Use cases that can be supported with Yelp Reviews

A. Market Research and Analysis: Leverage Yelp data to conduct comprehensive market research and analysis in the restaurant industry. Identify emerging culinary trends, popular cuisines, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your restaurant with competitors on Yelp. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your restaurant's online reputation effectively. Track and analyze customer reviews and ratings on Yelp to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Yelp dataset to analyze pricing strategies and revenue trends in the restaurant sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your restaurant's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Yelp reviews to gauge customer satisfaction and sentiment towards your restaurant. Use this information to improve dining experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your restaurant's website based on popular keywords, cuisines, and dining preferences identified in the Yelp dataset. Optimize your content to improve search engine rankings and attract more potential diners.

G. Personalized Marketing Campaigns: Use Yelp data to segment your target audience based on dining preferences, food habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and repeat business.

H. Investment and Expansion Decisions: Access historical and real-time data on restaurant performance and market dynamics from Yelp. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new culinary ventures.

I. Predictive Analytics: Utilize the Yelp dataset to build predictive models that forecast future trends in the restaurant industry. Anticipate shifts in culinary preferences, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Yelp dataset. These dashboards can help restaurant executives and stakeholders get a quick overview of the restaurant's performance and make data-driven decisions.

Incorporating the Yelp dataset into your business processes will enhance your understanding of the restaurant market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive culinary industry.

Problem Statement

You are the CEO of a restaurant franchise and are considering different cities for opening a new outlet.

You would like to expand your business to cities that may give your restaurant higher profits. The chain already has restaurants in various cities and you have data for profits and populations from the cities. You also have data on cities that are candidates for a new restaurant. For these cities, we have the city population. Task is to use the data to help you identify which cities may potentially give the business higher profits?

Dataset

x_train is the population of a city y_train is the profit of a restaurant in that city. A negative value for profit indicates a loss. Both X_train and y_train are numpy arrays.

Dataset Overview

This dataset contains 2,000 rows of data from coffee shops, offering detailed insights into factors that influence daily revenue. It includes key operational and environmental variables that provide a comprehensive view of how business activities and external conditions affect sales performance. Designed for use in predictive analytics and business optimization, this dataset is a valuable resource for anyone looking to understand the relationship between customer behavior, operational decisions, and revenue generation in the food and beverage industry.

Columns & Variables

The dataset features a variety of columns that capture the operational details of coffee shops, including customer activity, store operations, and external factors such as marketing spend and location foot traffic.

1. Number of Customers Per Day

   • The total number of customers visiting the coffee shop on any given day.
   • Range: 50 - 500 customers.

2. Average Order Value ($)

   • The average dollar amount spent by each customer during their visit.
   • Range: $2.50 - $10.00.

3. Operating Hours Per Day

   • The total number of hours the coffee shop is open for business each day.
   • Range: 6 - 18 hours.

4. Number of Employees

   • The number of employees working on a given day. This can influence service speed, customer satisfaction, and ultimately, sales.
   • Range: 2 - 15 employees.

5. Marketing Spend Per Day ($)

   • The amount of money spent on marketing campaigns or promotions on any given day.
   • Range: $10 - $500 per day.

6. Location Foot Traffic (people/hour)

   • The number of people passing by the coffee shop per hour, a variable indicative of the shop's location and its potential to attract customers.
   • Range: 50 - 1000 people per hour.

Target Variable

• Daily Revenue ($)
  • This is the dependent variable representing the total revenue generated by the coffee shop each day.
  • It is calculated as a combination of customer visits, average spending, and other operational factors like marketing spend and staff availability.
  • Range: $200 - $10,000 per day.

Data Distribution & Insights

The dataset spans a wide variety of operational scenarios, from small neighborhood coffee shops with limited traffic to larger, high-traffic locations with extensive marketing budgets. This variety allows for exploring different predictive modeling strategies. Key insights that can be derived from the data include:

• The effect of marketing spend on daily revenue.
• The correlation between customer count and daily sales.
• The relationship between staffing levels and revenue generation.
• The influence of foot traffic and operating hours on customer behavior.

Use Cases & Applications

The dataset offers a wide range of applications, especially in predictive analytics, business optimization, and forecasting:

• Predictive Modeling: Use machine learning models such as regression, decision trees, or neural networks to predict daily revenue based on operational data.
• Business Strategy Development: Analyze how changes in marketing spend, staff numbers, or operating hours can optimize revenue and improve efficiency.
• Customer Insights: Identify patterns in customer behavior related to shop operations and external factors like foot traffic and marketing campaigns.
• Resource Allocation: Determine optimal staffing levels and marketing budgets based on predicted sales, improving overall profitability.

Real-World Applications in the Food & Beverage Industry

For coffee shop owners, managers, and analysts in the food and beverage industry, this dataset provides an essential tool for refining daily operations and boosting profitability. Insights gained from this data can help:

• Optimize Marketing Campaigns: Evaluate the effectiveness of daily or seasonal marketing campaigns on revenue.
• Staff Scheduling: Predict busy days and ensure that the right number of employees are scheduled to maximize efficiency.
• Revenue Forecasting: Provide accurate revenue projections that can assist with financial planning and decision-making.
• Operational Efficiency: Discover the most profitable operating hours and adjust business hours accordingly.

This dataset is also ideal for aspiring data scientists and machine learning practitioners looking to apply their skills to real-world business problems in the food and beverage sector.

Conclusion

The Coffee Shop Revenue Prediction Dataset is a versatile and comprehensive resource for understanding the dynamics of daily sales performance in coffee shops. With a focus on key operational factors, it is perfect for building predictive models, ...

Cooks working in fast food restaurants in the United States had a median hourly wage of 14.50 U.S. dollars as of May 2024. Meanwhile, 10 percent of fast food cooks earned less than 10.76 U.S. dollars per hour.

A. Market Research and Analysis: Utilize the Tripadvisor dataset to conduct in-depth market research and analysis in the travel and hospitality industry. Identify emerging trends, popular destinations, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your hotel or travel services with competitors on Tripadvisor. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your hotel's online reputation effectively. Track and analyze customer reviews and ratings on Tripadvisor to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Tripadvisor dataset to analyze pricing strategies and revenue trends in the hospitality sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your hotel's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Tripadvisor reviews to gauge customer satisfaction and sentiment towards your hotel or travel service. Use this information to improve guest experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your hotel or travel website based on the popular keywords, topics, and interests identified in the Tripadvisor dataset. Optimize your content to improve search engine rankings and attract more potential guests.

G. Personalized Marketing Campaigns: Use the data to segment your target audience based on preferences, travel habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and conversions.

H. Investment and Expansion Decisions: Access historical and real-time data on hotel performance and market dynamics from Tripadvisor. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new ventures.

I. Predictive Analytics: Utilize the dataset to build predictive models that forecast future trends in the travel industry. Anticipate demand fluctuations, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Tripadvisor dataset. These dashboards can help executives and stakeholders get a quick overview of the hotel's performance and make data-driven decisions.

Incorporating the Tripadvisor dataset into your business processes will enhance your understanding of the travel market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive hospitality industry

📹 Project Video available on YouTube - https://youtu.be/dQwnyCEZ-UU

🖇️Connect with me on LinkedIn - https://www.linkedin.com/in/rohit-grewal

It is a sales data of a restaurant company operating in multiple cities in the world. It contains information about individual sales transactions, customer demographics, and product details. The data is structured in a tabular format, with each row representing a single record and each column representing a specific attribute. This dataset can be commonly used for business intelligence, sales forecasting, and customer behaviour analysis.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1) Most Preferred Payment Method ?

Q.2) Most Selling Product - By Quantity & By Revenue ?

Q.3) Which city had maximum revenue , or , Which Manager earned maximum revenue ?

Q.4) Date wise revenue.

Q.5) Average Revenue.

Q.6) Average Revenue of November & December month.

Q.7) Standard Deviation of Revenue and Quantity ?

Q.8) Variance of Revenue and Quantity ?

Q.9) Is revenue increasing or decreasing over time?

Q.10) Average 'Quantity Sold' & 'Average Revenue' for each product ?

These are the main Features/Columns available in the dataset :

1) Order ID: A unique identifier for each sales order. This can be used to track individual transactions.

2) Order Date: The date when the order was placed. This column is essential for time-series analysis, such as identifying sales trends over time or seasonality.

3) Product: The name or type of the product sold. This column is crucial for analyzing sales performance by product category.

4) Price : The unit price of the product. This, along with 'Quantity Ordered', is used to calculate the total price of each order.

5) Quantity : The number of units of the product sold in a single order. This is a key metric for calculating revenue and understanding sales volume.

6) Purchase Type : The order was made online or in-store or drive-thru.

7) Payment Method : How the payment for the order was done.

8) Manager : Name of the manager of the store.

9) City : The location of the store. This can be used for geographical analysis of sales, such as identifying top-performing regions or optimizing logistics.

The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of food services and drinking places (NAICS 722), annual, for five years of data.

Gross-Profit-Margin Time Series for Insight Enterprises Inc. Insight Enterprises, Inc., together with its subsidiaries, provides information technology, hardware, software, and services in the United States and internationally. The company offers modern platforms/infrastructure that manages and supports cloud and data platforms, modern networks, and edge technologies; cybersecurity solutions automates and connects modern platform securely; data and artificial intelligence modernizes data platforms and architectures, and build data analytics and AI solutions; modern workplace and apps; and intelligent edge solutions that gathers and utilizes data for real-time decision making. It provides software maintenance solutions that offers clients to obtain software upgrades, bug fixes, help desk, and other support services; vendor direct support services contracts; and cloud/software-as-a-service subscription products. In addition, the company designs, procures, deploys, implements, and manages solutions that combine hardware, software, and services to help businesses. It serves construction, esports, financial services, health care and life sciences, manufacturing, retail and restaurant, service providers, small to medium business, and travel and tourism industries. The company was founded in 1988 and is headquartered in Chandler, Arizona.

This dataset provides a comprehensive analysis of the tourism and hospitality industry, covering key metrics such as visitor trends, hotel occupancy rates, average spending per tourist, seasonal demand patterns, and revenue insights. It is useful for travel analysts, hospitality businesses, researchers, and policymakers to understand industry dynamics, predict trends, and optimize business strategies.

Potential Use Cases:

📊 Market Analysis: Identify travel trends and popular destinations.
📈 Revenue Forecasting: Predict hotel occupancy rates and revenue patterns.
🏨 Hospitality Business Insights: Analyze customer preferences and spending habits.
🌍 Tourism Policy Evaluation: Support government tourism strategies.
🧠 Machine Learning Applications: Build predictive models for visitor demand and pricing optimization.

The resort booking dataset chosen for this data science project is a rich repository of information designed to provide a comprehensive understanding of the dynamics associated with reservations at a high-end resort. The dataset is meticulously curated, encompassing a diverse array of variables that capture various facets of the booking process. Customer demographics, including age, gender, and geographical location, offer insights into the resort's target audience. Reservation-specific details, such as dates, room preferences, and the duration of stays, are meticulously recorded, enabling a granular analysis of booking patterns over time.

In addition to the fundamental booking information, the dataset delves into the channels through which reservations are made. This includes online platforms, travel agencies, and direct bookings, shedding light on the diverse pathways through which guests engage with the resort. Furthermore, the dataset incorporates data on special requests made by guests, offering a nuanced perspective on their preferences and expectations.

A critical aspect covered by the dataset is the occurrence of cancellations. Understanding the factors influencing cancellations is essential for optimizing resource allocation and revenue management. Whether driven by changes in plans, external circumstances, or other factors, a thorough analysis of cancellation patterns can inform strategies to mitigate revenue loss and enhance operational efficiency.

This data science project aims to leverage the resort booking dataset for exploratory data analysis (EDA) and predictive modeling. EDA will involve uncovering patterns, trends, and anomalies within the data, providing valuable insights into customer behavior and preferences. Predictive modeling will then allow for the development of algorithms to forecast future booking trends, optimize pricing strategies, and identify factors contributing to cancellations.

Ultimately, the project seeks to empower resort management with actionable intelligence derived from the dataset. Strategic decision-making, informed by the findings of this analysis, can lead to improved operational efficiency, targeted marketing efforts, and an elevated overall guest experience. By harnessing the power of data, this project endeavors to contribute to the resort's success in a highly competitive hospitality landscape.

Chicken Republic Lagos Sales Dataset – Fast Food Sales Analysis (NG)

📝 Dataset Overview: This dataset captures real-world retail transaction data from Chicken Republic outlets in Lagos, Nigeria. It provides detailed insights into fast food sales performance across different product categories, with columns that track revenue, quantity sold, and profit.

Ideal for anyone looking to:

Practice sales analysis

Build business intelligence dashboards

Forecast product performance

Analyze profit margins and pricing

🔍 Dataset Features: Column Name Description Date Date of each transaction Location Outlet or branch where the sale occurred Product Category Category of the product sold (e.g., Meals, Drinks, Snacks) Product Name of the specific product Quantity Sold Number of units sold Unit Price (NGN) Price per unit in Nigerian Naira Total Sales (NGN) Quantity × Unit Price Profit (NGN) Estimated profit from the sale

🎯 Use Cases: Build Power BI dashboards with slicers and filters by product category

Perform profitability analysis per outlet

Create forecast models to predict sales

Analyze customer preferences based on high-selling items

Create data storytelling visuals for retail presentations

🛠 Tools You Can Use: Excel / Google Sheets

Power BI / Tableau

Python (Pandas, Matplotlib, Seaborn)

SQL for querying sales trends

👤 Creator: Fatolu Peter (Emperor Analytics) Working actively on real-world retail, healthcare, and social media analytics. This dataset is part of my ongoing data project series (#Project 9 and counting!) 🚀

✅ LinkedIn Post: 🚨 New Dataset Drop for Analysts & BI Enthusiasts 📊 Chicken Republic Lagos Sales Dataset – Now on Kaggle! 🔗 Access here

Whether you’re a student, analyst, or business developer—this dataset gives you a clean structure for performing end-to-end sales analysis:

✅ Track daily sales ✅ Visualize profit by product category ✅ Create Power BI dashboards ✅ Forecast best-selling items

Columns include: Date | Location | Product | Quantity Sold | Unit Price | Total Sales | Profit

Built with love from Lagos 🧡 Let’s drive real insights with real data. Tag me if you build something amazing—I’d love to see it!

SalesAnalytics #ChickenRepublic #PowerBI #RetailData #KaggleDataset #NigerianBusiness #BusinessIntelligence #FatoluPeter #EmperorAnalytics #Project9 #DataForPractice

Revenue team in the hospitality domain Domain: Hospitality
Function: Revenue

AtliQ Grands owns multiple five-star hotels across India. They have been in the hospitality industry for the past 20 years. Due to strategic moves from other competitors and ineffective decision-making in management, AtliQ Grands are losing its market share and revenue in the luxury/business hotels category. As a strategic move, the managing director of AtliQ Grands wanted to incorporate “Business and Data Intelligence” to regain their market share and revenue. However, they do not have an in-house data analytics team to provide them with these insights.

Their revenue management team had decided to hire a 3rd party service provider to provide them with insights from their historical data.

Here is the reference of Data Visualization :

https://github.com/SanthoshSivakumar/Hospatility-Domain

Introduction / Overview: HRAST is a rich, multi-label dataset with 23,113 unique user-generated review sentences designed for natural language processing tasks focused on hotel reviews. Unlike many existing datasets, it offers both sentiment labels and detailed aspect/topic annotations at the sentence level. This makes it particularly valuable for training and evaluating models in aspect-based sentiment analysis (ABSA), topic modeling, and for benchmarking. A key feature of HRAST is the inclusion of a substantial subset of sentences expressing contradicting sentiments across different aspects, presenting a significant challenge for ABSA models that process overall sentiment without isolating individual aspects. The dataset fills a critical gap in benchmark resources for the hospitality sector and is fully annotated by one human annotator and one expert annotator to ensure consistency and quality.

Context: The dataset was originally introduced by Andreou et al. (2023) to support research in aspect-based sentiment analysis and topic modeling. It was created from user-generated hotel reviews sourced from Booking.com, covering 42 hotels in four European cities: Naples, Salzburg, Barcelona, and Copenhagen. The hospitality sector was chosen due to the strong influence of user-generated reviews on consumer decision-making and hotel competitiveness.

Data Collection: The dataset was manually collected through a crowdsourcing approach by students enrolled in the Collective Intelligence course (CIS 473) at the Cyprus University of Technology. Each student was assigned a hotel listing on Booking.com and tasked with gathering 500 positive and 500 negative reviews written in English, each containing at least two sentences. Students then split the reviews into individual sentences, recorded them in Excel, and independently annotated each sentence for sentiment—positive, negative, or neutral (factual). Additionally, they labeled each sentence with one or more topics, based either on predefined Booking.com categories (such as Staff, Cleanliness, Comfort, Facilities, Location, and Value for Money) or on self-suggested topics reflecting other aspects mentioned in the reviews. In total, 16,813 reviews were collected from 42 hotels located in four European cities: Naples, Salzburg, Barcelona, and Copenhagen.

Structure and Content: Each entry represents a review sentence with a unique ID and the sentence text (review). Sentiment is labeled across three mutually exclusive columns: positive, negative, and neutral. Each sentence is also annotated for the presence of hotel-related topics, including Clean, Comfort, Facilities/Amenities, Location, Restaurant (dinner), Staff, View (Balcony), Breakfast, Room, Pool, Beach, Bathroom/Shower (toilet), Bar, Bed, Parking, Noise, Reception-checkin, Lift, Value for money, Wi-Fi, and Generic. These are binary indicators where sentences can be linked to multiple aspects simultaneously. The Aspect column signals whether the sentence contains any aspect-related content.

Usage: The dataset supports model training, validation, and benchmarking for aspect-based sentiment analysis, topic modeling, and sentiment analysis in hospitality user-generated reviews.

Citations / Credits: - Tsapatsoulis, N., Voutsa, M.C., & Djouvas, C. (2025). Biased by Design? Evaluating LLM Annotation Performance for Real-World and Synthetic Hotel Reviews. AI , forthcoming. And the original source: Andreou, C., Tsapatsoulis, N., & Anastasopoulou, V. (2023, September). A Dataset of Hotel Reviews for Aspect-Based Sentiment Analysis and Topic Modeling. In 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP) 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023) (pp. 1-9). IEEE. Licensing: CC BY-NC 4.0

📊 Supplement Sales Data (2020–2025) Overview This dataset contains weekly sales data for a variety of health and wellness supplements from January 2020 to April 2025. The data includes products in categories like Protein, Vitamins, Omega, and Amino Acids, among others, and covers multiple e-commerce platforms such as Amazon, Walmart, and iHerb. The dataset also tracks sales in several locations including the USA, UK, and Canada.

Dataset Details Time Range: January 2020 to April 2025

Frequency: Weekly (Every Monday)

Number of Rows: 4,384

Columns:

Date: The week of the sale.

Product Name: The name of the supplement (e.g., Whey Protein, Vitamin C, etc.).

Category: The category of the supplement (e.g., Protein, Vitamin, Omega).

Units Sold: The number of units sold in that week.

Price: The selling price of the product.

Revenue: The total revenue generated (Units Sold * Price).

Discount: The discount applied on the product (as a percentage of original price).

Units Returned: The number of units returned in that week.

Location: The location of the sale (USA, UK, or Canada).

Platform: The e-commerce platform (Amazon, Walmart, iHerb).

Use Cases This dataset is ideal for:

Time-series forecasting and sales trend analysis 📈

Price vs. demand analysis and revenue prediction 📊

Sentiment analysis and impact of promotions (Discounts) on sales 🛍️

Product performance tracking across different platforms and locations 🛒

Business optimization in the health and wellness e-commerce sector 💼

Potential Applications Build predictive models to forecast future sales 📅

Analyze the effectiveness of discounts and promotions 💸

Create recommendation systems for supplement products 🧠

Perform exploratory data analysis (EDA) and uncover trends 🔍

Model return rates and their effect on overall revenue 📉

Why This Dataset? This dataset provides an excellent starting point for those interested in building business intelligence tools, e-commerce forecasting models, or exploring health & wellness trends. It also serves as a perfect dataset for data science learners looking to apply regression, time-series analysis, and predictive modeling techniques.

The Dairy Goods Sales Dataset provides a detailed and comprehensive collection of data related to dairy farms, dairy products, sales, and inventory management. This dataset encompasses a wide range of information, including farm location, land area, cow population, farm size, production dates, product details, brand information, quantities, pricing, shelf life, storage conditions, expiration dates, sales information, customer locations, sales channels, stock quantities, stock thresholds, and reorder quantities.

Features:

1. Location: The geographical location of the dairy farm.
2. Total Land Area (acres): The total land area occupied by the dairy farm.
3. Number of Cows: The number of cows present in the dairy farm.
4. Farm Size: The size of the dairy farm(in sq.km).
5. Date: The date of data recording.
6. Product ID: The unique identifier for each dairy product.
7. Product Name: The name of the dairy product.
8. Brand: The brand associated with the dairy product.
9. Quantity (liters/kg): The quantity of the dairy product available.
10. Price per Unit: The price per unit of the dairy product.
11. Total Value: The total value of the available quantity of the dairy product.
12. Shelf Life (days): The shelf life of the dairy product in days.
13. Storage Condition: The recommended storage condition for the dairy product.
14. Production Date: The date of production for the dairy product.
15. Expiration Date: The date of expiration for the dairy product.
16. Quantity Sold (liters/kg): The quantity of the dairy product sold.
17. Price per Unit (sold): The price per unit at which the dairy product was sold.
18. Approx. Total Revenue (INR): The approximate total revenue generated from the sale of the dairy product.
19. Customer Location: The location of the customer who purchased the dairy product.
20. Sales Channel: The channel through which the dairy product was sold (Retail, Wholesale, Online).
21. Quantity in Stock (liters/kg): The quantity of the dairy product remaining in stock.
22. Minimum Stock Threshold (liters/kg): The minimum stock threshold for the dairy product.
23. Reorder Quantity (liters/kg): The recommended quantity to reorder for the dairy product.

Potential Use-Case:

This dataset can be used by researchers, analysts, and businesses in the dairy industry for various purposes, such as:

1. Analyzing the performance of dairy farms based on location, land area, and cow population.
2. Understanding the sales and distribution patterns of different dairy products across various brands and regions.
3. Studying the impact of storage conditions and shelf life on the quality and availability of dairy products.
4. Analyzing customer preferences and buying behavior based on location and sales channels.
5. Optimizing inventory management by tracking stock quantities, minimum thresholds, and reorder quantities.
6. Conducting market research and trend analysis in the dairy industry.
7. Developing predictive models for demand forecasting and pricing strategies.

Note: This dataset includes data from the period between 2019 and 2022, and it specifically focuses on selected dairy brands operating in specific states and union territories of India. There is an intentional drift highlighted in the dataset's figures due to its opensource and creative license, currently !

his dataset provides comprehensive insights into the operational and revenue performance of a hotel throughout the year 2024. It includes detailed records of daily operations, revenue figures, guest demographics, booking sources, economic indicators, and more. Key features encompass:

Date: The date of the recorded data. Month: Numeric representation of the month. Day of the Week: Numeric representation of the day in a week. Season: Categorical representation of the season (e.g., Winter, Spring, Summer, Fall). Public Holiday: Binary indicator (0 or 1) denoting whether it's a public holiday. Previous Month Revenue: Revenue generated in the previous month. Year-over-Year Revenue: Revenue compared to the same month the previous year. Monthly Trend: Trend in revenue or occupancy for the month. Occupancy Rate: Percentage of rooms occupied. Average Daily Rate (ADR): Average rate charged per occupied room. Revenue per Available Room (RevPAR): Revenue generated per available room. Booking Lead Time: Average lead time between booking and stay. Booking Cancellations: Percentage of bookings cancelled. Booking Source: Source of the booking (e.g., Direct, OTA). Guest Type: Type of guest (e.g., Leisure, Business). Repeat Guests: Percentage of guests who are repeat visitors. Nationality: Nationality of guests. Group Bookings: Binary indicator denoting group bookings. Discounts and Promotions: Use of discounts or promotions. Room Rate: Average rate charged for rooms. Local Events: Presence of local events influencing occupancy. Hotel Events: Events hosted by the hotel affecting operations. Competitor Rates: Rates offered by competitors. Weather Conditions: Local weather conditions influencing guest behavior. Economic Indicators: Economic factors influencing hotel performance. Staff Levels: Staffing levels affecting service quality. Guest Satisfaction: Guest satisfaction ratings. Maintenance Issues: Issues related to maintenance affecting operations. Marketing Spend: Expenditure on marketing activities. Online Reviews: Ratings and reviews provided online. Social Media Engagement: Engagement metrics on social media platforms. Seasonal Adjustments: Adjustments made for seasonal variations. Trend Adjustments: Adjustments made for trending factors. Room Revenue: Total revenue from room bookings. Food and Beverage Revenue: Revenue from food and beverage services. Other Services Revenue: Revenue from other hotel services. Total Revenue for the Month: Overall revenue generated for the month.

This company operates in the retail food sector, mainly serving meats, wines, fruits, fish, and sweets. They have an app for food delivery. The current financial outlook does not look good for them, hence they want to improve the performance of marketing activities. The new campaign aims at selling a new gadget to its existing customers. Before they launched this, a pilot campaign was conducted to random customers, resulting in 15% success rate, and a revenue of 3.674 imaginary currency with -3.046 imaginary currency profit.

This dataset is the result of the pilot campaign. It provides information about campaign results, customers' marketing campaign activities, purchase activities, app activities, and customer demographic information.

Case Objective: Which customers should be targeted to maximize the profit?

Column dictionary: - cid : customer ID - response : last campaign offer accepted status (1 for yes else 0) - acceptedcmp1 : 1st campaign offer accepted status (1 for yes else 0) - acceptedcmp2 : 2nd campaign offer accepted status (1 for yes else 0) - acceptedcmp3 : 3rd campaign offer accepted status (1 for yes else 0) - acceptedcmp4 : 4th campaign offer accepted status (1 for yes else 0) - acceptedcmp5 : 5th campaign offer accepted status (1 for yes else 0) - acceptedcmpoverall : Total accepted campaign from campaign 1 to 5 (1 to 5) - recency: days since last purchase - numdealspurchases: number of purchases with discount - numwebpurchases: number of purchases from web - numcatalogpurchases : Number of purchases made using the catalogue.
- numstorepurchases : Number of purchases directly from stores. - mntwines : amount spent on wines in the last 2 years. - mntfruits : amount spent on fruits in the last 2 years. - mntmeatproducts : amount spent on meats in the last 2 years. - mntfishproducts : amount spent on fishes in the last 2 years. - mntsweetproducts : amount spent on sweets in the last 2 years. - mntgoldprods : amount spent on gold products in the last 2 years. - mntregularprods : amount spent on regular products in the last 2 years.
- mnttotal : Total amount spent in the last 2 years. - numwebvisitsmonth : number of web visits last month - customer_days : days since customer onboarded - complain : did customers file a complaint in the last two years? (1 yes or 0 no) - age : customer's age - income : customer's income - kidhome : number of dependent kids - teenhome : number of dependent teens - marital_status : marital status - education : last education - cost : cost to acquire one customer (normalized) - revenue : revenue acquired from one customer (normalized)

Original Reference: kaggle dataset, github repo