Restaurant Sales Dataset with Dirt Documentation

Overview

The Restaurant Sales Dataset with Dirt contains data for 17,534 transactions. The data introduces realistic inconsistencies ("dirt") to simulate real-world scenarios where data may have missing or incomplete information. The dataset includes sales details across multiple categories, such as starters, main dishes, desserts, drinks, and side dishes.

Dataset Use Cases

This dataset is suitable for: - Practicing data cleaning tasks, such as handling missing values and deducing missing information. - Conducting exploratory data analysis (EDA) to study restaurant sales patterns. - Feature engineering to create new variables for machine learning tasks.

Columns Description

Key Characteristics

1. Data Dirtiness:

   • Missing values in key columns (Item, Price, Quantity, Order Total, Payment Method) simulate real-world challenges.
   • At least one of the following conditions is ensured for each record to identify an item:
     • Item is present.
     • Price is present.
     • Both Quantity and Order Total are present.
   • If Price or Quantity is missing, the other is used to deduce the missing value (e.g., Order Total / Quantity).

2. Menu Categories and Items:

   • Items are divided into five categories:
     • Starters: E.g., Chicken Melt, French Fries.
     • Main Dishes: E.g., Grilled Chicken, Steak.
     • Desserts: E.g., Chocolate Cake, Ice Cream.
     • Drinks: E.g., Coca Cola, Water.
     • Side Dishes: E.g., Mashed Potatoes, Garlic Bread.

3 Time Range: - Orders span from January 1, 2022, to December 31, 2023.

Cleaning Suggestions

1. Handle Missing Values:

   • Fill missing Order Total or Quantity using the formula: Order Total = Price * Quantity.
   • Deduce missing Price from Order Total / Quantity if both are available.

2. Validate Data Consistency:

   • Ensure that calculated values (Order Total = Price * Quantity) match.

3. Analyze Missing Patterns:

   • Study the distribution of missing values across categories and payment methods.

Menu Map with Prices and Categories

This notebook focuses on cleaning and exploring a raw sales dataset provided by a local fashion brand. I performed:

Data cleaning (nulls, types, duplicates)

EDA (distribution, correlation)

Visualizations using Matplotlib, Seaborn, and Plotly

📁 Dataset Information

This dataset was provided by a fashion retail company and contains raw sales data used for cleaning, exploration, and visualization.

File Name: Train_csv.py.csv
Number of Rows: 10,000 (approx.)
Number of Columns: 12
File Format: CSV

MB "Clean Up LT" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Dataset Description

This dataset is a collection of customer, product, sales, and location data extracted from a CRM and ERP system for a retail company. It has been cleaned and transformed through various ETL (Extract, Transform, Load) processes to ensure data consistency, accuracy, and completeness. Below is a breakdown of the dataset components: 1. Customer Information (s_crm_cust_info)

This table contains information about customers, including their unique identifiers and demographic details.

Columns:

  cst_id: Customer ID (Primary Key)

  cst_gndr: Gender

  cst_marital_status: Marital status

  cst_create_date: Customer account creation date

Cleaning Steps:

  Removed duplicates and handled missing or null cst_id values.

  Trimmed leading and trailing spaces in cst_gndr and cst_marital_status.

  Standardized gender values and identified inconsistencies in marital status.

1. Product Information (s_crm_prd_info / b_crm_prd_info)

This table contains information about products, including product identifiers, names, costs, and lifecycle dates.

Columns:

  prd_id: Product ID

  prd_key: Product key

  prd_nm: Product name

  prd_cost: Product cost

  prd_start_dt: Product start date

  prd_end_dt: Product end date

Cleaning Steps:

  Checked for duplicates and null values in the prd_key column.

  Validated product dates to ensure prd_start_dt is earlier than prd_end_dt.

  Corrected product costs to remove invalid entries (e.g., negative values).

1. Sales Details (s_crm_sales_details / b_crm_sales_details)

This table contains information about sales transactions, including order dates, quantities, prices, and sales amounts.

Columns:

  sls_order_dt: Sales order date

  sls_due_dt: Sales due date

  sls_sales: Total sales amount

  sls_quantity: Number of products sold

  sls_price: Product unit price

Cleaning Steps:

  Validated sales order dates and corrected invalid entries.

  Checked for discrepancies where sls_sales did not match sls_price * sls_quantity and corrected them.

  Removed null and negative values from sls_sales, sls_quantity, and sls_price.

1. ERP Customer Data (b_erp_cust_az12, s_erp_cust_az12)

This table contains additional customer demographic data, including gender and birthdate.

Columns:

  cid: Customer ID

  gen: Gender

  bdate: Birthdate

Cleaning Steps:

  Checked for missing or null gender values and standardized inconsistent entries.

  Removed leading/trailing spaces from gen and bdate.

  Validated birthdates to ensure they were within a realistic range.

1. Location Information (b_erp_loc_a101)

This table contains country information related to the customers' locations.

Columns:

  cntry: Country

Cleaning Steps:

  Standardized country names (e.g., "US" and "USA" were mapped to "United States").

  Removed special characters (e.g., carriage returns) and trimmed whitespace.

1. Product Category (b_erp_px_cat_g1v2)

This table contains product category information.

Columns:

  Product category data (no significant cleaning required).

Key Features:

Customer demographics, including gender and marital status

Product details such as cost, start date, and end date

Sales data with order dates, quantities, and sales amounts

ERP-specific customer and location data

Data Cleaning Process:

This dataset underwent extensive cleaning and validation, including:

Null and Duplicate Removal: Ensuring no duplicate or missing critical data (e.g., customer IDs, product keys).

Date Validations: Ensuring correct date ranges and chronological consistency.

Data Standardization: Standardizing categorical fields (e.g., gender, country names) and fixing inconsistent values.

Sales Integrity Checks: Ensuring sales amounts match the expected product of price and quantity.

This dataset is now ready for analysis and modeling, with clean, consistent, and validated data for retail analytics, customer segmentation, product analysis, and sales forecasting.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

UAB City Service Cleaning financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Market Overview:

The global Semiconductor Wafer Cleaning Systems market is expected to grow from USD 1.02 Billion in 2017 to USD 1.48 Billion by 2030, at a CAGR of 4.5% from 2017 to 2030. The growth of the market can be attributed to the increasing demand for semiconductor devices across the globe and rising investments in the semiconductor industry. In addition, technological advancements in wafer cleaning systems are also contributing to the growth of this market.

Product Definition:

Semiconductor wafer cleaning systems sales is the term used to describe the sale of semiconductor wafer cleaning systems. These systems are used in the manufacture of semiconductors and are critical to ensuring that quality products are produced.

Single-Wafer Processing Systems:

Single-wafer processing systems are used for manufacturing semiconductor devices. The single wafer is processed through all the steps such as polishing, cleaning, and deposition of a thin film to produce multiple products in one go. These systems are majorly used by companies that have high production volumes and need to reduce their capital investment in cleanrooms and equipment.

Auto Wet Stations:

Auto wet stations are used in semiconductor wafer cleaning systems to clean the surface of a wafer by bringing it into contact with liquid. The liquid used can be water or some other type of fluid such as alcohol, acetone, etc. Auto wet stations have an advantage over manual wetting systems as they provide a uniform and controlled flow rate across the entire wafer surface which is not possible with hand-operated devices.

Scrubbers:

A scrubber is a device used in semiconductor manufacturing to clean the surface of wafers. It uses a fluid and abrasive mixture to remove contaminants from the surface of the wafer.

Application Insights:

The global demand for semiconductor wafer cleaning systems is expected to witness growth on account of the growing demand for chips from various end-use industries. The memory manufacturers are anticipated to dominate the application segment over the forecast period owing to increasing production levels of dynamic random access memory (DRAM) and flash memory. IDMs were estimated as the largest segment in 2014 and are projected to grow at a CAGR exceeding 7% from 2022 to 2030. Growing demand for integrated circuits owing to technological advancements coupled with rapid urbanization is anticipated fuel growth over the forecast period. Integrated circuit manufacturing involves the use of several materials such as polycrystalline silicon, metal layers, insulating layers, and dielectric layers which leads to contamination during the manufacturing process resulting in the need for a Semiconductor Wafer Cleaning System installation at IDM plants globally.

Regional Analysis:

The Asia Pacific is expected to be the fastest-growing regional market over the forecast period. The growth can be attributed to increasing demand for electronic products in countries, such as China and India. In addition, the growing semiconductor industry in Taiwan and South Korea is also anticipated to drive product demand over the next eight years. The Middle East & Africa region accounted for a revenue share of more than 7% in 2021 owing to the rapid development of the electronics manufacturing sector mainly driven by rising investments from foreign players like Intel Corporation; Samsung Electronics Co., Ltd.; Micron Technology Inc.; and SK Hynix Inc. Moreover, increasing government support for the establishment of new foundries will boost regional growth further over the forecast period. For instance, Saudi Arabia plans on establishing its own foundry by 2030 with an investment worth SAR 200 million (USD X million).

Growth Factors:

• Increasing demand for semiconductor devices in consumer electronics and automotive industries.
• The growing trend of miniaturization of semiconductor devices.
• The proliferation of 3D printing technology.
• Rising demand for advanced packaging technologies.
• The increasing number of fabless companies.

Report Scope

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SHL Telemedicine Ltd American Depositary Shares is assigned short-term B1 & long-term Ba3 estimated rating.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The RevOps Data Automation Solution market is experiencing robust growth, driven by the increasing need for businesses to optimize their revenue operations. The market's expansion is fueled by several key factors. Firstly, the escalating complexity of sales and marketing data necessitates automated solutions for efficient data integration, cleaning, and analysis. This allows revenue teams to gain actionable insights faster and make more informed decisions. Secondly, the rising adoption of cloud-based solutions and the growing preference for data-driven decision-making are significantly contributing to market growth. Companies are increasingly leveraging data automation to enhance sales forecasting accuracy, improve lead scoring, and streamline sales processes. Finally, the demand for personalized customer experiences is driving investment in solutions that enable better customer segmentation and targeted marketing campaigns, further boosting market adoption. We estimate the 2025 market size to be approximately $5 billion, based on industry analysis of similar SaaS solutions and considering a conservative CAGR of 20% from a hypothetical 2019 base of $1 Billion. Despite the significant growth potential, the market faces some challenges. The high initial investment cost for implementing data automation solutions can be a barrier to entry for smaller businesses. Furthermore, the need for skilled personnel to manage and interpret the data generated by these solutions poses a significant hurdle. The complexity of integrating these solutions with existing CRM and marketing automation platforms also presents an obstacle. However, the long-term benefits of improved efficiency, enhanced data quality, and data-driven decision-making outweigh these challenges. The market is expected to continue its upward trajectory, with ongoing innovation and the emergence of more user-friendly and affordable solutions driving wider adoption across various industry verticals. By 2033, we project a market size significantly exceeding $20 billion, fueled by continuous technological advancements and expanding market penetration.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Short/Long Term Stocks: LON:PMG Stock Forecast

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The Global Automotive Cleaning Products Market size is expected to grow from USD 9.8 billion in 2021 to USD XX billion by 2028, at a CAGR of 5.1% during the forecast period. The growth of this market can be attributed to the increasing demand for automotive cleaning products from department stores and supermarkets, automotive parts stores, and online retailers.

Automotive Cleaning Products is a term used for a wide range of products that are designed to clean and protect vehicles. This can include car washes, waxes, polishes, and more. Automotive cleaning products can be purchased from a variety of retailers, both in-store and online. There are many different types of automotive cleaning products available on the market. Some of the most popular types include Car Wash Shampoo, car wheel cleaner, car bug, insect remover, and car screenwash. Each type of product has its own unique set of benefits and features.

On the basis of Type, the market is segmented into Car Wash Shampoo, Car Wheel Cleaner, Car Bug and Insect Remover, and Car Screenwash. Among these, the Car Wash Shampoo segment is expected to account for the largest market share during the forecast period.

Car Wash Shampoo:

Car Wash Shampoo is a detergent used to clean automobiles. It is usually a thick, white liquid that is poured onto the car and then scrubbed in with a brush. Car Wash Shampoo contains surfactants that help it to loosen dirt, grease, and grime from the car's surface. The shampoo is then rinsed off with water. Car wash shampoos contain ingredients that are specifically designed to clean cars without causing any damage. Car wash shampoo is available in both liquid and powder form.

The Automotive Wash Shampoo Sales segment has been witnessing significant growth due to the increasing number of vehicles on the road and the rising consumer awareness about vehicle maintenance. As more consumers recognize the importance of maintaining their vehicles' appearance and performance, the demand for high-quality wash shampoos has surged. These products not only enhance the aesthetic appeal of vehicles but also contribute to their longevity by removing dirt and grime that can lead to corrosion. The convenience of purchasing automotive wash shampoos through various retail channels, including online platforms, has further fueled their sales, making them a staple in the automotive cleaning products market.

Car Wheel Cleaner:

A car wheel cleaner is a liquid or gel designed to clean the wheels of a car. It is usually applied using a brush and then rinsed off with water. Wheel cleaner can be used on alloy wheels, chrome wheels, and wire wheels. Some cleaners are also suitable for use on brake discs and pads. The wheel cleaner is available in both wet and dry forms. The wet form is typically a liquid, while the dry form is a powder or gel that must be mixed with water before use. Wheel cleaner comes in a variety of colors, including clear, blue, green, and yellow.

Car Bug and Insect Remover:

Car Bug and Insect Remover is a type of automotive cleaning product that is used to remove bugs, insects, and other debris from the exterior of a car. It can be applied using a spray bottle or other dispensing method, and often comes in concentrated form so that it can be diluted with water as needed. Car bug and insect removers typically contain harsh chemicals that can damage paint if not used properly.

On the basis of Region, the market is segmented into (North America, Latin America, Europe, Asia Pacific, and Middle East & Africa. North America is expected to dominate the global automotive cleaning products market during the forecast period. Europe is the second-largest region in the automotive cleaning products market. The Asia Pacific is projected to be the fastest-growing market during the forecast period.

Growth Factors For The Global Automotive Cleaning Products Market:

• Rising demand for automobiles across the globe
• The increasing popularity of do-it-yourself (DIY) car care methods among consumers
• The growing awareness about car care and detailing
• The increasing number of retail outlets selling automotive cleaning products is also contributing to the growth of this market.
• Stringent government regulations pertaining to environmental protection

These and other factors are expected to drive the global automotive cleaning products marke

This dataset contains historical sales data for chocolate products across various countries and sales representatives. It is designed to support retail sales analysis and business intelligence use cases such as performance tracking, sales forecasting, and market trend identification.

🔢 Key Columns: Column Description Sales Person Name of the salesperson who made the sale Country Country where the sale was made Product Type of chocolate product sold Date Date of the transaction Amount Sales value in currency (cleaned to decimal) Boxes Shipped Quantity of chocolate boxes sold and shipped

📊 Key Use Cases: Sales Forecasting – Predict future revenue based on past sales data

Performance Tracking – Analyze top-performing salespeople and countries

Product Analysis – Identify best-selling chocolate products

Geographic Insights – Compare regional sales across markets

Inventory Planning – Estimate product demand using historical trends

📌 Tools Used: Power BI Desktop for data modeling, cleaning, and dashboard visualization

DAX for calculations (Total Sales, Average Sales, Forecasting, etc.)

Power Query for data transformation and cleaning

The electric cleaning brush market, encompassing corded, rechargeable, and battery-powered models for online and offline sales, is experiencing robust growth. While precise market size figures for 2025 are unavailable, a reasonable estimate, considering the presence of major players like Black+Decker, Bissell, and Hoover, and a projected CAGR (let's assume a conservative 8% based on industry trends for similar products), suggests a global market valuation of approximately $2.5 billion in 2025. This growth is driven by increasing consumer demand for efficient and convenient cleaning solutions, coupled with rising disposable incomes in developing economies. The shift towards cordless and rechargeable models is a prominent trend, driven by improved battery technology and enhanced portability. However, factors such as the relatively higher initial cost of electric brushes compared to manual ones and potential concerns about battery life and environmental impact act as market restraints. The market is segmented by application (online vs. offline sales) and type (corded, rechargeable, battery-powered), with the rechargeable segment likely holding the largest share due to its balance of convenience and affordability. Regional variations exist, with North America and Europe anticipated to hold significant market shares initially, followed by growth in the Asia-Pacific region driven by increasing urbanization and rising middle-class spending. Future growth will likely be fueled by technological advancements, such as improved battery life, more powerful motors, and the incorporation of smart features. The competitive landscape is characterized by a mix of established brands like Black+Decker and Hoover, alongside emerging players. Successful strategies for companies involve focusing on product innovation, building strong brand recognition, and expanding distribution channels, particularly through e-commerce platforms. Growth will likely also depend on addressing concerns related to sustainability and developing environmentally friendly cleaning solutions. Long-term projections suggest the market will continue its upward trajectory, with a potential market valuation exceeding $4 billion by 2033, if the CAGR maintains a steady growth trajectory. This is fueled by a continuous rise in demand for time-saving appliances and a growing preference for hygiene. Further segmentation by specific cleaning applications (e.g., bathroom, kitchen) and the introduction of specialized brush heads will also contribute to market expansion.

Market Overview:

The Pool Cleaning Machines market is projected to grow at a CAGR of 5.5% from 2022 to 2030. The growth in the market can be attributed to the increasing demand for robotic Pool Cleaners, suction pool cleaners, and Pressure Pool Cleaners across the globe. In terms of type, the robotic pool cleaner segment is projected to grow at a higher CAGR than other segments during the forecast period. This can be attributed to its growing popularity among consumers owing to its features such as automatic navigation and obstacle avoidance. In terms of application, the household segment is projected to account for a larger share of the global market than commercial applications during the forecast period.

Product Definition:

Pool cleaning machines are devices that use jets of water to clean pools. They come in different sizes and shapes and can be used with or without a filter. This type of machine is often used by homeowners who want to keep their pools clean and free from debris. This is a popular choice for people who have small pools or those who do not want to spend time cleaning the pool themselves.

Robotic Pool Cleaners:

Robotic pool cleaners are automatic devices, which are used to clean swimming pools. They work based on sensors and inflow water pressure. The robotic pool cleaner works with the help of a battery-operated pump, which removes dirt and leaves from the pool through an intake pipe. It has different cleaning cycles depending on water type (fresh or saltwater) and volume (small or large).

Suction Pool Cleaners:

Suction pool cleaners are used to remove leaves, debris, and other objects from the pool. It is a mechanical device that consists of a vacuum cleaner with an attached pump. The suction cleaner works by creating a vacuum around the surface of the water and then removing everything into the pump. This process is usually effective for small pools but may take longer in large pools as it has to go deeper to get more volume.

Pressure Pool Cleaners:

Pressure pool cleaners are a type of pool cleaning machine that uses high-pressure water to clean pools. They work by spraying a stream of water at high pressure over the surface of the pool, which helps to break down dirt and debris. This process is faster and more effective than traditional pool cleaning methods, such as scrubbing with a brush.

Application Insights:

The commercial application segment accounted for more than half of the market share in 2014 and is projected to witness significant growth over the forecast period. Commercial pool cleaning machines include robotic cleaners that can clean large pools with ease as they have been designed with a safety cover so that no fingers get stuck or injured. The suction cleaner is one of the most preferred methods used by pool owners for keeping their swimming pools sparkling clean regularly. It uses high-velocity water to remove dirt from deep within the pool walls without having to climb down into the water which saves time as well as reduces human efforts thus making it more cost-effective than other methods used before such as a manually done scrubbing process or using chemicals alone would have been.

Regional Analysis:

The Asia Pacific dominated the pool cleaning machines market in terms of revenue with a share of over 35.0% in 2019. This is attributed to increasing awareness regarding health and hygiene, along with the growing population, especially in China and India. Moreover, rising disposable income levels are projected to drive the regional market further. The Chinese government has taken several initiatives for maintaining public pools; these initiatives include regular maintenance schedules and providing necessary tools for efficient pool cleaning operations (cleaning chemicals).

Growth Factors:

• Increasing awareness about the benefits of using pool cleaning machines among consumers.
• The rising disposable income of consumers is enabling them to invest in high-quality pool cleaning machines.
• Growing demand for automatic pool cleaners from residential as well as commercial establishments.
• The proliferation of online retail channels is making it easier for consumers to purchase pool cleaning machines at competitive prices.

Report Scope

Market Overview:

The global pipe inspection and Cleaning Robot market is expected to grow at a CAGR of 7.5% from 2022 to 2030. The growth in the market can be attributed to the increasing demand for robots in the oil and gas industry, water industry, and other industries.

Product Definition:

A pipe inspection and cleaning robot is a remotely operated device that is used to clean and inspect the inside of pipes. The importance of pipe inspection and Cleaning Robots sales is that they allow for the safe and efficient cleaning of pipes without the need for someone to be in close proximity to the hazardous environment.

Wheel Type:

The wheel type is a part of the robot which helps in 360-degree scanning for detecting cracks, breaks, and corrosion. The growth factor in the market is increasing demand from end-user industries such as oil & gas, construction, and manufacturing sectors.

Tracked Type:

Tracked type is a method to represent the position of various components in a pipe network. It helps the robot navigate through pipes without causing any damage. The tracked types are made up of magnetic, optical, or Laser Sensors which help them follow the target path and reach the destination without any accidents.

Application Insights:

The oil and gas industry was the largest application segment in the global market for pipe inspection and cleaning robots, accounting for over 40% of the overall demand in 2015. The growth can be attributed to the increasing production of crude oil & natural gas liquids across major economies including Russia, Saudi Arabia, UAE, and China. Moreover, technological advancements such as the implementation of advanced software solutions coupled with the growing need to optimize on-site drilling operations are expected to drive demand further.

The water industry is the second largest application segment and is expected to grow during the forecast period. The growth can be attributed to the increasing demand for clean drinking water across developed economies, such as the US, UK, and Germany. Furthermore, technological advancements in robotics are expected to drive the adoption of pipe inspection and cleaning robots in this sector.

Regional Analysis:

North America was the leading market for pipe inspection and cleaning robots in 2015 with a share of over 38%. This can be attributed to factors such as high investment levels in oil & gas exploration & production (E&P) activities as well as stringent environmental regulations that favor the use of robotic technology for on-site inspections. Europe was the second largest market with a share of over 26%. This can be attributed to factors such as growing investments by major oil companies in this region along with stringent environmental regulations that favor the use of robotic technology for on-site inspections. The Asia Pacific was expected to witness significant growth during the forecast period owing to rising investments by key players in this region into automation technologies. The Middle East & Africa region is projected to witness significant growth over the forecast period owing to its flourishing oil & gas industry coupled with rising water treatment projects across various countries including Saudi Arabia and UAE.

Growth Factors:

• Increasing demand for pipe inspection and cleaning robots from the oil and gas industry as these robots help in reducing the chances of accidents and also improve the efficiency of operations.
• The growing popularity of robotic automation in various industries is expected to boost the demand for pipe inspection and cleaning robots over the forecast period.
• Rising awareness about the benefits offered by pipe inspection and cleaning robots is projected to propel their sales across different industrial sectors during the forecast period.
• Technological advancements in robotics are anticipated to create new opportunities for the growth of this market over the next few years.

Report Scope

UAB "Svea Cleaning Dag" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

MB "Cleaning services" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

UAB "Euro cleaning service" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Factori's Person API empowers businesses to enhance their contact database of Shopify and Klaviyo by enriching data. Simply input phone numbers, email addresses, hashed values, or name/company details, and receive comprehensive contact details in a standardized format. Fuel your marketing, sales, and customer relationship management activities with enriched contact information, including names, company details, job titles, contact information, social media profiles, and more. With optimized performance, robust error handling, and data security measures, Factori's Person API provides a seamless experience. Unlock valuable insights, personalize your outreach, and drive business growth effortlessly with Factori's Person API. Use Cases: Personalized Marketing: Enrich existing contact data with additional details such as social media profiles, educational background, or job titles. Tailor your marketing messages and campaigns to specific customer segments, improving personalization and engagement. Account-Based Marketing (ABM): Enhance your ABM strategy by enriching contact data of target accounts. Gain a comprehensive understanding of key stakeholders, their roles, and their preferences to deliver highly targeted and personalized campaigns. Sales Intelligence: Arm your sales team with enriched contact information to improve prospecting and sales conversations. Access valuable insights such as past experiences, interests, or industry expertise to establish meaningful connections and drive conversions. Data Cleansing and Validation: Ensure the accuracy and completeness of your contact database by enriching existing data with verified information. Update outdated or missing contact details, improving data quality and integrity. Market Research and Analysis: Enrich contact data to gain deeper insights into industry trends, job movements, or market dynamics. Analyze enriched data to identify patterns, opportunities, and market gaps for informed decision-making.

CompanyData.com, powered by BoldData, is your global partner for verified B2B company data sourced from official trade registers. Our Germany database features 6,531,436 verified company records, offering comprehensive insight into Europe’s largest economy.

Each German company profile includes detailed firmographic information, such as company name, Handelsregisternummer (registration number), legal form, industry classification (WZ codes), company size, turnover estimates, and number of employees. Many records also contain contact details, including executive names, direct emails, phone numbers, and mobile numbers when available.

Our German data is trusted by businesses across industries for a wide range of purposes — from KYC and AML compliance, risk analysis, and corporate due diligence, to sales prospecting, B2B marketing, CRM enrichment, and AI training. Whether you’re targeting Mittelstand firms, global enterprises, or startups in Berlin, Munich, or Hamburg, our data helps you reach the right businesses with precision.

We offer flexible delivery solutions to fit your needs — including tailored company lists, complete databases in Excel or CSV, real-time access through our API, and a self-service platform for on-demand data access. Our data enrichment and cleansing services can also upgrade and update your existing company datasets with fresh, verified German records.

With a global reach of 6,531,436 verified companies, CompanyData.com gives you the power to scale locally in Germany and globally across markets. Rely on our accurate, structured data to support smarter decisions, stronger outreach, and sustainable business growth.