Actionable Insights: Recommendations for optimizing sales strategies and inventory management.
Key Highlights
Order Volume: Processed 21,350 orders, reflecting high demand.
Revenue: Generated $817,860, with a steady upward trend.
Customer Preferences: Large pizzas were most popular (18,526 orders).
Top-Selling Pizza: Classic Deluxe Pizza with 2,453 orders.
Peak Hours: 5-7 PM, with 6 PM being the busiest.
Skills: MySQL, SQL Querying, Data Cleaning and Transformation, Data Normalization, Data Modeling, Database Design and Management, Exploratory Data Analysis (EDA), Data Visualization, Business Intelligence, Revenue Analysis.
This project focuses on analyzing Indian Railway bookings using SQL Server Management Studio (SSMS). The dataset, containing 20,000+ records, was self-created using Python and ChatGPT to simulate real-world railway operations. The goal was to extract insights on train traffic, revenue generation, passenger behavior, and booking trends using advanced SQL queries and analytics techniques.
Python – Used for dataset generation
SQL Server Management Studio (SSMS) – For SQL queries and data analysis
Advanced SQL Queries (Joins, Aggregations, Window Functions, CTEs)
Busiest Railway Stations – Identified stations with the most departures and arrivals
Revenue Insights – Found top revenue-generating trains and stations
Passenger Behavior Analysis – Discovered frequent travelers and ticket booking patterns
Booking & Cancellation Trends – Analyzed most canceled train routes
Ticket Pricing Analysis – Evaluated fare variations based on train types
Monthly Booking Trends – Analyzed seasonality and peak travel periods
This project demonstrates how data-driven decision-making can enhance railway operations, optimize revenue streams, and improve passenger experiences. By leveraging SQL analytics, railway authorities can gain actionable insights to enhance scheduling, pricing strategies, and overall efficiency.
Project Overview
This project analyzes Superstore Sales Data using SQL to extract valuable insights into sales trends, profitability, customer behavior, and shipping performance. The dataset includes information on orders, customers, products, shipping, and regional sales performance.
Revenue & Profit Trends: Analyzed total sales and profit across different years and quarters.
Regional & State-Wise Performance: Identified the highest revenue-generating regions, states, and cities.
Product & Category Analysis: Determined the most profitable and most discounted product categories.
Customer Behavior: Found customers with multiple orders and analyzed average order frequency.
Shipping Performance: Calculated average shipping time per class.
Year-over-Year (YoY) Growth: Used window functions to compute annual sales growth.
Top Performing Products: Ranked the most profitable products per region.
SQL Server Management Studio (SSMS) for data querying and analysis
Window Functions, Aggregations, Joins, and CTEs for advanced insights
This project focuses on analyzing an Online Book Store dataset using SQL to gain valuable insights into book inventory, customer behavior, sales trends, and revenue generation. The dataset includes details on books, customers, and orders, enabling in-depth analysis through SQL queries.
Perform basic and advanced SQL queries to extract meaningful insights.
Analyze customer purchasing behavior and order trends.
Evaluate book sales performance by genre, author, and pricing.
Optimize stock management and revenue analysis.
Book Sales Performance: Identified best-selling books and most popular genres.
Customer Analysis: Found repeat customers and spending patterns.
Revenue Insights: Calculated total revenue and highest-spending customers.
Stock Management: Determined books with the lowest stock and stock remaining after sales.
Order Trends: Analyzed peak purchasing periods and most frequently ordered books.
SQL Server / MySQL for data querying and analysis
Joins, Aggregations, Window Functions, CTEs for advanced insights
This project focuses on analyzing a food delivery dataset inspired by Swiggy to understand customer behavior, restaurant performance, and delivery efficiency. The dataset was generated using Python libraries such as Pandas, Random, and Faker and contains details on customers, orders, restaurants, deliveries, and riders.
🔹 Perform data extraction, cleaning, and analysis using SQL.
🔹 Analyze customer purchasing behavior and ordering patterns.
🔹 Evaluate restaurant performance, sales trends, and peak order times.
🔹 Assess delivery efficiency and rider performance.
🔹 Identify business growth opportunities and revenue trends.
Customer Behavior – Identified high-spending customers and frequent order trends.
Restaurant Performance – Ranked top restaurants by sales and city-wise order distribution.
Order Trends – Analyzed peak ordering hours and weekday vs. weekend sales.
Delivery Efficiency – Measured average delivery time and success rates.
Revenue Insights – Tracked monthly revenue trends and customer churn patterns.
SQL – Queries for data analysis, joins, aggregations, and trend analysis.
Python – Generated dataset using Pandas, Faker, and Random libraries.
This project focuses on analyzing an E-Commerce dataset to understand customer purchasing behavior, product performance, sales trends, and operational efficiency. The dataset was generated using Python libraries such as Pandas, Faker, and Random, simulating a real-world online retail business. It includes details on customers, orders, products, sellers, inventory, payments, and shipping.
Perform data extraction, cleaning, and analysis using Python and SQL.
Analyze customer demographics, order frequency, and spending patterns.
Identify top-selling products, revenue trends, and category performance.
Evaluate seller performance and market contribution.
Assess inventory status and optimize stock management.
Examine payment methods, refund trends, and failed transactions.
Measure shipping efficiency, delivery times, and return rates.
Customer Behavior – Identified high-value customers, repeat purchase trends, and regional demand distribution.
Sales Performance – Ranked top-selling products, high-revenue categories, and best-performing sellers.
Order Trends – Analyzed peak order times, seasonal demand fluctuations, and cart abandonment rates.
Inventory Optimization – Identified low-stock products, restocking needs, and warehouse distribution.
Payment Insights – Evaluated preferred payment methods, refund patterns, and fraud detection.
Shipping Efficiency – Measured average delivery times, on-time delivery rates, and return logistics.
SQL – Data extraction, joins, aggregations, and trend analysis.
Python – Dataset generation and analysis using Pandas, Faker, and Random libraries.
Project Overview
This project focuses on analyzing restaurant sales data to uncover customer purchasing patterns, menu performance, and revenue trends. The dataset includes three key tables—Sales, Menu, and Members—providing insights into customer behavior, product performance, and membership impact. SQL queries were used to extract, clean, and analyze the data, helping to drive data-driven business decisions.
Objectives
Perform data extraction, cleaning, and analysis using SQL.
Analyze customer spending patterns, order frequency, and visit trends.
Identify top-selling menu items and their contribution to total revenue.
Assess the impact of membership on customer purchases.
Rank customers based on total spending and purchase frequency.
Key Insights & Analysis
Customer Behavior – Identified high-value customers and analyzed spending trends.
Menu Performance – Determined best-selling items and high-revenue menu categories.
Revenue Insights – Calculated revenue contributions and seasonal sales trends.
Membership Impact – Compared purchase patterns before and after membership enrollment.
Order Trends – Analyzed peak ordering hours and most popular food categories.
Technologies Used
🔹 SQL – Data extraction, joins, aggregations, CTEs, and window functions.
🔹 Data Visualization – Used Excel and Tableau to present insights.
💡 Skills Applied: SQL, Data Analysis, Business Intelligence, Performance Metrics.