Datasets using R-Studio
Usha Rani Singh
1
Datasets for cars
Dataset is a collection of related information which is useful to analyze data and derive the outputs
The dataset contains information in various forms, and it isn't straightforward for the analyzer to extract the data and present it to the business
2
Preparing Dataset for cars
Preparing and analyzing the dataset is very important for any threat information, which helps to provide accurate data
We have to consider the data which provide more value or relevant for the problem
Categorize the data into regression, classification, clustering, and ranking
It is difficult to establish data collection mechanism and data is scattered into various forms and departments
We have to make consistency in the data
Data sample has been reduced, and at the same time it should consist of the required information
Preparing Dataset for cars
We have to clean the data so that the processing time will be faster and accurate
Complex datasets have to be decomposed into multiple parts
Data normalization has to be performed to improve the quality of the data
R Studio for dataset
Pie Diagram for data set
ggplot_1 for dataset
ggplot_2 for data set
ggplot_3 for dataset
Dataset used
Thank you!
11
Microsoft
Excel Worksheet
Microsoft
Excel Worksheet
Week 10 – Analysing Data sets in RapidMiner
The data sets used for this weeks analysis relates to the CSRIC best practices:
The CSRIC Best Practices Search Tool allows you to search CSRIC's collection of Best Practices using a variety of criteria including Network Type, Industry Role, Keywords, Priority Levels, and BP Number. The Communications Security, Reliability and Interoperability Council's (CSRIC) mission is to provide recommendations to the FCC to ensure, among other things, optimal security and reliability of communications systems, including telecommunications, media, and public safety. CSRIC’s members focus on a range of public safety and homeland security-related communications matters, including: (1) the reliability and security of communications systems and infrastructure, particularly mobile systems; (2) 911, Enhanced 911 (E911), and Next Generation 911 (NG911); and (3) emergency alerting.
The CSRIC's recommendations will address the prevention and remediation of detrimental cyber events, the development of best practices to improve overall communications reliability, the availability and performance of communications services and emergency alerting during natural disasters, terrorist attacks, cyber security attacks or other events that result in exceptional strain on the communications infrastructure, the rapid restoration of communications services in the event of widespread or major disruptions and the steps communications providers can take to help secure end-users and servers.
I have used RapidMiner to analyze the data se.
The company provides advanced analytics and data-driven decision making services. It has deep analytical capabilities across various industries, developed custom products, and has an expert team of data scientists, analysts, architects and programmers. The vision is to be a world leader in advanced analytics and enabling technology. Services include marketing, operations, supply chain and risk analytics. The company uses big data technologies like Hadoop and advanced tools to deliver solutions focused on customers across industries.
DBMS delivers info to the market about big data solutions. Presentation brings overview about possible data analysis for increasing sales and better customer service
1. The document discusses rational decision making and business intelligence. It defines rational decision making as selecting the optimal alternative based on analyzing past data and considering various performance criteria.
2. It describes the typical cycle of a business intelligence analysis as involving defining objectives, generating insights from data analysis, making decisions based on insights, and evaluating performance.
3. Key components of business intelligence architectures are data sources, data warehouses/marts for storing and processing data, and business intelligence tools for generating insights and supporting decision making.
Database Marketing, part two: data enhancement, analytics, and attribution Relevate
This document outlines the key steps in a database marketing roadmap, including data enhancement, smart profiling, predictive analytics, and campaign attribution and reporting. It discusses enhancing a database with external data and best practices for data hygiene. Smart profiling provides a descriptive view of a customer base by comparing to national averages. Predictive analytics uses past performance to predict future behavior through models. Attribution and reporting involves measuring campaign performance, analyzing results, and using insights to improve future efforts. The overall roadmap is designed to optimize customer interactions and marketing success.
This presentation looks at the different sources of data that will help to inform Senior Executives about the current quality of IT services overall and help make the right decisions about future IT investment priorities?
This document summarizes a research paper that predicts customer churn using logistic regression with regularization and optimization techniques. The paper applies these techniques to predict churn customers in the banking, e-commerce, and telecom sectors. It first discusses customer relationship management (CRM) and how data mining can be used for customer churn prediction. Then, it describes logistic regression and how the proposed method adds regularization and optimization to improve accuracy. The method is tested on datasets from the three sectors to classify customers as churners or non-churners. The paper finds that adding regularization and optimization to logistic regression enhances its performance in customer churn prediction.
The company provides advanced analytics and data-driven decision making services. It has deep analytical capabilities across various industries, developed custom products, and has an expert team of data scientists, analysts, architects and programmers. The vision is to be a world leader in advanced analytics and enabling technology. Services include marketing, operations, supply chain and risk analytics. The company uses big data technologies like Hadoop and advanced tools to deliver solutions focused on customers across industries.
DBMS delivers info to the market about big data solutions. Presentation brings overview about possible data analysis for increasing sales and better customer service
1. The document discusses rational decision making and business intelligence. It defines rational decision making as selecting the optimal alternative based on analyzing past data and considering various performance criteria.
2. It describes the typical cycle of a business intelligence analysis as involving defining objectives, generating insights from data analysis, making decisions based on insights, and evaluating performance.
3. Key components of business intelligence architectures are data sources, data warehouses/marts for storing and processing data, and business intelligence tools for generating insights and supporting decision making.
Database Marketing, part two: data enhancement, analytics, and attribution Relevate
This document outlines the key steps in a database marketing roadmap, including data enhancement, smart profiling, predictive analytics, and campaign attribution and reporting. It discusses enhancing a database with external data and best practices for data hygiene. Smart profiling provides a descriptive view of a customer base by comparing to national averages. Predictive analytics uses past performance to predict future behavior through models. Attribution and reporting involves measuring campaign performance, analyzing results, and using insights to improve future efforts. The overall roadmap is designed to optimize customer interactions and marketing success.
This presentation looks at the different sources of data that will help to inform Senior Executives about the current quality of IT services overall and help make the right decisions about future IT investment priorities?
This document summarizes a research paper that predicts customer churn using logistic regression with regularization and optimization techniques. The paper applies these techniques to predict churn customers in the banking, e-commerce, and telecom sectors. It first discusses customer relationship management (CRM) and how data mining can be used for customer churn prediction. Then, it describes logistic regression and how the proposed method adds regularization and optimization to improve accuracy. The method is tested on datasets from the three sectors to classify customers as churners or non-churners. The paper finds that adding regularization and optimization to logistic regression enhances its performance in customer churn prediction.
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdasProf Dr Mehmed ERDAS
Big Data Analytics for TELCOs Customer Experience Management Permission Based Marketing for Location and Movement Data Data Modelling Business Use Cases Data Mining BSS OSS COTS OTT Churm Modeling Markov Processes HANA HADOOP INtegration Video Streaming Test cases
Enterprise systems help businesses achieve operational excellence by standardizing key business processes, integrating data across the organization, and providing valuable information for improved decision making. Supply chain management systems coordinate planning, production, and logistics with suppliers by sharing accurate and up-to-date information to reduce uncertainties. Customer relationship management systems help firms achieve customer intimacy through tools that enhance customer service, support marketing campaigns, and provide sales automation. Summit Electric implemented a new ERP system to address problems with its outdated system and improve operational efficiency, such as faster order processing and inventory management.
This document provides an overview of various business functions and how information systems support them. It discusses accounting, finance, engineering, supply chain management, customer relationship management, and human resource management. Information systems help with activities like inventory control, manufacturing scheduling, targeted marketing, and employee records management. The document also covers ethical issues around consumer privacy and the collection and use of personal data.
21st century has been defined by application of and advancement in information technology. Information technology has become an integral part of our daily life. According to Information Technology Association of America, information technology is defined as “the study, design, development, application, implementation, support or management of computer-based information systems.”
Information technology has served as a big change agent in different aspect of business and society. It has proven game changer in resolving economic and social issues.
Advancement and application of information technology are ever changing.
Gmid Associates provides analytics services including predictive modeling, descriptive analytics, data mining, and dashboard solutions. They have experience across industries including banking, insurance, and retail. Case studies highlighted include developing churn prediction models for a telecom company, sales forecasting for an apparel retailer, and implementing collection scorecards for a bank. Gmid aims to help clients make better data-driven decisions through analytics.
Tools and Techniques for Quality ManagementNazrul Islam
The tools and techniques most commonly used in Quality management and process improvement are: Cause and effect diagram. Control Charts. Histogram. Pareto Charts.
Chapter 10 Tools and Techniques for Quality Management.pptDr. Nazrul Islam
QMS have sub-elements, or tools, that enable users to tailor its use to specific project needs. There are seven conventional QMS tools: flow charts, Ishikawa diagrams, checklists, Pareto charts, histograms, scattergrams, and control charts.
Supply chain management involves coordinating all activities involved in procuring raw materials, manufacturing products, and distributing goods to meet market demand in the most cost-effective way. The objectives of SCM are to efficiently integrate suppliers, manufacturers, warehouses and stores to produce and deliver the right products, to the right locations, at the right time. SCM software, e-supply chains, and other approaches are used to manage supply chain activities and relationships to achieve competitive advantages.
IRJET- Credit Profile of E-Commerce CustomerIRJET Journal
This document proposes using RFM (Recency, Frequency, Monetary) variables and advanced k-means clustering to create positive and negative credit profiles for e-commerce customers. This will help minimize losses by identifying genuine versus fraudulent customers. The methodology calculates credit scores based on RFM and other factors. Advanced k-means clustering is then used to segment customers into clusters like excellent, good, average, and worst. Customers in different clusters will receive different benefits or restrictions based on their predicted reliability. The goal is to reduce losses from unwanted cancellations while retaining high value customers.
The document provides information about business analytics in different industries including business analytics, automotive analytics, FMCG analytics, and e-commerce analytics. It discusses key components of business analytics including data aggregation, data mining, association/sequence identification, and forecasting. For automotive analytics, it outlines use cases for predictive analytics, data from sensors for traffic and insurance, and cost/financial tracking. Top FMCG analytics uses cases include inventory optimization, forecast optimization, and price/promotion analytics. E-commerce analytics focuses on functions like supply chain management, merchant analytics, product analytics, online marketing, and user experience analytics.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Intelligent Shopping Recommender using Data MiningIRJET Journal
The document presents an intelligent shopping recommender system that uses data mining techniques. It analyzes customer purchase behavior data to provide personalized product recommendations and targeted offers. The proposed system aims to improve over traditional recommendation systems by focusing recommendations on individual customer interests and purchase histories rather than broad segments. It uses association rule mining on customer transaction data to identify patterns and predict customer tastes to provide more relevant recommendations and increased customer satisfaction compared to existing systems.
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET Journal
The document discusses implementing a social customer relationship management (CRM) system for an online grocery shopping platform using customer reviews. It proposes collecting customer reviews from social media and other sources, refining the data, analyzing it using natural language processing and machine learning techniques, and storing the results in a database. This would allow the platform to better understand customer sentiment and needs to improve products, services and the customer experience.
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET Journal
This document presents a proposed system architecture for implementing a social customer relationship management (CRM) system for an online grocery shopping platform using customer reviews and sentiment analysis. The proposed architecture involves collecting customer reviews from social media, preprocessing and analyzing the data using natural language processing techniques like stemming, and storing the results in a database. Sentiment analysis is performed to categorize reviews by aspects and sentiment. The analyzed data is then presented to users through an interface to help the online grocery shopping platform better understand customer needs and improve products/services based on feedback.
1. The document discusses how contact center managers currently struggle to track customer interactions across multiple vendor systems storing proprietary data.
2. It proposes using a big data platform like Esgyn to ingest real-time and historical data from various systems into a single "customer data lake" to better analyze customer journeys and behavior.
3. Esgyn provides a vendor-agnostic platform for building a contact center data lake to gain insights into customer experiences across different touchpoints.
I have been drinking from a virtual fire hose since joining my most recent technology company, Anametrix, a cloud-based digital analytics innovator. A whole new book opened for me on how digital analytics can both increase top line revenue and reduce spend by shining a very bright flashlight into marketing efforts.
We are all painfully aware of the data explosion problem. In 2011, the Gartner Group stated that information volume collected by businesses today is growing at a minimum 59% annually. The rapid adoption of social media has also caused customer data to explode in the last few years, creating entirely new challenges for marketers. It is now imperative for organizations to think differently to accommodate the variety, volume, and velocity of their growing customer-related data.
This is where my recent experiences come in: I have personally seen how digital analytics can harness the power of massive amounts customer-related data. It can literally simplify the accelerating complexity by providing deep visibility – as well as clarity – into the effectiveness of various marketing efforts, across both online and offline channels.
I will now outline the role of IT and CFO in adopting cloud-based digital analytics solutions, discuss the benefits as well as challenges of moving to this emerging category, and provide some illustrative examples on how digital analytics can transform your marketing organization.
This document provides an overview of supply chain management (SCM) and customer relationship management (CRM). It discusses how SCM involves collaboration between resellers and suppliers to deliver value to customers. CRM uses customer data to build stronger marketing programs and long-term customer relationships. The document argues that integrating SCM and CRM allows companies to achieve improvements in financial and performance metrics that would not be possible through standalone approaches. It provides details on the benefits and components of both SCM and CRM systems.
120. business intelligence modeling for increasing company value and competit...Hendry Hartono
This document discusses how business intelligence modeling can increase company value and competitive advantage in franchise businesses. It proposes a generic business intelligence model for franchise companies that involves collecting franchise data, performing extract-transform-load processes, and using data mining to make predictions about franchisor business strategies, franchisee performance, customer behaviors, and more. The model is meant to bridge gaps between business objectives and real activities by analyzing franchise data to inform decisions. Business intelligence is argued to benefit both franchisors and franchisees by providing insights that can improve business value and competitive positions.
Proposed ranking for point of sales using data mining for telecom operatorsijdms
This study helps telecom companies in making decisions that optimize its sales points to reduce costs, also
to identify profitable customers and churn ones. This study builds two research models; physical model for
continuous mining of database where ever it resides i.e., as we have On Line Analytic Processing (OLAP)
we must have On Line Data Mining (OLDM), and logical model using Technology Acceptance Model.
Previous Studies showed that using basic information of customers, call details and customer service
related data, a model can effectively achieve accurate prediction data.
This research gives a new definition and classification for telecommunication services from the data
mining point of view. Then this research proposed a formula for total rank a shop and each term of this
formula gives a sub rank. The proposed example shows that even a shop with lower numbers of population
and visitors, it still has higher rank.
This research suggested that telecom operators has to concentrate more on their e-shopping and epayment
as it is more cost effective and use data from shops for marketing issues. Some assumptions made
in this study need to be validated using surveys, also proposed ranking should be applied on live database.
Deadline 6 PM Friday September 27, 201310 Project Management Que.docxedwardmarivel
Deadline 6 PM Friday September 27, 2013
10 Project Management Questions with sub-questions under each question. A word document is provided with all questions and directions.
Problem 1
The following data were obtained from a project to create a new portable electronic.
Activity
Duration
Predecessors
A
5 Days
---
B
6 Days
---
C
8 Days
---
D
4 Days
A, B
E
3 Days
C
F
5 Days
D
G
5 Days
E, F
H
9 Days
D
I
12 Days
G
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
What is the Scheduled Completion of the Project?
b)
What is the Critical Path of the Project?
c)
What is the ES for Activity D?
d)
What is the LS for Activity G?
e)
What is the EF for Activity B?
f)
What is the LF for Activity H?
g)
What is the float for Activity I?
Problem 2
The following data were obtained from a project to build a pressure vessel:
Activity
Duration
Predecessors
A
6 weeks
---
B
6 weeks
---
C
5 weeks
B
D
4 weeks
A, C
E
5 weeks
B
F
7 weeks
D, E, G
G
4 weeks
B
H
8 weeks
F
I
5 weeks
G
J
3 week
I
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path
c)
What is the slack time (float) for activity A?
d)
What is the slack time (float) for activity D?
e) What is the slack time (float) for activity E?
f) What is the slack time (float) for activity G?
Problem 3
The following data were obtained from a project to design a new software package:
Activity
Duration
Predecessors
A
5 Days
---
B
8 Days
---
C
6 Days
A
D
4 Days
C, B
E
5 Days
A
F
4 Days
D, E, G
G
4 Days
B, C
H
3 Day
G
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path(s)
c)
What is the slack time (float) for activity B?
d)
What is the slack time (float) for activity D?
e) What is the slack time (float) for activity E?
f) What is the slack time (float) for activity G?
Problem 4
The following data were obtained from an in-house MIS project:
Activity
Duration
Predecessors
A
5 Days
---
B
8 Days
---
C
5 Days
A
D
4 Days
B
E
5 Days
B
F
3 Day
C, D
G
7 Days
C, D
H
6 Days
E, F, G
I
9 Days
E, F
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path
c)
What is the slack time (float) for activity A?
d)
What is the slack time (float) for activity D?
e)
What is the slack time (float) for activity E?
f)
What is the slack time (float) for activity F?
PROBLEM 5
Use the network diagram below and the additional information provided to answer the corresponding questions.
a) Give the crash cost per day per activity.
b) Which activities should be crash.
More Related Content
Similar to Datasets using R-StudioUsha Rani Singh.docx
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdasProf Dr Mehmed ERDAS
Big Data Analytics for TELCOs Customer Experience Management Permission Based Marketing for Location and Movement Data Data Modelling Business Use Cases Data Mining BSS OSS COTS OTT Churm Modeling Markov Processes HANA HADOOP INtegration Video Streaming Test cases
Enterprise systems help businesses achieve operational excellence by standardizing key business processes, integrating data across the organization, and providing valuable information for improved decision making. Supply chain management systems coordinate planning, production, and logistics with suppliers by sharing accurate and up-to-date information to reduce uncertainties. Customer relationship management systems help firms achieve customer intimacy through tools that enhance customer service, support marketing campaigns, and provide sales automation. Summit Electric implemented a new ERP system to address problems with its outdated system and improve operational efficiency, such as faster order processing and inventory management.
This document provides an overview of various business functions and how information systems support them. It discusses accounting, finance, engineering, supply chain management, customer relationship management, and human resource management. Information systems help with activities like inventory control, manufacturing scheduling, targeted marketing, and employee records management. The document also covers ethical issues around consumer privacy and the collection and use of personal data.
21st century has been defined by application of and advancement in information technology. Information technology has become an integral part of our daily life. According to Information Technology Association of America, information technology is defined as “the study, design, development, application, implementation, support or management of computer-based information systems.”
Information technology has served as a big change agent in different aspect of business and society. It has proven game changer in resolving economic and social issues.
Advancement and application of information technology are ever changing.
Gmid Associates provides analytics services including predictive modeling, descriptive analytics, data mining, and dashboard solutions. They have experience across industries including banking, insurance, and retail. Case studies highlighted include developing churn prediction models for a telecom company, sales forecasting for an apparel retailer, and implementing collection scorecards for a bank. Gmid aims to help clients make better data-driven decisions through analytics.
Tools and Techniques for Quality ManagementNazrul Islam
The tools and techniques most commonly used in Quality management and process improvement are: Cause and effect diagram. Control Charts. Histogram. Pareto Charts.
Chapter 10 Tools and Techniques for Quality Management.pptDr. Nazrul Islam
QMS have sub-elements, or tools, that enable users to tailor its use to specific project needs. There are seven conventional QMS tools: flow charts, Ishikawa diagrams, checklists, Pareto charts, histograms, scattergrams, and control charts.
Supply chain management involves coordinating all activities involved in procuring raw materials, manufacturing products, and distributing goods to meet market demand in the most cost-effective way. The objectives of SCM are to efficiently integrate suppliers, manufacturers, warehouses and stores to produce and deliver the right products, to the right locations, at the right time. SCM software, e-supply chains, and other approaches are used to manage supply chain activities and relationships to achieve competitive advantages.
IRJET- Credit Profile of E-Commerce CustomerIRJET Journal
This document proposes using RFM (Recency, Frequency, Monetary) variables and advanced k-means clustering to create positive and negative credit profiles for e-commerce customers. This will help minimize losses by identifying genuine versus fraudulent customers. The methodology calculates credit scores based on RFM and other factors. Advanced k-means clustering is then used to segment customers into clusters like excellent, good, average, and worst. Customers in different clusters will receive different benefits or restrictions based on their predicted reliability. The goal is to reduce losses from unwanted cancellations while retaining high value customers.
The document provides information about business analytics in different industries including business analytics, automotive analytics, FMCG analytics, and e-commerce analytics. It discusses key components of business analytics including data aggregation, data mining, association/sequence identification, and forecasting. For automotive analytics, it outlines use cases for predictive analytics, data from sensors for traffic and insurance, and cost/financial tracking. Top FMCG analytics uses cases include inventory optimization, forecast optimization, and price/promotion analytics. E-commerce analytics focuses on functions like supply chain management, merchant analytics, product analytics, online marketing, and user experience analytics.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Intelligent Shopping Recommender using Data MiningIRJET Journal
The document presents an intelligent shopping recommender system that uses data mining techniques. It analyzes customer purchase behavior data to provide personalized product recommendations and targeted offers. The proposed system aims to improve over traditional recommendation systems by focusing recommendations on individual customer interests and purchase histories rather than broad segments. It uses association rule mining on customer transaction data to identify patterns and predict customer tastes to provide more relevant recommendations and increased customer satisfaction compared to existing systems.
IRJET- Strength and Workability of High Volume Fly Ash Self-Compacting Concre...IRJET Journal
The document discusses implementing a social customer relationship management (CRM) system for an online grocery shopping platform using customer reviews. It proposes collecting customer reviews from social media and other sources, refining the data, analyzing it using natural language processing and machine learning techniques, and storing the results in a database. This would allow the platform to better understand customer sentiment and needs to improve products, services and the customer experience.
IRJET- Implementing Social CRM System for an Online Grocery Shopping Platform...IRJET Journal
This document presents a proposed system architecture for implementing a social customer relationship management (CRM) system for an online grocery shopping platform using customer reviews and sentiment analysis. The proposed architecture involves collecting customer reviews from social media, preprocessing and analyzing the data using natural language processing techniques like stemming, and storing the results in a database. Sentiment analysis is performed to categorize reviews by aspects and sentiment. The analyzed data is then presented to users through an interface to help the online grocery shopping platform better understand customer needs and improve products/services based on feedback.
1. The document discusses how contact center managers currently struggle to track customer interactions across multiple vendor systems storing proprietary data.
2. It proposes using a big data platform like Esgyn to ingest real-time and historical data from various systems into a single "customer data lake" to better analyze customer journeys and behavior.
3. Esgyn provides a vendor-agnostic platform for building a contact center data lake to gain insights into customer experiences across different touchpoints.
I have been drinking from a virtual fire hose since joining my most recent technology company, Anametrix, a cloud-based digital analytics innovator. A whole new book opened for me on how digital analytics can both increase top line revenue and reduce spend by shining a very bright flashlight into marketing efforts.
We are all painfully aware of the data explosion problem. In 2011, the Gartner Group stated that information volume collected by businesses today is growing at a minimum 59% annually. The rapid adoption of social media has also caused customer data to explode in the last few years, creating entirely new challenges for marketers. It is now imperative for organizations to think differently to accommodate the variety, volume, and velocity of their growing customer-related data.
This is where my recent experiences come in: I have personally seen how digital analytics can harness the power of massive amounts customer-related data. It can literally simplify the accelerating complexity by providing deep visibility – as well as clarity – into the effectiveness of various marketing efforts, across both online and offline channels.
I will now outline the role of IT and CFO in adopting cloud-based digital analytics solutions, discuss the benefits as well as challenges of moving to this emerging category, and provide some illustrative examples on how digital analytics can transform your marketing organization.
This document provides an overview of supply chain management (SCM) and customer relationship management (CRM). It discusses how SCM involves collaboration between resellers and suppliers to deliver value to customers. CRM uses customer data to build stronger marketing programs and long-term customer relationships. The document argues that integrating SCM and CRM allows companies to achieve improvements in financial and performance metrics that would not be possible through standalone approaches. It provides details on the benefits and components of both SCM and CRM systems.
120. business intelligence modeling for increasing company value and competit...Hendry Hartono
This document discusses how business intelligence modeling can increase company value and competitive advantage in franchise businesses. It proposes a generic business intelligence model for franchise companies that involves collecting franchise data, performing extract-transform-load processes, and using data mining to make predictions about franchisor business strategies, franchisee performance, customer behaviors, and more. The model is meant to bridge gaps between business objectives and real activities by analyzing franchise data to inform decisions. Business intelligence is argued to benefit both franchisors and franchisees by providing insights that can improve business value and competitive positions.
Proposed ranking for point of sales using data mining for telecom operatorsijdms
This study helps telecom companies in making decisions that optimize its sales points to reduce costs, also
to identify profitable customers and churn ones. This study builds two research models; physical model for
continuous mining of database where ever it resides i.e., as we have On Line Analytic Processing (OLAP)
we must have On Line Data Mining (OLDM), and logical model using Technology Acceptance Model.
Previous Studies showed that using basic information of customers, call details and customer service
related data, a model can effectively achieve accurate prediction data.
This research gives a new definition and classification for telecommunication services from the data
mining point of view. Then this research proposed a formula for total rank a shop and each term of this
formula gives a sub rank. The proposed example shows that even a shop with lower numbers of population
and visitors, it still has higher rank.
This research suggested that telecom operators has to concentrate more on their e-shopping and epayment
as it is more cost effective and use data from shops for marketing issues. Some assumptions made
in this study need to be validated using surveys, also proposed ranking should be applied on live database.
Similar to Datasets using R-StudioUsha Rani Singh.docx (20)
Deadline 6 PM Friday September 27, 201310 Project Management Que.docxedwardmarivel
Deadline 6 PM Friday September 27, 2013
10 Project Management Questions with sub-questions under each question. A word document is provided with all questions and directions.
Problem 1
The following data were obtained from a project to create a new portable electronic.
Activity
Duration
Predecessors
A
5 Days
---
B
6 Days
---
C
8 Days
---
D
4 Days
A, B
E
3 Days
C
F
5 Days
D
G
5 Days
E, F
H
9 Days
D
I
12 Days
G
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
What is the Scheduled Completion of the Project?
b)
What is the Critical Path of the Project?
c)
What is the ES for Activity D?
d)
What is the LS for Activity G?
e)
What is the EF for Activity B?
f)
What is the LF for Activity H?
g)
What is the float for Activity I?
Problem 2
The following data were obtained from a project to build a pressure vessel:
Activity
Duration
Predecessors
A
6 weeks
---
B
6 weeks
---
C
5 weeks
B
D
4 weeks
A, C
E
5 weeks
B
F
7 weeks
D, E, G
G
4 weeks
B
H
8 weeks
F
I
5 weeks
G
J
3 week
I
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path
c)
What is the slack time (float) for activity A?
d)
What is the slack time (float) for activity D?
e) What is the slack time (float) for activity E?
f) What is the slack time (float) for activity G?
Problem 3
The following data were obtained from a project to design a new software package:
Activity
Duration
Predecessors
A
5 Days
---
B
8 Days
---
C
6 Days
A
D
4 Days
C, B
E
5 Days
A
F
4 Days
D, E, G
G
4 Days
B, C
H
3 Day
G
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path(s)
c)
What is the slack time (float) for activity B?
d)
What is the slack time (float) for activity D?
e) What is the slack time (float) for activity E?
f) What is the slack time (float) for activity G?
Problem 4
The following data were obtained from an in-house MIS project:
Activity
Duration
Predecessors
A
5 Days
---
B
8 Days
---
C
5 Days
A
D
4 Days
B
E
5 Days
B
F
3 Day
C, D
G
7 Days
C, D
H
6 Days
E, F, G
I
9 Days
E, F
Step 1: Construct a network diagram for the project.
Step 2: Answer the following questions:
a)
Calculate the scheduled completion time.
b)
Identify the critical path
c)
What is the slack time (float) for activity A?
d)
What is the slack time (float) for activity D?
e)
What is the slack time (float) for activity E?
f)
What is the slack time (float) for activity F?
PROBLEM 5
Use the network diagram below and the additional information provided to answer the corresponding questions.
a) Give the crash cost per day per activity.
b) Which activities should be crash.
DEADLINE 15 HOURS
6 PAGES
UNDERGRADUATE
COURSEWORK
HARVARD FORMATING
DOUBLE SPACING
INSTRUCTIONS
This assignment seeks to assess your ability to:
• Critically evaluate and discuss the major developments during 2017 in corporate taxation from the perspective of multinational companies and their auditors, governments and other stakeholders.
• Apply appropriate knowledge, analytical techniques and concepts to problems and issues arising from both familiar and unfamiliar situations;
• Think critically, examine problems and issues from a number of perspectives, challenge viewpoints, ideas and concepts and make well-reasoned judgements;
• Present, discuss and defend ideas, concepts and views effectively through formal language.
Background:
In the final weeks of 2017 a leading tax expert suggested that “a whirlwind of international tax changes has swept the globe”. He also went on to say that for companies operating in Europe there is no end in sight to the pace of change. The final recommendations on base erosion and profit shifting (BEPS) from the OECD have been endorsed by the EU. In fact a number of European governments have already implemented large parts of these proposals ahead of schedule.
The third quarter of the year saw the European Commission in the spotlight with its landmark decision that the technology giant Apple must repay no less than €13 billion of taxes to the Irish government. This ruling was based on the view that the favourable tax treatment was effectively state aid and hence the Irish government had broken EU law. At the same time countries across the world continue to compete by reducing the rate of corporate taxes. Many commentators suggest that the UK government will cut the corporate tax rate to 10% if the country fails to negotiate a trade deal with the European Union as part of the Brexit process. In a separate development earlier in the year the government of Hungary announced it would become the tax haven of Central Europe with a plan to reduce corporation tax to a mere 9%.
Required:
You are to write a report for the Board of Directors of a listed global company that has manufacturing and R&D activities across Europe, Asia, Australasia and America. The report should assume that the directors have detailed knowledge of the group activities but are not taxation specialists. However they would be aware of issues relating to corporate governance, transparency and reputational risks.
The report should cover the following aspects:
Evaluate the major developments that occurred in corporate taxation in 2017 and the issues that may arise in the current year.
Discuss the implications for the group in regard to the relationship with its auditors.
Consider how other stakeholders and non-governmental organisations (NGOs) may be affected by changes in the level of corporate taxes and their possible reaction.
The resources below are on Blackboard and provide an introduction to the topic.
“Corpor.
De nada.El gusto es mío.Encantada.Me llamo Pepe.Muy bien, grac.docxedwardmarivel
Este documento presenta varios diálogos y conversaciones cortas que incluyen saludos comunes, preguntas sobre el origen y el nombre de las personas, y despedidas. Los diálogos practican vocabulario y estructuras básicas de conversación en español.
DDL 24 hours reading the article and writing a 1-page doubl.docxedwardmarivel
DDL:
24 hours
reading the article and writing a
1-page double space
annotated bibliography
including:
1.reference
2.specify the concept you will use
3.explain its significance to the course
4.specify how you'll use it in your project
see the article and project inf below
.
*
DCF valuation methodSuper-normal growth modelApplications: single CF, annuity, perpetuity, uneven CFs, bond, stock, etc.
LECTURE 2 Valuation Basics
(Chapters 4, 6, 7)
*
Amount of cash flows expectedRisk of the cash flows Timing of the cash flow stream
Factors that Determine Value
*
DCF Method: General Formula
Finding PVs is discounting. The discount factor i is determined by the cost of capital invested.
*
10%
Single Cash Flow
100
0
1
2
3
PV = ?
What’s the PV of $100 due in 3 years if i = 10%?
*
Financial Calculator Setup
BGN END
P/Y 1
FORMAT: DEC 4 or larger
*
Financial Calculator
Solution
s
N I/YR PV PMTFV
?
N = 3, I/YR = 10, PMT = 0, FV = 100
CPT, PV
-75.13
/
INPUTS
OUTPUT
*
Spreadsheet
.
DDBA 8307 Week 2 Assignment Exemplar
John Doe[footnoteRef:1] [1: Type your name here]
DDBA 8307-6[footnoteRef:2] [2: Type in DDBA section number (e.g. DDBA 8307 – 6) ]
Dr. Jane Doe[footnoteRef:3] [3: Enter faculty name here.]
1
Scales of Measurement
Type text here. Discuss the implications of “scales of measurement” in quantitative research. Be sure to use a minimum of two citations to support your position(s). Be sure to review the “Scales of Measurement” media from Week 1. This section should be no more than two paragraphs.
Research Question
What are the means, standard deviations, frequencies, and percentages of the Lesson 21 Exercise File variables?
Presentation of Findings
I analyzed data from Lesson 21 Exercise File [footnoteRef:4]. In this section, I present descriptive statistics for the study quantitative and qualitative variables. Appropriate APA tables and figures accompany the analysis[footnoteRef:5]. [4: Insert the appropriate file name. ] [5: The tables and figures from your SPSS output will need to be copied and pasted in the appropriate location.]
Descriptive Statistics[footnoteRef:6] [6: Detailed information can be found in Lesson 20, “Univariate Descriptive Statistics for Qualitative Variables,” and Lesson 21, “Univariate Descriptive Statistics for Quantitative Variables,” in the Green and Salkind text.
]
Descriptive statistics were run for the quantitative and qualitative variables in the Week 1 Assignment data set. Table 1 depicts the means and standard deviations for the quantitative data. Figure 1 depicts a histogram for the GPA variable. Table 2 depicts the frequencies and percentages for the qualitative (categorical) data. Figure 2 depicts a pie chart for the ethnic variable. Appendix 1 depicts the SPSS output.
Table 1[footnoteRef:7] [7: This is an example of an APA-formatted descriptive statistics table. Refer to Sections 5.01-5.19, in the APA Manual for detailed information on APA tables. The descriptive statistics table here includes the appropriate information derived from the SPSS output that is to be pasted as an appendix. Do not split tables across pages. Note: The numbers in the SPSS output presented here are fictitious numbers and do not represent correct numbers in the data set you will use for this application.
]
Means (M) and Standard Deviations (SD) for Study
Quantitative Variables (N = 105)
Variable[footnoteRef:8] [8: You would simply add rows to the table to accommodate the variables you have used in the analysis (i.e., variable 3, variable 4, etc.). Hint: Use the Microsoft Word Table feature.
]
M
SD
GPA
2.78
.76
Final
61.48
7.94
Percent
80.34
12.12
Figure 1. Histogram of GPA distribution.
Table 2[footnoteRef:9] [9: Recall from Lesson 20, “Univariate Descriptive Statistics for Qualitative Variables” (Green & Salkind, 2017), frequencies and percentages are reported for qualitative (nominal) variables. Note: Frequency and percentages are the only c.
DBM380 v14Create a DatabaseDBM380 v14Page 2 of 2Create a D.docxedwardmarivel
DBM/380 v14
Create a Database
DBM/380 v14
Page 2 of 2Create a Database
The following assignment is based on the business scenario for which you created both an entity-relationship diagram and a normalized database design in Week 2.
For this assignment, you will create multiple related tables that match your normalized database design. In other words, you will implement a physical design (an actual, usable database) based on a logical design.
Refer to the linked W3Schools.com articles “SQL CREATE TABLE Statement,” “SQL PRIMARY KEY Constraint,” “SQL FOREIGN KEY Constraint,” and “SQL INSERT INTO Statement” for help in completing this assignment.
Note: In the industry, even the most carefully thought out database designs can contain mistakes. Feel free to correct in your tables any mistakes you notice in your normalized database design. Also, note that in Microsoft® Access®, you follow the steps below to launch the SQL editor:
Figure 1. To create a SQL query in Microsoft® Access®, begin by clicking the CREATE tab.
To Complete This Assignment:
1. Use the CREATE TABLE statement to create each table in your design. Note that a table in a RDMS corresponds to an entity in an entity-relationship diagram. Recommended tables for this assignment are CUSTOMER, ORDER, ORDER_DETAIL, PRODUCT, EMPLOYEE, and STORE.
2. As part of each CREATE TABLE statement, define all of the columns, or fields, that you want each particular table to contain. Give them short, meaningful names and include constraints; that is, describe what type of data each column (field) is allowed to hold and any other constraints, such as size, range, or uniqueness.
3. Note that any field you marked as a unique identifier in your normalized database design is a key field. Key fields must be described as both UNIQUE and NOT NULL, which means a value must exist for each record and that value must be unique across all records.
4. After you have created all six tables, including relationships between the tables as appropriate (matching the primary key in one table to a foreign key in another table), use the INSERT INTO statement to insert 10 records into each of your tables. You will need to make up the data you insert into your tables. For example, to insert one record into the CUSTOMER table, you will need to invent a customer number, a customer name, and so on—one value for each of the fields you defined for the CUSTOMER table—to insert into the table.
5. To ensure that your INSERT INTO statements succeeded in populating your tables, use the SELECT statement described in Ch. 7, “Introduction to Structured Query Language,” in Database Systems: Design, Implementation, and Management.to retrieve the records you inserted. For example, to see all 10 records you inserted into the CUSTOMER table, you might apply the following SQL statement: SELECT * FROM CUSTOMER;
After you have created all six tables and populated ten records in each table, submit to the Assignment Files tab the database containin.
DB3.1 Mexico corruptionDiscuss the connection between pol.docxedwardmarivel
DB3.1: Mexico corruption
Discuss the connection between politics, corruption, and criminal organizations in Mexico. How would you go about separating these? Give examples and be specific. Support your ideas on why you would do these specific measures.
DB3.2: Collapse of Soviet Union
How has the collapse of the Soviet Union fostered pirate capitalism and organized crime? Be specific with your answer and support your answer. Do you think that if the Soviet Union did not collapse pirate capitalism and organized crime would still flourish? Support your opinion.
300 words per post
.
DB2Pepsi Co and Coke American beverage giants, must adhere to th.docxedwardmarivel
DB2
Pepsi Co and Coke American beverage giants, must adhere to the U.S Foreign Corruption Act wherever their businesses may take them. Both companies expanded their U.S businesses to India with differing initial results. Coke came home (initially) and Pepsi Co prospered.
Do your research and explain the socio-cultural barriers faced by these two companies? What in your view were the reasons which negatively impacted Coke and positively touched Pepsi Co?
WEEK 3:
Interactive
: Select one company other than the 2 mentioned above, and share this company’s experience in the United Arab Emirates. Comment on another learner’s company experience in a different location of the world.
WEEK 4:
Interactive
: Comment on a different learner’s company experience in a totally different location from those completed earlier. Do you feel that cultural training is an essential pre-requisite for expatriates in any host country? Why/Why not?
Remember to use APA referencing in the body of your posting.
.
DB1 What Ive observedHave you ever experienced a self-managed .docxedwardmarivel
DB1: What I've observed
Have you ever experienced a self-managed team? If so, describe it. If not, why do you think your organization has not embraced self managed teams?
DB2: Case Analysis
Review the case study at the end of Chapter 8, Frederick W. Smith - FedEx. Answer the five questions below:
1. How do the standards set by Fred Smith for FedEx teams improve organizational performance?
2. What motivates the members of FedEx to remain highly engaged in their teams?
3. Describe the role FedEx managers play in facilitating team effectiveness.
4. What types of teams does FedEx use? Provide evidence from the case to support your answer.
5. Leaders play a critical role in building effective teams. Cite evidence from the case that FedEx managers performed some of these roles in developing effective teams.
Image Source Team:
http://www.freedigitalphotos.net/images/gallery-thumbnails.php?id=50143103253525199427035558
.
DB Response 1I agree with the decision to search the house. Ther.docxedwardmarivel
DB Response 1
I agree with the decision to search the house. There was reasonable suspicion to believe the fugitive could have been in the home. The homeowner not only consented to the search of the house but requested it for her safety. Complacency kills. In this situation, the officer is very regretful in his decision to conduct a complacent search of the home, and luckily nobody was killed.
My department does not have body cameras, but I still conduct business as if somebody is recording me. We live in a generation of surveillance. You never know when there are hidden cameras, a camera on a business you did not notice, or a cell phone recording from the top floor of a building. We hire police officers with high amounts of integrity because the definition of integrity is doing the right thing even when nobody is looking. I would be lying if I said my grandmother would approve of everything I do on the job. I am most guilty of foul language and it is something that I am working on not doing that. However, I can emphatically say I work with integrity and honesty without a doubt.
I think setting limits on tolerable behavior in regards to sexual and general harassment is appropriate; however, there are too many situations to make a policy for every behavior one could find inappropriate. When it comes to using force again every situation is different but there should be a pretty well laid out policy at departments for when and how an officer should use a certain amount of force. Officers should be trained on de-escalation tactics and alternatives to using force. Tactical training should include strategies to create time, space, and distance, to reduce the likelihood that force will be necessary and should occur in realistic conditions appropriate to the department’s location (U.S. Commission On Civil Rights, 2018).
Philippians 2 verses 3 – 8 is a pretty straightforward verse with great leadership lessons. Be humble, put others before yourself, and be a servant leader.
From the very beginning of any interrogation, the accused has constitutional rights not to speak to police and also to have an attorney present. The Eighth Amendment to the Constitution prohibits cruel and unusual punishments placed upon any persons in the U.S. With these rights in mind I will only go as far as the Constitution allows when interrogating this suspect even if the suspect admits where the child is if the admission was coerced that admission could get thrown out of court. I would never compromise the investigation. There are other ways to find the abducted girl through detective work than just interrogating the suspect. The cost of illegal interrogations is documented in the number of lost prosecutions. Literally, thousands of cases across the country have had to be dismissed because prosecutors could not trust that the evidence provided by police officers was legitimate or the officer had lost credibility as a witness in all cases because of his or her wrongdoing (P.
DB Response prompt ZAKChapter 7, Q1.Customers are expecting.docxedwardmarivel
DB Response prompt ZAK
Chapter 7, Q1.
Customers are expecting more from their service providers. Rather than traditionally accepting boilerplate offerings from service providers, customers desire that service providers cater to their requests. Organizations providing services must keep up with the customer’s demand or risk losing business to others who will. Many service providers have been adopting lean principles to accommodate the needs of their customers in successful attempts to decrease waste, increase efficiency, improve customer service and satisfaction (Daft, 2016, p. 275). From online music providers, customers expect music tracks personalized for their tastes. From airlines, customers can expect preflight seat and meal selections. Amazon.com provides custom personalization to a customers’ home pages by placing personally directed advertisements and products which the customer is more likely to order from the company. Amazon book recommendations are personalized to the specific customer and are provided based upon previous books read. With customers expecting customized and catered experiences, companies need to keep up with this demand and embrace mass customization in order to obtain and retain customers.
Chapter 7, Q2.
While many facets of businesses may involve craft technology, it is still important for business schools to teach management. Some businesses which only expect their leaders to gain knowledge and expertise from experience, may be creating a bureaucratic and restricted model for their business. Companies which rely only on internal training for their leaders can miss opportunities from potential leaders coming in from the outside. Business schools which teach management can provide potential leaders with a foundation to draw from. Teaching management can expose students to issues and opportunities experienced by others, not just ones restricted to one specific company. Teaching management from a textbook is just one method of conveying information. Just as one would not necessarily be proficient in piloting a boat from reading a book, a textbook about doing so would provide the student with underlying concepts which could dramatically increase the success of the student when they move to an actual boat. This textbook based training would be further enhanced with some practical experience.
Chapter 8, Q1.
Technology has progressed allowing real time instant messaging and virtual meetings. High level managers can indeed expect technology to allow them to do their jobs with little face-to-face communication, but they should question if that is something they really want to do. There are currently methods available which could be used effectively to communicate with subordinates, employees and stockholders, such as recorded feeds which would be able to reach every associated individual. These however may not provide a sense of personalization from the managers. Leaders in an organization may resort to using tec.
DB Topic of Discussion Information-related CapabilitiesAnalyze .docxedwardmarivel
DB Topic of Discussion: Information-related Capabilities
Analyze 2 of the 14 information-related capabilities and explain how the joint force can use these capabilities to affect the three dimensions of the information environment. Give examples of real-world or life events for the capabilities and how can you use these concepts as a CSM/SGM.
Consumer Brand Metrics Q3 2015
Eater Archetypes:
Brand usage and preferences by consumer segment
The restaurant industry has long relied on demographic factors to
identify and prioritize consumer groups. For example, many
brands currently obsess over attracting Millennials—some
without pausing to consider the variations among consumers
within this demographic cohort. In addition to life stages,
consumer attitudes about health, value, convenience and the
overall role of foodservice in their lives drive significant
differences in preferences and behavior.
With these distinctions in mind, we have updated the Consumer
Brand Metrics (CBM) survey with questions that allow us to
segment consumers into one of seven Eater Archetypes. Each
segment has a distinct psychographic profile, which is outlined in
our recent Consumer Foodservice Landscape. Accordingly, their
patronage of the segments and brands tracked in CBM varies.
This paper explores some differences we can discern after the
initial quarterly results, including the archetypes’ segment usage,
brand patronage and occasion dynamics. Examining CBM data by
Eater Archetype reveals nuances that complement a demographic
profile of a chain’s guests.
By Colleen Rothman, Manager, Consumer Insights
To learn more about the Consumer Brand Metrics program or to sign up for future
Spotlight by Consumer Brand Metrics white papers, please contact Bart Henyan,
Senior Marketing Manager, at [email protected]
Consumer Brand Metrics Q3 2015
Segmenting consumers by psychographic factors, rather than
just demographic characteristics, can lead to a better
understanding of the consumers that matter to your brand and
how to appeal to them.
Key Takeaways
Busy Balancers and Functional Eaters drive usage across
restaurants and convenience stores. Full-service restaurant
(FSR) operators may also consider targeting Foodservice
Hobbyists and Affluent Socializers, as these archetypes
comprise more than a quarter of FSR patrons, on average.
How does foodservice segment usage vary by archetype?
Driven by unique needs and motivations, Eater Archetypes
gravitate to a wide variety of brands. For example,
McDonald’s, Burger King and Whataburger each
disproportionately attract unique archetypes (Habitual
Matures, Bargain Hunters and Functional Eaters,
respectively).
Which chains do each archetype visit most frequently?
Archetypes that patronize the same restaurant may not use
the brand the same way. For example, usage varies by
daypart, with afternoon snacks skewing to Busy Balancers
and late-night meals d.
DB Instructions Each reply must be 250–300 words with a minim.docxedwardmarivel
DB Instructions:
Each reply must be 250–300 words with a minimum of 1 scholarly source. The scholarly source used for your thread and response should be in addition to the class textbooks.
Reference Book: Young, M. (2017). Learning the Art of Helping. Boston, MA: Pearson. ISBN: 9780134165783.
.
DB Defining White Collar CrimeHow would you define white co.docxedwardmarivel
DB: Defining White Collar Crime
How would you define white collar crime? What are the advantages and disadvantages of the various terms, such as “white collar crime,” “crimes of the powerful,” “elite deviance,” etc., used to describe the type of crimes.
300 Word Minimum
.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
1. Datasets using R-Studio
Usha Rani Singh
1
Datasets for cars
Dataset is a collection of related information which is useful to
analyze data and derive the outputs
The dataset contains information in various forms, and it isn't
straightforward for the analyzer to extract the data and present
it to the business
2
Preparing Dataset for cars
2. Preparing and analyzing the dataset is very important for any
threat information, which helps to provide accurate data
We have to consider the data which provide more value or
relevant for the problem
Categorize the data into regression, classification, clustering,
and ranking
It is difficult to establish data collection mechanism and data is
scattered into various forms and departments
We have to make consistency in the data
Data sample has been reduced, and at the same time it should
consist of the required information
Preparing Dataset for cars
We have to clean the data so that the processing time will be
faster and accurate
Complex datasets have to be decomposed into multiple parts
Data normalization has to be performed to improve the quality
of the data
R Studio for dataset
3. Pie Diagram for data set
ggplot_1 for dataset
ggplot_2 for data set
ggplot_3 for dataset
5. Microsoft
Excel Worksheet
Microsoft
Excel Worksheet
Week 10 – Analysing Data sets in RapidMiner
The data sets used for this weeks analysis relates to the CSRIC
best practices:
The CSRIC Best Practices Search Tool allows you to search
CSRIC's collection of Best Practices using a variety of criteria
including Network Type, Industry Role, Keywords, Priority
Levels, and BP Number. The Communications Security,
Reliability and Interoperability Council's (CSRIC) mission is to
provide recommendations to the FCC to ensure, among other
things, optimal security and reliability of communications
systems, including telecommunications, media, and public
safety. CSRIC’s members focus on a range of public safety and
homeland security-related communications matters, including:
(1) the reliability and security of communications systems and
infrastructure, particularly mobile systems; (2) 911, Enhanced
911 (E911), and Next Generation 911 (NG911); and (3)
emergency alerting.
6. The CSRIC's recommendations will address the prevention and
remediation of detrimental cyber events, the development of
best practices to improve overall communications reliability,
the availability and performance of communications services
and emergency alerting during natural disasters, terrorist
attacks, cyber security attacks or other events that result in
exceptional strain on the communications infrastructure, the
rapid restoration of communications services in the event of
widespread or major disruptions and the steps communications
providers can take to help secure end-users and servers.
I have used RapidMiner to analyze the data set :
The statistical view of various names, types and attributes
related to the data set.
Visualization of public safety vs prioritization
7. Overall prioritization pie chart
Bar graph comparing various network types and internet/data
usage
customer-segmentation-data set.zip
Mall_Customers.csv
CustomerID,Gender,Age,Annual Income (k$),Spending Score
(1-100)
1,Male,19,15,39
2,Male,21,15,81
3,Female,20,16,6
4,Female,23,16,77
5,Female,31,17,40
6,Female,22,17,76
7,Female,35,18,6
19. Mall Customer Segment Data Analysis using RFM
Vivek Ijjagiri
Agenda
2
Introduction
Mall Customer Segmentation data
Mall Customer Segment analysis data using RFM
Problem Solving
20. Clustering
Conclusion
References
Introduction
When we want to increase the sales we need to do planning for
marketing spend, or while formulating a new promotion, as a
retail marketer we have to be more careful about how we
segment and target the customers. It would be a waste of time
and money if, for example, we launch an ad campaign that is
central to a lot of customers. Such untargeted marketing and
advertising is not likely to have a high conversion fee and may
additionally even hurt our company value.
Retailers now use sophisticated strategies to section their
customers and goal their marketing efforts to these segments.
RFM analysis is one such famous patron segmentation technique
that can assist shops to maximize the return on their advertising
investments.
Why RFM.?
Improving customer segmentation marketing and widely used
for surveys.
Superior and simplistic compared to other methods.(CHAID and
21. logistic regression)
Focuses on transaction information and delivering better
marketing to customers.
What is RFM?
R => Recency
F => Frequency
M=> Monetary
How are we using the RFM and target customers?
Simple we score the customers based on the RFM from high to
low.
Greater the score there’s likely more chance to buy a product or
take a new offer or promotion.
It’ll help us identify customers that are most likely to respond
to a new offer or promotion.
Identifying the most valuable RFM segments can capitalize on
chance relationships in the data used for this analysis.
22. Mall Customer Segment analysis data using RFM
7
Recency: Recency is most important predictor of customers who
did the purchases recently. Customers who have purchased
recently a product are more likely to purchase again from your
store/mall compared to those who did not purchase recently.
Frequency: The second most important factor is how frequently
these customers purchase from you. The higher the frequency,
the higher of chances of them purchasing the products again.
Monetary: The third factor is the amount of money these
customers have spent on purchases. Customers who have spent
higher are more likely to purchase based on their recent
purchase compared to those who have spent less.
How are we going to calculate RFM?
To implement the RFM analysis, we need to further process the
data set in by the following steps:
Find the most recent date for each ID and calculate the days to
the now or some other date, to get the Recency data
Calculate the quantity of translations of a customer, to get the
Frequency data
Sum the amount of money a customer spent and divide it by
23. Frequency, to get the amount per transaction on average, that is
the Monetary data.
8
Problem Solving
Make sure we have the following libraries to procced with the
data analysis, if the libraries not found in your R Studio install
those packages.
library(data.table)
library(dplyr)
library(ggplot2)
library(tidyr)
library(knitr)
library(rmarkdown)
9
Load and examine data
> Mall_Customers<- fread('data.csv’)
> glimpse(Mall_Customers)
24. Ijjagiri, Vivek (IV) - This is like a transposed version of print:
columns run down the page, and data runs across. This makes it
possible to see every column in a data frame. It's a little like str
applied to a data frame but it tries to show you as much data as
possible. (And it always shows the underlying data, even when
applied to a remote data source.)
View Data
14
Data Cleanup
Or
WRangle
15
> Mall_Customers<- Mall_Customers%>%
mutate(Quantity = replace(Quantity, Quantity<=0, NA),
UnitPrice = replace(UnitPrice, UnitPrice<=0, NA))
26. > summary(df_RFM)
17
Calculate RFM
> kable(head(df_RFM))
18
K-means clustering is one of the simplest and popular
unsupervised machine learning algorithms.
The objective of K-means is simple: group similar data points
together and discover underlying patterns.
To achieve this objective, K-means looks for a fixed number (k)
of clusters in a dataset.”
A cluster refers to a collection of data points aggregated
together because of certain similarities.
In other words, the K-means algorithm identifies k number of
centroids, and then allocates every data point to the nearest
cluster, while keeping the centroids as small as possible.
27. K Means Clustering Algorithm
1.Specify number of clusters K.
2.Initialize centroids by first shuffling the dataset and then
randomly selecting K data points for the centroids without
replacement.
3.Keep iterating until there is no change to the centroids. i.e
assignment of data points to clusters isn’t changing.
K Means clustering algorithm
Recency
Recency – How recently did the customer purchase?
> Customer_Purchase_Recency <- df_RFM$recency
> hist(Customer_Purchase_Recency, main = 'Recency')
20
Frequency
Frequency – How often do they purchase?
> Customer_Purchase_Frequency <- df_RFM$frequency
> hist(Customer_Purchase_Frequency, main = ‘Frequency')
21
28. Monetary
Monetary Value – How much do they spend?
> Customer_Purchase_Monitery <- df_RFM$monitery
> hist(Customer_Purchase_Monitery, main = ‘Monetary’,
breaks=50 )
22
Monetary Log
Because the data is skewed, we use log scale to normalize
> MoniteryLog <- log(df_RFM$monitery)
> hist(MoniteryLog, main ='MoniteryLog')
23
Ijjagiri, Vivek (IV) -
https://www.rdocumentation.org/packages/amap/versions/0.8-
17/topics/hcluster
Ijjagiri, Vivek (IV) - This function is a mix of function hclust
and function dist. hcluster(x, method = "euclidean",link =
"complete") = hclust(dist(x, method = "euclidean"),method =
"complete")) It use twice less memory, as it doesn't store
distance matrix.
For more details, see documentation of hclust and Dist.
Clustering
> DataFrame_Clustering <- df_RFM
29. > DataFrame_CustomerID <-
DataFrame_Clustering$CustomerID
> row.names(DataFrame_Clustering) <- DataFrame_CustomerID
> DataFrame_CustomerID <- NULL
> DataFrame_Clustering <- scale(DataFrame_Clustering)
> summary(DataFrame_Clustering )
24
Clustering
> d <- dist(DataFrame_Clustering)
> c <- hclust(d, method = 'ward.D2’)
> Plot(c)
25
Ijjagiri, Vivek (IV) - A dendrogram is a diagram that shows the
hierarchical relationship between objects. It is most commonly
created as an output from hierarchical clustering. The main use
of a dendrogram is to work out the best way to allocate objects
to clusters. The dendrogram below shows the hierarchical
clustering of six observations shown to on the scatterplot to the
left. (Dendrogram is often miswritten as dendogram.)
Plotting with less data
30. 26
Plotting with less data
27
Plotting with less data
28
Conclusion
Customer segmentation process can be performed using various
clustering algorithms.
We focused on k-means clustering in R.
The algorithm is quite simple to implement. However,
representing data in the correct format and interpreting results
is the difficult part.
31. RFM Analysis can segment customers, design offers,
promotions specific to audience and produce products based on
customer profile and interests.
References
Shubhankar Rawat (May 2019), Mall Customers Segmentation
— Using Machine Learning retrieved from
https://towardsdatascience.com/mall-customers-segmentation-
using-machine-learning-274ddf5575d5
What is market segmentation, Different types explained
retrieved from https://www.qualtrics.com/experience-
management/brand/what-is-market-segmentation/
Bradley, P. S., Bennett, K. P., & Demiriz, A. (2000).
Constrained k-means clustering (Technical Report MSR-TR-
2000-65). Microsoft Research, Redmond, WA.
K means clustering, AlindGupta retrieved from
https://www.geeksforgeeks.org/k-means-clustering-introduction/
Thank you
Any Questions
32. .MsftOfcThm_Accent1_Fill {
fill:#4472C4;
}
.MsftOfcThm_Accent1_Stroke {
stroke:#4472C4;
}
RcodeProject.R
##########################################
# section 3.3 Statistical Methods for Evaluation
##########################################
##########################################
# section 3.3.1 Hypothesis Testing
##########################################
# generate random observations from the two populations
x <- rnorm(10, mean=100, sd=5) # normal distribution centered
at 100
y <- rnorm(20, mean=105, sd=5) # normal distribution centered
at 105
# Student's t-test
t.test(x, y, var.equal=TRUE) # run the Student's t-test
# obtain t value for a two-sided test at a 0.05 significance level
qt(p=0.05/2, df=28, lower.tail= FALSE)
# Welch's t-test
t.test(x, y, var.equal=FALSE) # run the Welch's t-test
33. # Wilcoxon Rank-Sum Test
wilcox.test(x, y, conf.int = TRUE)
##########################################
# section 3.3.6 ANOVA
##########################################
offers <- sample(c("offer1", "offer2", "nopromo"), size=500,
replace=T)
# Simulated 500 observations of purchase sizes on the 3 offer
options
purchasesize <- ifelse(offers=="offer1", rnorm(500, mean=80,
sd=30),
ifelse(offers=="offer2", rnorm(500, mean=85,
sd=30),
rnorm(500, mean=40, sd=30)))
# create a data frame of offer option and purchase size
offertest <- data.frame(offer=as.factor(offers),
purchase_amt=purchasesize)
# display a summary of offertest where offer="offer1"
summary(offertest[offertest$offer=="offer1",])
# display a summary of offertest where offer="offer2"
summary(offertest[offertest$offer=="offer2",])
# display a summary of offertest where offer="nopromo"
summary(offertest[offertest$offer=="nopromo",])
# fit ANOVA test
model <- aov(purchase_amt ~ offers, data=offertest)
summary(model)