Pravat Kumar Gupta’s Post

I am pleased to announce the completion of my first project: Pizza Sales Analysis using SQL. I extend my sincere gratitude to WsCube Tech and Ayushi Jain for their invaluable assistance and guidance throughout this project. This project provided a significant opportunity to refine my SQL skills and to observe the direct impact of data analytics on business decision-making. The potential of SQL to generate actionable insights is inspiring, and I am eager to delve deeper into this field. The project process involved the following steps: 1. Collected data from multiple CSV files and consolidated it into a SQL server after unzipping the folder. 2. Cleaned the data to remove any junk or missing fields, ensuring high-quality data for analysis. 3. Structured the data to reveal relationships between various factors, such as the highest-priced pizza. 4. Utilized SQL commands to uncover key insights including total sales, most common pizza size, highest-priced pizza, category-wise pizza distribution, cumulative revenue analysis, orders distribution by hour, average orders, and more. 5. Integrated data from different tables in the Model View to visualize substantial pizza sales. 6. Grouped and sorted the data to identify trends and strategize for increased profitability. 7. Enhanced the presentation with images and a well-designed background to make the insights visually appealing. I am eager to connect with SQL professionals and enthusiasts. Your insights, tips, or advice to further improve my skills and efficiency would be greatly appreciated. Please feel free to share your experiences or thoughts in the comments. #SQL #DataAnalytics #BusinessOptimization #SQLQueries #BusinessInsights #PizzaSales #DataDrivenDecisions #SQLLearning #DataScience ---

🚀 Unlocking Insights from Pizza Sales Data with SQL! 🍕📊 Hey LinkedIn Family, I'm excited to share a recent project where I leveraged SQL to dive deep into a Pizza Sales Analysis. With four comprehensive CSV files, we uncovered valuable insights using powerful SQL techniques, including functions, joins, and subqueries. Here’s a snapshot of our journey: 📂 The Data We worked with four key CSV files: Orders: Detailed records of pizza orders. Pizzas: Information on different pizza types. Pizza Types: Descriptions and categories of pizzas. Order Details: Specifics about each order, like quantity and price. 🛠️ Techniques Used Joins: To combine data across multiple tables and get a holistic view. Subqueries: For calculating intermediate results, like average order values. Aggregations: To sum up sales and quantities for insightful metrics. Functions: Such as SUM(), AVG(), and DAYNAME() to process data efficiently. 🌟 Key Insights Top Performer: The "Thai Chicken" pizza emerged as the top-selling item. Peak Hour: 5pm - 9pm time range had the highest sales, indicating a spike in orders. Revenue Leader: The "Classic" category led in revenue generation, highlighting customer preference for unique flavors. This analysis not only helped in understanding customer preferences but also paved the way for strategic decisions in marketing and inventory management. #DataAnalytics #SQL #PizzaSales #BusinessIntelligence #Insights #Functions #Report #CSV

🚀 Excited to Share My Latest SQL Project: Pizza Sales Analysis! 🍕 As we all know, data analysis plays a crucial role in making informed business decisions 📊. And as a passionate pizza lover and data enthusiast, I am excited to share my latest project with you all – a sales analysis of the pizza industry using SQL.📊 This project provided invaluable insights into customer preferences, sales trends, and strategic opportunities and also sharpening my data analysis skills. GITHUB PROJECT LINK :- https://lnkd.in/gafyAH6k 🌟 Special Thanks: A heartfelt thanks to Ayushi Jain, Shradha Khapra and WsCube Tech for their invaluable guidance and support throughout this project. 🎯 Project Objective: The primary objective of this project was leverages SQL to explore and analyze sales data from a pizza shop .The goal is to uncover insights into sales trends, popular pizza types, and customer preferences to help improve business strategies and decision-making. 📊 Project Structure: 1) PIZZAS: Detailed data on pizza types, sizes, and prices. 2) ORDERS: Comprehensive records of all orders. 3) ORDER_DETAILS: Insights into order quantities and specifics. 4) PIZZA_TYPES: Classification of pizzas by category and ingredients. 📈 Insights Gained: 1) Identification of best-selling pizza types and peak sales periods. 2) Analysis of order quantities and customer preferences. 3) Analyzed weekly and monthly sales trends to uncover significant patterns. #DataAnalysis #SQL #PizzaSales #Analytics #DataVisualization #PizzaSales #AdvancedSQL #DataInsights #ProjectShowcase #DataDriven #Analytics #BusinessInsights

🚀 𝗘𝘅𝗰𝗶𝘁𝗲𝗱 𝘁𝗼 𝗦𝗵𝗮𝗿𝗲 𝗺𝘆 𝗙𝗶𝗿𝘀𝘁 𝗦𝗤𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 𝗣𝗶𝘇𝘇𝗮 𝗦𝗮𝗹𝗲𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗨𝘀𝗶𝗻𝗴 𝗦𝗤𝗟 Under the Guidance of Ayushi Jain from WsCube Tech YouTube Channel📊 🥅 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲: - 1️⃣ Analyzing a comprehensive pizza sales dataset that Includes 4 csv files providing information about the Pizza. 2️⃣ Executing SQL queries to answer critical business questions, focusing on uncovering patterns in orders, understanding customer preferences, evaluating revenue trends, and assessing product performance. 3️⃣ Converting unprocessed data into actionable insights by answering 13 Business Questions. ➡ 𝗜𝗺𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗰𝘀𝘃 𝗙𝗶𝗹𝗲𝘀: - Import Dataset csv in Workbench using import wizard. order and order_details are too big and data type are not correct So, created table first and then added data in these 2 tables. ➡ 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: - ▶ Large pizzas were the most ordered size around 18,526 units, indicating a preference for larger portions among customers. ▶ The classic Deluxe Pizza is the most ordered (Quantity sold) pizza type followed by others. ▶ Classic Category has highest Total quantity of pizza ordered in comparison to Supreme, Veggie, and Chicken. ▶ The busiest hours for orders were in the evening (5–7 PM) and afternoon (12–1 PM), reflecting mealtime peaks for the Billu’s Pizza Hub. ▶ In terms of revenue, the Chicken category dominated, with its top three pizza types contributing significantly. Contribute around 23.96% in total revenue. ▶ Total ordered placed are around 21,350 and total revenue generated is around $8,17,860. ▶ Average pizza ordered per day is 138. This project showcases my ability to use SQL to solve real-world business problems and extract meaningful insights from raw Data. I want to give a huge shoutout to Ayushi Jain Ma'am for her amazing guidance on data analysis with SQL. Her YouTube channel, WsCube Tech, are great to horn one’s skills.   𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗲 𝗚𝗶𝘁𝗛𝘂𝗯 𝗟𝗶𝗻𝗸 𝗵𝗲𝗿𝗲:  https://lnkd.in/gjMCTGey #SQL #MYSQL #PIZZASALES #DATA #DATAANALYSIS #WSCUBETECH #BUSINESSINSIGHTS #YOUTUBE #DATAANALYST

🚀 SQL Pizza Sales Analysis Project 🍕💻 I’m excited to share my latest SQL project, where I analyzed a real-world pizza sales dataset to extract meaningful business insights! This project involved writing SQL queries to understand key aspects of pizza sales performance. Here’s a quick overview of the objectives I accomplished: • Total orders and revenue generated. • Highest-priced pizza and most common pizza size. • Top 5 most ordered pizzas by quantity. • Category-wise pizza distribution and hourly order trends. • Average pizzas ordered per day. • Top 3 pizzas by revenue and their contribution to total revenue. • Cumulative revenue analysis over time. This project strengthened my SQL skills and ability to generate actionable business insights. Let’s connect to discuss SQL and data analytics! #SQL #DataAnalysis #PizzaSales #BusinessIntelligence #Analytics #SQLProjects WsCube Tech Ayushi Jain

🚀 Exciting SQL Analysis Project 🚀 I'm thrilled to share my recent deep dive into SQL data analysis with Ayushi Jain, where I tackled a variety of insightful queries on pizza orders! 🍕📊 Here's a snapshot of the insights uncovered: 1.Total Number of Orders Placed: Analyzed order data to determine the overall volume of pizza orders. 2.Total Revenue from Pizza Sales: Calculated the total revenue generated, providing a clear picture of sales performance. 3. Highest-Priced Pizza: Identified the priciest pizza on the menu, a favorite for premium customers. 4. Most Common Pizza Size Ordered: Found the most popular pizza size, revealing customer preferences. 5. Top 5 Most Ordered Pizza Types: Listed the most ordered pizza types along with their quantities, highlighting customer favorites. 6. Hourly Order Distribution: Analyzed order patterns by hour to understand peak times. 7. Category-wise Pizza Distribution: Joined tables to show how different categories contribute to the overall order distribution. 8. Top 3 Pizza Types by Revenue in Each Category: Determined the top revenue-generating pizzas within each category. These insights are crucial for making informed business decisions, optimizing inventory, and tailoring marketing strategies. 📈💡 GitHub: https://lnkd.in/dska982Y #SQL #DataAnalysis #BusinessIntelligence #DataScience #PizzaLovers #DataDrivenDecisionMaking #SQLQueries #DataInsights #TechInBusiness Feel free to connect if you're interested in discussing more about data analysis or have similar projects! 🤝

Day 19 of #30daysqlquerychallenge Hello Data Enthusiasts, In this post, I'll share a SQL code snippet that can help you analyze pizza delivery data to identify trends in late deliveries and free pizzas given away. Solution: 1. Creating a monthly timeframe: The code uses the TO_CHAR function to format the "order_time" column into a 'Mon YYYY' format, creating a monthly timeframe for grouping the data. 2. Calculating Delayed Deliveries: It calculates the percentage of delayed deliveries per month using a conditional statement within the SUM function. The condition checks if the difference between the actual delivery time and the expected delivery time is greater than 30 minutes. If it is, the order is considered delayed and is counted towards the monthly total. 3. Identifying Free Pizzas: Another conditional statement within the SUM function calculates the total number of free pizzas given away each month. Similar to the delayed delivery calculation, it checks the delivery time difference. If an order is delayed, the number of pizzas for that order is included in the monthly free pizza count. 4. Grouping and Sorting: The GROUP BY clause groups the results by month, and the ORDER BY clause sorts the results chronologically by month. Do you have any questions about these solutions or alternative approaches? Share your thoughts in the comments! I want to extend my gratitude to Thoufiq Mohammed for organizing this engaging SQL challenge. It's a fantastic opportunity to hone our skills and connect with fellow data enthusiasts. Much appreciated! #SQL #Database #Education #DataAnalysis #ProblemSolving

SQL Project : Pizza Sales Analysis📉 📋 About : This Project focuses on using SQL to analyze data effectively, to uncover actionable insights for a pizza restaurant to inform strategic decision-making and optimize sales strategies. 📌 Project Highlights : ➡ Extract and aggregate sales data from the pizza sales dataset to calculate key metrics such as total sales revenue, average order value, Total Orders. ➡ Analyze sales trends over time (hourly, daily, monthly) to identify peak sales periods and seasonal variations. ➡ Calculate sales performance metrics for individual products, including best-selling pizzas, popular pizza sizes, and revenue contribution by product category. 📊 Skills Used : Advanced SQL queries including JOINs, GROUP BY, Window functions and other aggregate functions. 🎓 Learning Outcomes: Enhanced my SQL skills through practical application. Gained insights into data-driven decision-making processes. Improved my ability to present data in a clear and impactful manner. Special thanks 🙌 to Ayushi Jain and WsCube Tech for their support and guidance for this project. I’m posting this project to showcase my skills and seek constructive feedback. Your insights on how I can further improve my skills would be greatly appreciated! #sql #database #dataanalysis #mavenanalytics #businessintelligence

🙋 Just completed a comprehensive SQL project analyzing 𝒑𝒊𝒛𝒛𝒂 𝒔𝒂𝒍𝒆𝒔 𝒅𝒂𝒕𝒂! 🍕📊 In this project, I analyzed a dataset of pizza sales records to gain valuable insights into customer preferences and sales trends for a pizza business. 🍕 𝐊𝐞𝐲 𝐑𝐞𝐬𝐮𝐥𝐭𝐬: ●Total Orders Analyzed: Demonstrated the ability to retrieve and aggregate order data. ●Revenue Insights: Extracted total revenue generated from pizza sales through complex queries. ●Pizza Size Analysis: Identified the most common pizza size ordered, showcasing data categorization skills. ●Top 5 Pizza Types: Analyzed order quantities to determine the five most ordered pizzas, reflecting customer preferences. ●Trends and Patterns: Explored order distribution by hour and day, highlighting peak times for pizza sales. 🍕 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐔𝐭𝐢𝐥𝐢𝐳𝐞𝐝: ●Subqueries and Common Table Expressions (CTEs) ●Database creation and table imports ●Joins for comprehensive data analysis ●Analytical and window functions for advanced metrics 🍕 𝐊𝐞𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬: ●Developed a deeper understanding of SQL and data manipulation techniques. ●Gained hands-on experience with data analysis in a real-world context. I’m excited about the insights gained from this project and am eager to apply these skills to future data challenges. If you're interested in discussing SQL, data analysis, or any related topics, let’s connect! 🔗 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐋𝐢𝐧𝐤: https://lnkd.in/g45Ywa_h #SQL #DataAnalytics #MySQL #Workbench #PizzaSales #ProjectShowcase #DataDrivenInsights #SalesTrends #Upskilling #LearningJourney

Data-Driven Dough: Unveiling Pizza Sales Insights with SQL! Looking to slice up some serious success in the pizza business? I'm excited to share a recent project where I leveraged SQL queries in SSMS to analyze pizza sales data for a client. This deep dive aimed to identify key trends, customer preferences, and areas for improvement – all to fuel data-driven decision-making. Here's how this SQL analysis empowers pizza perfection: Boost Sales Performance: Identifying popular menu items and sales trends allows for targeted marketing campaigns and promotions, maximizing customer engagement and revenue. Optimize Operations: Understanding peak sales periods and order behavior leads to streamlined operational processes and resource allocation, improving efficiency and cost control. Support Strategic Planning: Data-driven insights inform strategic decisions regarding menu development, pricing strategies, and potential market expansion opportunities. By harnessing the power of SQL, businesses can transform pizza sales from a guessing game to a data-driven delight! I am grateful to Ayushi Jain and the team at WsCube Tech for their invaluable guidance and mentorship on this project. #PizzaSales #DataAnalysis #SSMS #SQL #BusinessInsights #DecisionMaking