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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
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Water sampling is an essential undertaking for water utilities and agencies to protect and enhance our natural resources. The high variability in water quality, however, often necessitates a spatially distributed sampling program which is impeded by high-cost and large sampling devices. This paper presents the BoSL FAL Pump - a low-cost, easily constructed, 3D-printed peristaltic pump which can be made from commonly available components and is sized to suit even the most space constrained installations. The pump is 38mm in height and 28mm in diameter, its components cost $16 AUD and the construction time is just 12 minutes (excluding 3D printing times). The pump is driven by a direct current motor which is commonly available, cheap and allows for flexibility in the energy supply (5-12 V). Optionally, the pump has a Hall effect sensor and magnet to detect rotation rates and pumping volumes to improve the accuracy of pumping rates/volumes. The pump can be easily controlled by commonly available microcontrollers, as demonstrated by this paper which implements the ATmega328P on the Arduino Uno R3. This paper validates the pump for long-term deployments at flow rates of up to 13mL per minute in 0.14mL volume increments at accuracy levels of greater than 99%. The pump itself is scalable, allowing for a wider range of pumping rates when, for example, large volume samples are required for pathogen and micropollutant detection.
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Discover the average cost of laying engineered wood flooring, including material and labor expenses ranging from $4 to $16 per square foot. Learn about factors affecting these costs and tips for budgeting effectively in our comprehensive guide.
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BackgroundPrimary healthcare (PHC) systems attain improved health outcomes and fairness and are affordable. However, the proportion of PHC spending to Total Current Health Expenditure in Kenya reduced from 63.4% in 2016/17 to 53.9% in 2020/21 while external funding reduced from 28.3% (Ksh 69.4 billion) to 23.9% (Ksh 68.2 billion) over the same period. This reduction in PHC spending negatively affects PHC performance and the overall health system goals.MethodsWe conducted a cost-benefit analysis and computed costs against the economic benefits of a PHC scale-up. Activity-Based Costing (ABC) on the provider perspective was employed to estimate the incremental costs. The OneHealth Tool was used to estimate the health impact of operationalizing PHC over five years. Finally, we quantified Return on Investment (ROI) by estimating monetized DALYs based on a constant value per statistical life year (VSLY) derived from a VSL estimate.ResultsThe total projected cost of PHC interventions in the Kenya was Ksh 1.65 trillion (USD 15,581.91 billion). Human resource was the main cost driver accounting for 75% of the total cost. PHC investments avert 64,430,316 Disability Adjusted Life-Years (DALYs) and generate cost savings of Ksh. 21.5 trillion (USD 204.4 Billion) over five years. Shifting services from high-level facilities to PHC facilities generates Ksh 198.2 billion (USD 1.9 billion) and yields a benefit-cost ratio of 16:1 in 5 years. Thus, every $1 invested in PHC interventions saves up to $16 in spending on conditions like stunting, NCDs, anaemia, TB, Malaria, and maternal and child health morbidity.ConclusionsEvidence of the economic benefits of continued prioritization of funding for PHC can strengthen the advocacy argument for increased domestic and external financing of PHC in Kenya. A well-resourced and functional PHC system translates to substantial health benefits with positive economic benefits. Therefore, governments and stakeholders should increase investments in PHC to accelerate economic growth.
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Explore the cost factors influencing engineered wood flooring, including types, quality, and installation complexities, with prices ranging from $4 to $16 per square foot installed.
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Explore the costs and benefits of composite decking, a durable and low-maintenance alternative to traditional wood, with prices ranging from $9 to $16 per square foot. Understand how material quality, brand, design, and location influence cost, and discover long-term savings potential despite higher initial expenses.
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Nautilus reported $16M in Selling and Administration Expenses for its fiscal quarter ending in September of 2023. Data for Nautilus | NLS - Selling And Administration Expenses including historical, tables and charts were last updated by Trading Economics this last June in 2025.
In 2024, Intel's research and development expenditure equated to ***** billion U.S. dollars, up slightly from the ***** billion U.S. dollars recorded in the previous year.
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Explore the cost, durability, and aesthetic appeal of engineered wood flooring in 2023, with prices ranging from $3 to $16 per square foot influenced by factors like material quality and wear layer thickness. Discover the best options for different home spaces, including considerations for installation and long-term value.
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Comscore reported $16M in Debt for its fiscal quarter ending in June of 2023. Data for Comscore | SCOR - Debt including historical, tables and charts were last updated by Trading Economics this last May in 2025.
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FreightCar America reported $16M in Trade Debtors for its fiscal quarter ending in June of 2024. Data for FreightCar America | RAIL - Trade Debtors including historical, tables and charts were last updated by Trading Economics this last May in 2025.
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Ball reported $16B in Market Capitalization this June of 2025, considering the latest stock price and the number of outstanding shares.Data for Ball | BLL - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last June in 2025.
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Vornado Realty reported $16B in Assets for its fiscal quarter ending in December of 2024. Data for Vornado Realty | VNO - Assets including historical, tables and charts were last updated by Trading Economics this last June in 2025.
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Huntsman reported $16M in EBIT for its fiscal quarter ending in March of 2025. Data for Huntsman | HUN - Ebit including historical, tables and charts were last updated by Trading Economics this last June in 2025.
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Ardelyx reported $16M in Trade Creditors for its fiscal quarter ending in December of 2024. Data for Ardelyx | ARDX - Trade Creditors including historical, tables and charts were last updated by Trading Economics this last June in 2025.
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YPF reported $16M in Interest Income for its fiscal quarter ending in March of 2025. Data for YPF - Interest Income including historical, tables and charts were last updated by Trading Economics this last June in 2025.
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Explore the cost factors of insulated metal roof panels, ranging from $7 to $16 per square foot, and learn how materials, location, and energy efficiency influence pricing and savings.
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Learn about the growing demand for household and sanitary paper products in the Middle East and the projected market trends for the next decade.
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The revenue of the coffee and tea market in the U.S. amounted to $15.9B in 2018, picking up by 3.9% against the...
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The global market for illuminated signs is set to experience growth over the next six years, with an expected increase in market volume and value by 2030.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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