This document discusses various regression techniques that can be used to predict housing prices based on different housing characteristics and features. It first provides background on housing price prediction and factors that influence prices. Then it describes several regression algorithms (hedonic pricing model, artificial neural networks, lasso regression, XGBoost) that can be used to predict prices. The document uses the Ames Housing dataset to test a lasso regression model and analyze impact of features like size, bedrooms, location on prices. The goal is to determine the most accurate advanced methodology for housing price prediction.
Machine Learning Based House Price Prediction Using Modified Extreme Boosting IIJSRJournal
In recent years, machine learning has become increasingly important in everyday voice commands and predictions. Instead, it provides a safer auto system and better customer assistance. As a result of all that has been demonstrated, ML is a technology that is becoming more and more popular in a range of industries. To gauge changes in house values, the House Price Index is frequently employed (HPI). Due to the substantial correlation that exists between property prices and other variables, such as location, region, and population, the HPI on its own is not sufficient to accurately forecast a person's house price. Some studies have successfully predicted house prices using conventional machine learning techniques, but they seldom evaluate the efficacy of different models and ignore the more complicated but less well-known models. We proposed Modified Extreme Gradient Boosting as our model in this study due to its adaptive and probabilistic model selection process. Feature engineering, hyperparameter training and optimization, model interpretation, and model selection and evaluation are all steps in the process. Home price indices, which are frequently used to support real estate policy initiatives and estimate housing costs. In this project, models for forecasting changes in home prices are developed using machine learning methods.
With China’s rapid economic growth in recent years and the acceleration of urbanization, the real estate price has
also shown a substantial increase, especially the housing prices have always been high in first-tier cities. This paper
systematically combs the research on the factors affecting house price and the forecasting method of house price at
home and abroad, and puts forward some suggestions on the regulation and control of real estate price in our
country. It also points out the deficiency of data statistics and research perspective in empirical research. On this
basis, it is proposed that we should strengthen the scientificalness and comprehensiveness of the empirical data, put
forward a reasonable and appropriate hierarchical classification method for influencing factors, make clear the
importance and coupling mechanism of each level, and excavate the dominant influencing factors.
Machine Learning Based House Price Prediction Using Modified Extreme Boosting IIJSRJournal
In recent years, machine learning has become increasingly important in everyday voice commands and predictions. Instead, it provides a safer auto system and better customer assistance. As a result of all that has been demonstrated, ML is a technology that is becoming more and more popular in a range of industries. To gauge changes in house values, the House Price Index is frequently employed (HPI). Due to the substantial correlation that exists between property prices and other variables, such as location, region, and population, the HPI on its own is not sufficient to accurately forecast a person's house price. Some studies have successfully predicted house prices using conventional machine learning techniques, but they seldom evaluate the efficacy of different models and ignore the more complicated but less well-known models. We proposed Modified Extreme Gradient Boosting as our model in this study due to its adaptive and probabilistic model selection process. Feature engineering, hyperparameter training and optimization, model interpretation, and model selection and evaluation are all steps in the process. Home price indices, which are frequently used to support real estate policy initiatives and estimate housing costs. In this project, models for forecasting changes in home prices are developed using machine learning methods.
With China’s rapid economic growth in recent years and the acceleration of urbanization, the real estate price has
also shown a substantial increase, especially the housing prices have always been high in first-tier cities. This paper
systematically combs the research on the factors affecting house price and the forecasting method of house price at
home and abroad, and puts forward some suggestions on the regulation and control of real estate price in our
country. It also points out the deficiency of data statistics and research perspective in empirical research. On this
basis, it is proposed that we should strengthen the scientificalness and comprehensiveness of the empirical data, put
forward a reasonable and appropriate hierarchical classification method for influencing factors, make clear the
importance and coupling mechanism of each level, and excavate the dominant influencing factors.
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
In this paper, we attempt to predict the price of a real estate individual homes sold in North West Indiana based on the individual homes sold in 2014. The data/information is collected from realtor.com. The purpose of this paper is to predict the price of individual homes sold based on multiple regression model and also utilize SAS forecasting model and software. We also determine the factors influencing housing prices and to what extent they affect the price. Independent variables such square footage, number of bathrooms, and whether there is a finished basement,. and whether there is brick front or not and the type of home: Colonial, Cotemporary or Tudor. How much does each type of home (Colonial, Contemporary, Tudor) add to the price of the real estate
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
In this paper, we attempt to predict the price of a real estate individual homes sold in North West Indiana based on the individual homes sold in 2014. The data/information is collected from realtor.com. The purpose of this paper is to predict the price of individual homes sold based on multiple regression model and also utilize SAS forecasting model and software. We also determine the factors influencing housing prices and to what extent they affect the price. Independent variables such square footage, number of bathrooms, and whether there is a finished basement,. and whether there is brick front or not and the type of home: Colonial, Cotemporary or Tudor. How much does each type of home (Colonial, Contemporary, Tudor) add to the price of the real estate
House Price Estimates Based on Machine Learning Algorithmijtsrd
Housing prices are increasing every year, necessitating the creation of a long term housing price strategy. Predicting a homes price will assist a developer in determining a homes purchase price, as well as a consumer in determining the best time to buy a home. The sale price of real estate in major cities depends on the specific circumstances. Housing prices are constantly changing from day to day and are sometimes fired rather than based on estimates. Predicting real estate prices by real factors is a key element as part of our analysis. We want to make our test dependent on all of the simple metrics that are taken into account when deciding the significance. In this research we use linear regression techniques pathway and our results are not self inflicted process rather is a weighted method of various techniques to give the most accurate results. There are fifteen features in the data collection. In this research. There has been an effort to build a forecasting model for determining the price based on the variables that influence the price.The results have proven to be effective lower error and higher accuracy than individual algorithms are used. Jakir Khan | Dr. Ganesh D "House Price Estimates Based on Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42367.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42367/house-price-estimates-based-on-machine-learning-algorithm/jakir-khan
Factors influencing the rise of house price in klang valleyeSAT Journals
Abstract
There is an increase of house price radically in Klang Valley that affect to Malaysian house buyer. House price is the value to be paid for the dealing of buying a residential property. House price rises continuously respecting few factors and had impacting house buyer in decision to buy their house. This study becomes necessary since there is less research that gives information in the factors influencing the rise of house price. The factors are found out through detailed literature reviews and information from pilot study. Pilot study is conducted through interviewing representative from National House Buyer Association, pioneer in solving house related problem, to provide legal suggestion and etc. The data is collected via questionnaire survey form distributed to respondents in sample area. The sample area is Klang Valley region, 10 municipal districts including Kuala Lumpur, the Capital City. In result and analysis stages, the factors had to be refined by analyzing the data using statistical tests. Every single factors are calculated its average index respect to few level of influence under respondents’ opinion. The index will then treated as influencing level of the factors. Based on the study, fluctuation in housing market, increasing in construction cost, population growth and increasing demand are factors which give major influence to rise of house price. The study also identified housing criteria to be considered during setup of house selling price and also preference among house buyer nowadays. This study also identified cost contributors in construction being foresees as control measure concerned in respect to respondents point of view.
Keywords: House price, affordable, and construction cost
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Model Proposed for the Prediction of Future Sustainable Residence Specifica...IEREK Press
In Egypt, people are unable to determine the qualities of appropriate residence that achieves quality and occupant satisfaction, and contributes to sustainability of residential conglomerations. In general, developing countries lack housing information which can be used to enhance quality of residence. Also, the methods of assessing and identifying the appropriate criteria for future residence quality remain traditional ones that cannot address the multiple, conflicting, overlapping aspects to reach a good decision. This calls for using the Analytical Network Process (ANP), an effective tool for specifying the relativeimportance of all factors impacting a specific issue for making an appropriate residential decision. In addition, this method provides results for the decision element impacts network within the decision structure; thus contributing to more understanding of the mechanisms and requirements of residence selection. The proposed decision structure comprises a two-level network: main clusters, main elements, and sub-elements included in the demographic characteristics group, the residence criteria group, the demand parameters group, the supply parameters group, the residence specifications group, and the alternatives group which representing, in total, the decision and specifying the percentage needed for each housing level. Results of the model showed complete capacity in smoothly addressing complexities and overlapping in the decision structure. The decision structure showed that 52% chose luxury residence, 28% chose middle-class residence, and 19.5% chose the economic residence. Mechanisms of decision making were analyzed; particularly in terms of relationship to demographic characteristics and residence specifications. Also, the importance and impact of demand / supply parameters in reaching decision were analyzed.
A Model Proposed for the Prediction of Future Sustainable Residence Specifica...IEREK Press
In Egypt, people are unable to determine the qualities of appropriate residence that achieves quality and occupant satisfaction, and contributes to sustainability of residential conglomerations. In general, developing countries lack housing information which can be used to enhance quality of residence. Also, the methods of assessing and identifying the appropriate criteria for future residence quality remain traditional ones that cannot address the multiple, conflicting, overlapping aspects to reach a good decision. This calls for using the Analytical Network Process (ANP), an effective tool for specifying the relative importance of all factors impacting a specific issue for making an appropriate residential decision. In addition, this method provides results for the decision element impacts network within the decision structure; thus contributing to more understanding of the mechanisms and requirements of residence selection. The proposed decision structure comprises a two-level network: main clusters, main elements, and sub-elements included in the demographic characteristics group, the residence criteria group, the demand parameters group, the supply parameters group, the residence specifications group, and the alternatives group which representing, in total, the decision and specifying the percentage needed for each housing level. Results of the model showed complete capacity in smoothly addressing complexities and overlapping in the decision structure. The decision structure showed that 52% chose luxury residence, 28% chose middle-class residence, and 19.5% chose the economic residence. Mechanisms of decision making were analyzed; particularly in terms of relationship to demographic characteristics and residence specifications. Also, the importance and impact of demand / supply parameters in reaching decision were analyzed
An Evaluation of the Impact of Government Assisted Housing Programmes (GAHPs)...theijes
Housing investment has been considered as a major contributor to economic growth with many social and economic benefits. This research work was carried out to evaluate the impact of Government Assisted Housing Programmes (GAHPs) in Bauchi State. The methodology employed were Oral interview, Self-assessment, Checklist and Questionnaires. Two target groups were considered for this research: the first group was construction professionals such as Architects, Engineers, Quantity surveyors, Builders and Service Engineers involved in the construction of such housing programmes, while the second group was family heads/occupants of government housing programmes. The research was conducted using completed Tambari housing estate and 1000-units of two bedrooms semi-detached / three bedrooms under construction at Dungal as case studies. The information obtained from these case studies were analysed using relative index, linear correlation, regression and one - way ANOVA (Analysis of variance). The research revealed that the first five critical impact of housing programmes were employment for skilled and unskilled workers (RI = 0.77); increase demand for building materials market (RI =0. 75); Generates income and employment in a number of related /non-related industry (RI = 0.75); increases income to land owners due to compensation paid for land acquisition (RI=0.74) and attracts infrastructural facilities to such localities (RI = 0.72). It also revealed that most of the respondents were satisfied with the impact of GAHPs within their vicinity.
Mortgage Default, Property Price and Banks’ Lending Behaviour in Hong Kong SAR.Fawaz Khaled
MORTGAGE DEFAULT, PROPERTY PRICE AND BANKS’ LENDING BEHAVIOUR IN HONG KONG SAR is a research presented in the 9th International Conference on Computational and Financial Econometrics. December 13th 2015.
The aim of this paper is to measure the influences of location, price and service quality on a
house purchase decisions by measure whether location, price and service quality have significantly influence on
purchase decision. This paper uses SPSS (Statistical Product and Service Solutions)
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
In this paper, we attempt to predict the price of a real estate individual homes sold in North West Indiana based on the individual homes sold in 2014. The data/information is collected from realtor.com. The purpose of this paper is to predict the price of individual homes sold based on multiple regression model and also utilize SAS forecasting model and software. We also determine the factors influencing housing prices and to what extent they affect the price. Independent variables such square footage, number of bathrooms, and whether there is a finished basement,. and whether there is brick front or not and the type of home: Colonial, Cotemporary or Tudor. How much does each type of home (Colonial, Contemporary, Tudor) add to the price of the real estate
Multiple Linear Regression Applications in Real Estate Pricinginventionjournals
In this paper, we attempt to predict the price of a real estate individual homes sold in North West Indiana based on the individual homes sold in 2014. The data/information is collected from realtor.com. The purpose of this paper is to predict the price of individual homes sold based on multiple regression model and also utilize SAS forecasting model and software. We also determine the factors influencing housing prices and to what extent they affect the price. Independent variables such square footage, number of bathrooms, and whether there is a finished basement,. and whether there is brick front or not and the type of home: Colonial, Cotemporary or Tudor. How much does each type of home (Colonial, Contemporary, Tudor) add to the price of the real estate
House Price Estimates Based on Machine Learning Algorithmijtsrd
Housing prices are increasing every year, necessitating the creation of a long term housing price strategy. Predicting a homes price will assist a developer in determining a homes purchase price, as well as a consumer in determining the best time to buy a home. The sale price of real estate in major cities depends on the specific circumstances. Housing prices are constantly changing from day to day and are sometimes fired rather than based on estimates. Predicting real estate prices by real factors is a key element as part of our analysis. We want to make our test dependent on all of the simple metrics that are taken into account when deciding the significance. In this research we use linear regression techniques pathway and our results are not self inflicted process rather is a weighted method of various techniques to give the most accurate results. There are fifteen features in the data collection. In this research. There has been an effort to build a forecasting model for determining the price based on the variables that influence the price.The results have proven to be effective lower error and higher accuracy than individual algorithms are used. Jakir Khan | Dr. Ganesh D "House Price Estimates Based on Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42367.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42367/house-price-estimates-based-on-machine-learning-algorithm/jakir-khan
Factors influencing the rise of house price in klang valleyeSAT Journals
Abstract
There is an increase of house price radically in Klang Valley that affect to Malaysian house buyer. House price is the value to be paid for the dealing of buying a residential property. House price rises continuously respecting few factors and had impacting house buyer in decision to buy their house. This study becomes necessary since there is less research that gives information in the factors influencing the rise of house price. The factors are found out through detailed literature reviews and information from pilot study. Pilot study is conducted through interviewing representative from National House Buyer Association, pioneer in solving house related problem, to provide legal suggestion and etc. The data is collected via questionnaire survey form distributed to respondents in sample area. The sample area is Klang Valley region, 10 municipal districts including Kuala Lumpur, the Capital City. In result and analysis stages, the factors had to be refined by analyzing the data using statistical tests. Every single factors are calculated its average index respect to few level of influence under respondents’ opinion. The index will then treated as influencing level of the factors. Based on the study, fluctuation in housing market, increasing in construction cost, population growth and increasing demand are factors which give major influence to rise of house price. The study also identified housing criteria to be considered during setup of house selling price and also preference among house buyer nowadays. This study also identified cost contributors in construction being foresees as control measure concerned in respect to respondents point of view.
Keywords: House price, affordable, and construction cost
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A Model Proposed for the Prediction of Future Sustainable Residence Specifica...IEREK Press
In Egypt, people are unable to determine the qualities of appropriate residence that achieves quality and occupant satisfaction, and contributes to sustainability of residential conglomerations. In general, developing countries lack housing information which can be used to enhance quality of residence. Also, the methods of assessing and identifying the appropriate criteria for future residence quality remain traditional ones that cannot address the multiple, conflicting, overlapping aspects to reach a good decision. This calls for using the Analytical Network Process (ANP), an effective tool for specifying the relativeimportance of all factors impacting a specific issue for making an appropriate residential decision. In addition, this method provides results for the decision element impacts network within the decision structure; thus contributing to more understanding of the mechanisms and requirements of residence selection. The proposed decision structure comprises a two-level network: main clusters, main elements, and sub-elements included in the demographic characteristics group, the residence criteria group, the demand parameters group, the supply parameters group, the residence specifications group, and the alternatives group which representing, in total, the decision and specifying the percentage needed for each housing level. Results of the model showed complete capacity in smoothly addressing complexities and overlapping in the decision structure. The decision structure showed that 52% chose luxury residence, 28% chose middle-class residence, and 19.5% chose the economic residence. Mechanisms of decision making were analyzed; particularly in terms of relationship to demographic characteristics and residence specifications. Also, the importance and impact of demand / supply parameters in reaching decision were analyzed.
A Model Proposed for the Prediction of Future Sustainable Residence Specifica...IEREK Press
In Egypt, people are unable to determine the qualities of appropriate residence that achieves quality and occupant satisfaction, and contributes to sustainability of residential conglomerations. In general, developing countries lack housing information which can be used to enhance quality of residence. Also, the methods of assessing and identifying the appropriate criteria for future residence quality remain traditional ones that cannot address the multiple, conflicting, overlapping aspects to reach a good decision. This calls for using the Analytical Network Process (ANP), an effective tool for specifying the relative importance of all factors impacting a specific issue for making an appropriate residential decision. In addition, this method provides results for the decision element impacts network within the decision structure; thus contributing to more understanding of the mechanisms and requirements of residence selection. The proposed decision structure comprises a two-level network: main clusters, main elements, and sub-elements included in the demographic characteristics group, the residence criteria group, the demand parameters group, the supply parameters group, the residence specifications group, and the alternatives group which representing, in total, the decision and specifying the percentage needed for each housing level. Results of the model showed complete capacity in smoothly addressing complexities and overlapping in the decision structure. The decision structure showed that 52% chose luxury residence, 28% chose middle-class residence, and 19.5% chose the economic residence. Mechanisms of decision making were analyzed; particularly in terms of relationship to demographic characteristics and residence specifications. Also, the importance and impact of demand / supply parameters in reaching decision were analyzed
An Evaluation of the Impact of Government Assisted Housing Programmes (GAHPs)...theijes
Housing investment has been considered as a major contributor to economic growth with many social and economic benefits. This research work was carried out to evaluate the impact of Government Assisted Housing Programmes (GAHPs) in Bauchi State. The methodology employed were Oral interview, Self-assessment, Checklist and Questionnaires. Two target groups were considered for this research: the first group was construction professionals such as Architects, Engineers, Quantity surveyors, Builders and Service Engineers involved in the construction of such housing programmes, while the second group was family heads/occupants of government housing programmes. The research was conducted using completed Tambari housing estate and 1000-units of two bedrooms semi-detached / three bedrooms under construction at Dungal as case studies. The information obtained from these case studies were analysed using relative index, linear correlation, regression and one - way ANOVA (Analysis of variance). The research revealed that the first five critical impact of housing programmes were employment for skilled and unskilled workers (RI = 0.77); increase demand for building materials market (RI =0. 75); Generates income and employment in a number of related /non-related industry (RI = 0.75); increases income to land owners due to compensation paid for land acquisition (RI=0.74) and attracts infrastructural facilities to such localities (RI = 0.72). It also revealed that most of the respondents were satisfied with the impact of GAHPs within their vicinity.
Mortgage Default, Property Price and Banks’ Lending Behaviour in Hong Kong SAR.Fawaz Khaled
MORTGAGE DEFAULT, PROPERTY PRICE AND BANKS’ LENDING BEHAVIOUR IN HONG KONG SAR is a research presented in the 9th International Conference on Computational and Financial Econometrics. December 13th 2015.
The aim of this paper is to measure the influences of location, price and service quality on a
house purchase decisions by measure whether location, price and service quality have significantly influence on
purchase decision. This paper uses SPSS (Statistical Product and Service Solutions)
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.