Tuesday, November 26, 2019

Free Essays on The Reshaping Of Everyday Life

The cultural and developmental aspects of American history in the 17th and 18th centuries are certainly among the most important and influential factors in the shaping of this country's long and storied history. The society that existed at that time had very different views on life and how it should occur. The daily routines were unlike ours even though it may be hard to believe. Even families, which seem to be a non-changing in history, were also distinct in size and order. A big change that occurred was the growth of the country. When the settlers first came to America they settled in villages near the coast. As more and more people migrated to America land for farming started to become scarce. There started to become a larger demand for land due to the fact that agriculture was the leading occupation for new settlers. The government started expanding the country by purchasing land from other countries. With all this new land, people started heading west to purchase their own. (Larkin 6) During the early years of colonization and exploration in North America, the New World collided and brought to each other many new things, both good and bad. There were exchanges of ideas, products and crops that greatly advanced the cultures of all involved, but on the other hand, new diseases, and harsh treatment of one another were also present. Before the arrival of the Europeans to present day United States, the Native Americans treated their homeland with respect and with spiritual properties. Occasionally they burned sections of land in the wilderness for better hunting area, but other than that they provided no threat to its well-being. This all changed when the European settlers arrived. The Europeans believed that humans had domination over the land. By building huge colonies, extensive road systems and for other technological advances, the colonizers greatly changed the face of our nation. Another impact on both the Native Amer... Free Essays on The Reshaping Of Everyday Life Free Essays on The Reshaping Of Everyday Life The cultural and developmental aspects of American history in the 17th and 18th centuries are certainly among the most important and influential factors in the shaping of this country's long and storied history. The society that existed at that time had very different views on life and how it should occur. The daily routines were unlike ours even though it may be hard to believe. Even families, which seem to be a non-changing in history, were also distinct in size and order. A big change that occurred was the growth of the country. When the settlers first came to America they settled in villages near the coast. As more and more people migrated to America land for farming started to become scarce. There started to become a larger demand for land due to the fact that agriculture was the leading occupation for new settlers. The government started expanding the country by purchasing land from other countries. With all this new land, people started heading west to purchase their own. (Larkin 6) During the early years of colonization and exploration in North America, the New World collided and brought to each other many new things, both good and bad. There were exchanges of ideas, products and crops that greatly advanced the cultures of all involved, but on the other hand, new diseases, and harsh treatment of one another were also present. Before the arrival of the Europeans to present day United States, the Native Americans treated their homeland with respect and with spiritual properties. Occasionally they burned sections of land in the wilderness for better hunting area, but other than that they provided no threat to its well-being. This all changed when the European settlers arrived. The Europeans believed that humans had domination over the land. By building huge colonies, extensive road systems and for other technological advances, the colonizers greatly changed the face of our nation. Another impact on both the Native Amer...

Friday, November 22, 2019

Expert Interview with Alison Green About Hiring and Interviewing

Expert Interview with Alison Green About Hiring and Interviewing After a chief of staff position at a successful organization, Alison Green set out on her own and became a consultant. Her experience with all that HR entails, including hiring, firing, managing and promoting, gave her the expertise to be a successful consultant and expert in her field. Alison, creator of Ask a Manager, took some time to talk with us about hiring, utilizing a cover letter and other interview/hiring tips. What is the most common mistake you see job applicants make?Can I give you two?The first one is squandering the opportunity that a cover letter can give you. Too many job seekers use their cover letter to simply summarize their resume. But with such limited initial contact, you do yourself a huge disservice if you use a whole page of your application to merely repeat the contents of the other pages. A cover letter is your opportunity to make a compelling case for yourself as a candidate, totally aside from what’s in your resume. You’re doing yourself a huge disservice if you don’t use it to add something new to your candidacy – information that doesn’t belong on your resume like personal traits, work habits and why you’re interested in the job.The second mistake that job seekers make all the time is forgetting to evaluate potential employers just as much as they’re evaluating you. In the anxiety of an interview, it can be easy to focus only on whether you’re impressing your interviewer, but it’s crucial to remember that you should be thinking about whether you even want the job. The interview process isn’t one-way; you should use the time to think about whether you’re the right fit for the work, the manager and the workplace culture. Otherwise, you can end up in a job where you don’t excel or aren’t happy.How assertive should an applicant be after an interview?You should certainly send a thank-you to reiterate your interest in the position and hopefully b uild on the conversation that you had in the interview, but beyond that, the ball is in the employer’s court.It’s a good idea to ask at the end of the interview when you can expect to hear back about next steps. If you do that and that time passes, then you have the perfect excuse to politely follow up. Simply drop them a quick email, explain that you’re still very interested but understand that hiring can take time, and ask if they have an updated timeline. But that’s really the only follow-up you should be doing. After that, it’s really up to them to get back to you. If they don’t, move on with other employers; don’t keep checking in with them – that will usually just be annoying and won’t get you a decision any faster.Once an offer is out there, how much negotiation should take place?It depends on the offer! If you earlier gave the employer the salary range you’re looking for and they offer you something at the high end of your range – or even higher than your range – asking for more would make you look like you were playing games or not operating in good faith. But outside of situations like that, it generally makes sense to negotiate, as long as you handle the discussion in a pleasant, professional and non-adversarial way, and as long as you’re not asking for something wildly outside the market range for the position.Of course, that means that you need to be prepared and do some research beforehand so that you know what the market rate is. Don’t try to wing it, or you can inadvertently ask for too much or too little.What do you think of job hopping?If you have a pattern of job hopping – which in most fields means a pattern of multiple stays of two years or less – that’s a big concern for most employers. Most hiring managers will tell you that the best predictor of how someone will behave in the future is how they’ve behaved in the past – their track record. So if someone has a pattern of leaving jobs relatively quickly, an interviewer will assume there’s a good chance they won’t stay long in a new position either. Since employers are generally hoping that anyone they hire will stay for at least a few years, a resume that shows little history of this is a red flag. Interviewers will assume you won’t stay long with them either, and they’ll wonder why you’re unable or unwilling to stay in one place for a more typical amount of time.(The exception to this is jobs that were designed from the beginning to be short-term, like internships, temp work or contract jobs. In that case, you’d just want to be sure that your resume makes it clear that these positions were designed to be short-term from the start, by noting â€Å"contract job† or something similar next to it.)How do you handle it when you’re interviewing and you just know someone is not right for the job?If I’m sure that it’s not the right match and it’s an easily articulable reason, I’ll try to share it on the spot if I can – for instance, explaining that we’re looking for someone with more of a background in X. However, a lot of the time it’s not as easy to capture in a single sentence or would lead to an awkward conversation (for instance, if the candidate seems like they wouldn’t work well with others or just isn’t sufficiently impressive, I’m of course not going to announce that to someone). In those cases, you still want the candidate to go away with positive feelings and to feel like they got a fair shot – so you continue to be warm and open and to give them a fair shot, although you might wrap up the interview a bit faster than you would with a candidate who seemed very promising.How have companies, positions and job security changed throughout recent years?Competition for jobs is greater, f or two reasons: First, the economy means that there are more people searching for jobs than there are job openings. Second, the ease of applying for jobs online means that employers are flooded with hundreds of applications for every opening they post. For the job seeker, that means that where in the past you might have been up against a few dozen other candidates, today you’re usually competing against several hundred others. That means that employers can be a lot pickier about who they hire. Because employers have so many qualified candidates to choose from, simply meeting the job qualifications isn’t nearly enough these days. That also means that it’s harder for less perfectly qualified candidates to stretch up to a job that in previous years they might have been able to get more easily.What inspired you to create Ask a Manager?At the time, I was the chief of staff for an organization where I kept seeing evidence that job seekers and employees would benefit s o much from getting more of an understanding of how managers think. And while there were a lot of career blogs out there, I didn’t see anyone giving advice from the perspective of a manager – a source that would explain to people, â€Å"Okay, here’s what your manager (or interviewer) is thinking when you say X,† or, â€Å"Here’s what your manager means when she says Y to you.† I felt like that could be really helpful to people – somewhere they could go and figure out how their manager might be thinking.Of course, when I started the blog back in 2007, I didn’t think it would get much interest. I figured I’d write it for a few months and get it out of my system. Seven years later, I’ve answered more than 6,000 reader questions at the site and it’s still going strong – which has been really gratifying.What’s your favorite part about writing Ask a Manager?My mail is full of letters from people who tell me that the site helped them get a better job, or negotiate a higher salary, or leave a toxic boss, or become a better manager, or even just be able to go on interviews without anxiety – and that’s an amazing feeling. I’ll never get tired of those letters.

Thursday, November 21, 2019

Child care should cut mothers tax Essay Example | Topics and Well Written Essays - 500 words

Child care should cut mothers tax - Essay Example But it is also mentioned by peter that taking such steps actually cause the rise in child care fees, which was according to the statistics provided by peter was 49 % more rapidly than inflation from 2000 to 2004. So the government during the election of 2004 proclaims an aid such that families can make reduction in their income tax equivalent to the 30 % of what they spent on their child care. Then all the related organizations, companies and societies came up with their own suggestions and improvement ideas, made to the government in order to improve child care facilities. Some suggestion were to make child care fees to be employee's pre-tax sacrifice, where as liberal 's want income tax reduction to be amplify to 50%, ACTU suggesting for spending $10 billion to construct new child-care centers and last but not the least Australia council of social service also demanded immense increase in child-care benefits. Peter also give reference of Families Ministers Mal Brough which identify the problem to s

Tuesday, November 19, 2019

Punishing children Essay Example | Topics and Well Written Essays - 1250 words

Punishing children - Essay Example Therefore, families should not adopt corporal punishment as a technique of teaching children how to behave as it impacts negatively on behavior, both in the short term and long term. Increasingly, research studies point out to the unintended negative consequences of corporal punishment. First, corporal punishment increases aggression among children as they appreciate physical violence as a form of solving conflicts, just as applied by their parents. Corporal punishment entails use of physical force which exhibits a positive curvilinear relationship with aggression in children. In fact, while vouching for the need for legislation against this form of punishment, Smith cites the United Nations Committee on the Rights of the Child referring to it as â€Å"legalized violence against children.† A review of various research studies by Elliman and Lynch (197) indicates that corporal punishment results in the child complying with the parental demands immediately after being hit, but f or a short term. Such a child does not learn what the desired good is and hence the threat of need for greater frequency and intensity of corporal punishment so as to maintain the compliance. This causes significant physical abuse among the children exposed to corporal punishment. Humphrey and Schmalleger observe that in school, such children are twice more likely to attack other children physically within 6 months (121). Further, such children exhibit tendencies of abuse of child or partner late in life. They become antisocial and have the sense of conscience, moral internalization and empathy in them significantly reduce (Aucoin, Frick, and Bodin 528). Therefore, corporal punishment does not cause positive behavioral gains, but rather arouses and propagates aggression in children. Corporal punishment has also been noted to increase the likelihood of children becoming delinquent. In fact, as noted by Aucoin, Frick, and Bodin (529), corporal punishment could lead to behavioral probl ems as opposed to behavioral problems leading to corporal punishment. As such, these children develop delinquent behaviors later in life. Continued use of corporal punishment upholds delinquent behavior, such trouble at school and lying, two years later (Humphrey and Schmalleger 120). Indeed, a research study documented by Elliman and Lynch on 4,888 residents of Ontario aged below 65 with no history of sexual or physical violence, but reported being spanked or slapped, exhibited significantly higher tendencies of alcohol abuse, dependence and anxiety disorders (197). It should therefore be appreciated that whereas corporal punishment aims at instilling desirable behavior in a child, it could lead to delinquency in children. The third negative impact of corporal punishment entails the lowering of self-esteem among children, together with causing depression. The physical pain that children endure as a result of corporal punishment causes a rise of bitterness in them. With limited oppo rtunities to release such feelings, such children end up being stressed and eventually depressed. Children who have been through years of emotional pain as a result of being

Saturday, November 16, 2019

Video game essay Essay Example for Free

Video game essay Essay Are video games bad for you? Sure, parents and adults will tell you they are bad, but how are they bad for you? Many will tell you that video games hurt your eyes and damage your nerves. They may even tell you video games make you more violent, but they cannot provide any evidence to back up their opinions. Nor do they realize the real problem—which is not video games themselves—but the addiction that drives many gamers to play video games to the exclusion of all else. In this essay, I will show that video game addiction is the real reason why video games are bad for you. Video game addiction leads to poor performance in school and insufficient physical inactivity. Video game addiction can have a negative effect on academic achievement. For example, a study published in the Journal of Personality and Social Psychology indicates that the more a student plays video games, the worse their academic performance is in school. A second study by Argosy Universitys Minnesota School on Professional Psychology found that video game addicts argue a lot with their teachers, fight a lot with their friends, and score lower grades than others who play video games less often. As these sources suggest, video game addicts obsessive gaming comes at a cost to their academic performance and relationships with those around them, ultimately leading to poor performance in school —and life. Video game addiction also leads to physical inactivity and related health problems. This occurs because obsessive gamers sacrifice a balanced lifestyle so they can spend all their free time playing video games. Instead of going out and rollerblading, playing hoops, or hanging out with friends, they sit in front of a screen for hours and hours. Consequently, they do get the exercise they need. The Center for Disease Control recommends 60 minutes of moderate to vigorous physical activity every day. When these gamers do not get this exercise, they are abusing their bodies and risking developing heart disease and other cardiovascular problems; they even run the risk of becoming obese, which further increases the chances of cardiovascular problems and could result in them developing diabetes. As I have shown in this essay, video game addiction leads to poor performance in school and physical inactivity. The urge to play games can drive gamers to neglect their studies and injure relationships with those around them. Their desire to play video games can cause them to disregard healthy levels of activity, leading to health problems and even obesity. The key to keeping the negative influence of video games in check is one word: control. If gamers can balance video games with the rest of their lives, video games can be a fun, entertaining diversion; but when video games take over their lives, they are left with a life that is not worth living. This type of addiction develops when gamers obsess over the video games they play. As result, they As this evidence indicates, video game addiction promotes physical inactivity, putting gamers at risk of obesity, heart disease and other health problems. Thus, students addicted to sacrifice homework and study to whatever game they are currently obsessing over, This results in excessive physical inactivity, which puts gamers at risk of developing heart disease and other cardiovascular problems, or becoming overweight. by spending all their free time playing. as gamers spend all their free time playing their games. Without the required levels of physical activity, these gamers risk developing cardiovascular problems, such as The Center for Disease Control and American Heart Associatoin.

Thursday, November 14, 2019

Slavery in Colonial America :: Slavery Essays

Slavery in Colonial America The first arrivals of Africans in America were treated similarly to the indentured servants in Europe. Black servants were treated differently from the white servants and by 1740 the slavery system in colonial America was fully developed. Slavery as it existed in America was a practice founded on the chattel principle. Slaves were treated as human chattel to be traded, sold, used, and ranked not among beings, but among things, as an article of property to the owner or possessor. Because the American slave system was based on this principle of human chattlehood, slaves were confined in many ways that handicapped them from even being able to act or live as a human being. The very idea of human chattelhood gave the master unlimited control over his defenseless slave. Chattels are not permitted to get married, acquire or hold property. Chattels cannot have rights and hence the slave has no rights. Chattels can be bought and sold and so justifies the existence of the slave trade. Chattels do not have any claim to legal protection, therefore the slave has none and must tolerate the cruelties of slavery. Chattels are not to be educated or instructed in religion. And lastly, chattels do not possess the freedom of speech and of the press. Race was a very important factor in American slavery. In other nations, slaves would be of the same race as their master. An ex-slave could re-enter society with their past forgotten and be accepted once again. On the other hand, American slavery was closely connected to racial differences that led to racial segregation and discrimination. Master and slave could physically be distinguished from one another, which ultimately distinguished one as human and the other as chattel. Before the American Revolution, slavery existed in every one of the colonies. But by the last quarter of the 18th century, slavery was eventually abandoned in the North mainly because it was not as profitable as it was to the South (where it was becoming even more prevalent). Slavery was an extremely important element in America's economy because of the expanding tobacco and cotton plantations in the Southern states that were in need of more and more cheap labor. At one point America was a land of 113, 000 slaveholders controlling twenty million slaves. By the 1760's many Americans were beginning to become dissatisfied with their mother nation and were waging a war of resistance against the British colonial government. Slavery in Colonial America :: Slavery Essays Slavery in Colonial America The first arrivals of Africans in America were treated similarly to the indentured servants in Europe. Black servants were treated differently from the white servants and by 1740 the slavery system in colonial America was fully developed. Slavery as it existed in America was a practice founded on the chattel principle. Slaves were treated as human chattel to be traded, sold, used, and ranked not among beings, but among things, as an article of property to the owner or possessor. Because the American slave system was based on this principle of human chattlehood, slaves were confined in many ways that handicapped them from even being able to act or live as a human being. The very idea of human chattelhood gave the master unlimited control over his defenseless slave. Chattels are not permitted to get married, acquire or hold property. Chattels cannot have rights and hence the slave has no rights. Chattels can be bought and sold and so justifies the existence of the slave trade. Chattels do not have any claim to legal protection, therefore the slave has none and must tolerate the cruelties of slavery. Chattels are not to be educated or instructed in religion. And lastly, chattels do not possess the freedom of speech and of the press. Race was a very important factor in American slavery. In other nations, slaves would be of the same race as their master. An ex-slave could re-enter society with their past forgotten and be accepted once again. On the other hand, American slavery was closely connected to racial differences that led to racial segregation and discrimination. Master and slave could physically be distinguished from one another, which ultimately distinguished one as human and the other as chattel. Before the American Revolution, slavery existed in every one of the colonies. But by the last quarter of the 18th century, slavery was eventually abandoned in the North mainly because it was not as profitable as it was to the South (where it was becoming even more prevalent). Slavery was an extremely important element in America's economy because of the expanding tobacco and cotton plantations in the Southern states that were in need of more and more cheap labor. At one point America was a land of 113, 000 slaveholders controlling twenty million slaves. By the 1760's many Americans were beginning to become dissatisfied with their mother nation and were waging a war of resistance against the British colonial government.

Tuesday, November 12, 2019

Children social and emotional development Essay

The advantage that day cares provide to children is, they help the child to socialize and improve any social skill. For example a kid who never go out and play with other children doesn’t know and doesn’t learn a different environment than the family atmosphere. Many cases as a result of it, children grow up shy and sometimes have difficulties to create social relationships with others. At daycares kids learn to see the differences between others kids, they might find people who speaks different languages or belong to a different ethnicity. So they start to see the world it’s different outside of home. Daycares help children to discover new things, improve social and emotional develop because children are around children and are not with the parents all the times, so it creates security and independence from parents, which it help in the future when this child becomes a teenager. Day care absolutely have a lot of influence in the language aspect. When a child stays home, this child just listen to how the mother or father speaks and sometimes mom has the bad habit to â€Å"talk little† or keep thinking the child is still a newborn confusing the toddler with small words.† The language used by the caregiver is the most important factor that predicted children’s cognitive and language outcome†. Children are like sponge, they absorb everything specially from the age 0 to 3, those ages are crucial for the cognitive social and emotional develop, and day cares help in a big part to increase and ensure the well develop of the child.

Saturday, November 9, 2019

Based Data Mining Approach for Quality Control

Classification-Based Data Mining Approach For Quality Control In Wine Production GUIDED BY: | | SUBMITTED BY:| Jayshri Patel| | Hardik Barfiwala| INDEX Sr No| Title| Page No. | 1| Introduction Wine Production| | 2| Objectives| | 3| Introduction To Dataset| | 4| Pre-Processing| | 5| Statistics Used In Algorithms| | 6| Algorithms Applied On Dataset| | 7| Comparison Of Applied Algorithm | | 8| Applying Testing Dataset| | 9| Achievements| | 1.INTRODUCTION TO WINE PRODUCTION * Wine industry is currently growing well in the market since the last decade. However, the quality factor in wine has become the main issue in wine making and selling. * To meet the increasing demand, assessing the quality of wine is necessary for the wine industry to prevent tampering of wine quality as well as maintaining it. * To remain competitive, wine industry is investing in new technologies like data mining for analyzing taste and other properties in wine. Data mining techniques provide more than summary, but valuable information such as patterns and relationships between wine properties and human taste, all of which can be used to improve decision making and optimize chances of success in both marketing and selling. * Two key elements in wine industry are wine certification and quality assessment, which are usually conducted via physicochemical and sensory tests. * Physicochemical tests are lab-based and are used to characterize physicochemical properties in wine such as its density, alcohol or pH values. * Meanwhile, sensory tests such as taste preference are performed by human experts.Taste is a particular property that indicates quality in wine, the success of wine industry will be greatly determined by consumer satisfaction in taste requirements. * Physicochemical data are also found useful in predicting human wine taste preference and classifying wine based on aroma chromatograms. 2. OBJECTIVE * Modeling the complex human taste is an important focus in wine industries. * The main purpose of this study was to predict wine quality based on physicochemical data. * This study was also conducted to identify outlier or anomaly in sample wine set in order to detect ruining of wine. 3. INTRODUCTION TO DATASETTo evaluate the performance of data mining dataset is taken into consideration. The present content describes the source of data. * Source Of Data Prior to the experimental part of the research, the data is gathered. It is gathered from the UCI Data Repository. The UCI Repository of Machine Learning Databases and Domain Theories is a free Internet repository of analytical datasets from several areas. All datasets are in text files format provided with a short description. These datasets received recognition from many scientists and are claimed to be a valuable source of data. * Overview Of Dataset INFORMATION OF DATASET|Title:| Wine Quality| Data Set Characteristics:| Multivariate| Number Of Instances:| WHITE-WINE : 4898 RED-WINE : 1599 | Area:| Business| Attrib ute Characteristic:| Real| Number Of Attribute:| 11 + Output Attribute| Missing Value:| N/A| * Attribute Information * Input variables (based on physicochemical tests) * Fixed Acidity: Amount of Tartaric Acid present in wine. (In mg per liter) Used for taste, feel and color of wine. * Volatile Acidity: Amount of Acetic Acid present in wine. (In mg per liter) Its presence in wine is mainly due to yeast and bacterial metabolism. * Citric Acid: Amount of Citric Acid present in wine. In mg per liter) Used to acidify wine that are too basic and as a flavor additive. * Residual Sugar: The concentration of sugar remaining after fermentation. (In grams per liter) * Chlorides: Level of Chlorides added in wine. (In mg per liter) Used to correct mineral deficiencies in the brewing water. * Free Sulfur Dioxide: Amount of Free Sulfur Dioxide present in wine. (In mg per liter) * Total Sulfur Dioxide: Amount of free and combined sulfur dioxide present in wine. (In mg per liter) Used mainly as pres ervative in wine process. * Density: The density of wine is close to that of water, dry wine is less and sweet wine is higher. In kg per liter) * PH: Measures the quantity of acids present, the strength of the acids, and the effects of minerals and other ingredients in the wine. (In values) * Sulphates: Amount of sodium metabisulphite or potassium metabisulphite present in wine. (In mg per liter) * Alcohol: Amount of Alcohol present in wine. (In percentage) * Output variable (based on sensory data) * Quality (score between 0 and 10) : White Wine : 3 to 9 Red Wine : 3 to 8 4. PRE-PROCESSING * Pre-processing Of Data Preprocessing of the dataset is carried out before mining the data to remove the different lacks of the information in the data source.Following different process are carried out in the preprocessing reasons to make the dataset ready to perform classification process. * Data in the real world is dirty because of the following reason. * Incomplete: Lacking attribute values, lacking certain attributes of interest, or containing only aggregate data. * E. g. Occupation=â€Å"† * Noisy : Containing errors or outliers. * E. g. Salary=â€Å"-10† * Inconsistent : Containing discrepancies in codes or names. * E. g. Age=â€Å"42† Birthday=â€Å"03/07/1997† * E. g. Was rating â€Å"1,2,3†, Now rating â€Å"A, B, C† * E. g. Discrepancy between duplicate records * No quality data, no quality mining results! Quality decisions must be based on quality data. * Data warehouse needs consistent integration of quality data. * Major Tasks in done in the Data Preprocessing are, * Data Cleaning * Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies. * Data integration * Integration of multiple databases, data cubes, or files. * The dataset provided from given data source is only in one single file. So there is no need for integrating the dataset. * Data transformation * Normalization a nd aggregation * The dataset is in Normalized form because it is in single data file. * Data reduction Obtains reduced representation in volume but produces the same or similar analytical results. * The data volume in the given dataset is not very huge, the procedure of performing different algorithm is easily done on dataset so the reduction of dataset is not needed on the data set * Data discretization * Part of data reduction but with particular importance, especially for numerical data. * Need for Data Preprocessing in wine quality, * For this dataset Data Cleaning is only required in data pre-processing. * Here, NumericToNominal, InterquartileRange and RemoveWithValues filters are used for data pre-processing. * NumericToNominal Filter weka. filters. unsupervised. attribute. NumericToNominal) * A filter for turning numeric attribute into nominal once. * In our dataset, Class attribute â€Å"Quality† in both dataset (Red-wine Quality, White-wine Quality) have a type †Å"Numeric†. So after applying this filter, class attribute â€Å"Quality† convert into type â€Å"Nominal†. * And Red-wine Quality dataset have class names 3, 4, 5 †¦ 8 and White-wine Quality dataset have class names 3, 4, 5 †¦ 9. * Because of classification does not apply on numeric type class field, there is a need for this filter. * InterquartileRange Filter (weka. filters. unsupervised. attribute. InterquartileRange) A filter for detecting outliers and extreme values based on interquartile ranges. The filter skips the class attribute. * Apply this filter for all attribute indices with all default options. * After applying, filter adds two more fields which names are â€Å"Outliers† and â€Å"ExtremeValue†. And this fields has two types of label â€Å"No† and â€Å"Yes†. Here â€Å"Yes† label indicates, there are outliers and extreme values in dataset. * In our dataset, there are 83 extreme values and 125 outliers i n White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * RemoveWithValues Filter (weka. filters. unsupervised. instance.RemoveWithValues) * Filters instances according to the value of an attribute. * This filter has two options which are â€Å"AttributeIndex† and â€Å"NominalIndices†. * AttributeIndex choose attribute to be use for selection and NominalIndices choose range of label indices to be use for selection on nominal attribute. * In our dataset, AttributeIndex is â€Å"last† and NominalIndex is also â€Å"last†, so It will remove first 83 extreme values and then 125 outliers in White-wine Quality dataset and 69 extreme values and 94 outliers in Red-wine Quality. * After applying this filter on dataset remove both fields from dataset. * Attribute SelectionRanking Attributes Using Attribute Selection Algorithm| RED-WINE| RANKED| WHITE-WINE| Volatile_Acidity(2)| 0. 1248| 0. 0406| Volatile_Acidity(2)| Total_sulfer_Diox ide(7)| 0. 0695| 0. 0600| Citric_Acidity(3)| Sulphates(10)| 0. 1464| 0. 0740| Chlorides(5)| Alcohal(11)| 0. 2395| 0. 0462| Free_Sulfer_Dioxide(6)| | | 0. 1146| Density(8)| | | 0. 2081| Alcohal(11)| * The selection of attributes is performed automatically by WEKA using Info Gain Attribute Eval method. * The method evaluates the worth of an attribute by measuring the information gain with respect to the class. 5. STATISTICS USED IN ALGORITHMS * Statistics MeasuresThere are Different algorithms that can be used while performing data mining on the different dataset using weka, some of them are describe below with the different statistics measures. * Statistics Used In Algorithms * Kappa statistic * The kappa statistic, also called the kappa coefficient, is a performance criterion or index which compares the agreement from the model with that which could occur merely by chance. * Kappa is a measure of agreement normalized for chance agreement. * Kappa statistic describe that our predicti on for class attribute for given dataset is how much near to actual values. * Values Range For Kappa Range| Result| lt;0| POOR| 0-0. 20| SLIGHT| 0. 21-0. 40| FAIR| 0. 41-0. 60| MODERATE| 0. 61-0. 80| SUBSTANTIAL| 0. 81-1. 0| ALMOST PERFECT| * As above range in weka algorithm evaluation if value of kappa is near to 1 then our predicted values are accurate to actual values so, applied algorithm is accurate. Kappa Statistic Values For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 5365| 0. 5294| J48| 0. 3813| 0. 3881| Multilayer Perceptron| 0. 2946| 0. 3784| * Mean absolute error (MAE) * Mean absolute error (MAE)  is a quantity used to measure how close forecasts or predictions are to the eventual outcomes. The mean absolute error is given by, Mean absolute Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 1297| 0. 1381| J48| 0. 1245| 0. 1401| Multilayer Perceptron| 0. 1581| 0. 1576| * Root Mean Squared Erro r * If you have some data and try to make a curve (a formula) fit them, you can graph and see how close the curve is to the points. Another measure of how well the curve fits the data is Root Mean Squared Error. * For each data point, CalGraph calculates the value of  Ã‚  y from the formula. It subtracts this from the data's y-value and squares the difference. All these squares are added up and the sum is divided by the number of data. * Finally CalGraph takes the square root. Written mathematically, Root Mean Square Error is Root Mean Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 0. 2428| 0. 2592| J48| 0. 3194| 0. 3354| Multilayer Perceptron| 0. 2887| 0. 3023| * Root Relative Squared Error * The  root relative squared error  is relative to what it would have been if a simple predictor had been used. More specifically, this simple predictor is just the average of the actual values. Thus, the relative squared error takes the to tal squared error and normalizes it by dividing by the total squared error of the simple predictor. * By taking the square root of therelative squared error  one reduces the error to the same dimensions as the quantity being predicted. * Mathematically, the  root relative squared error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0.So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal. Root Relative Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality| K-Star| 78. 1984 %| 79. 309 %| J48| 102. 9013 %| 102. 602 %| Multilayer Perceptron| 93. 0018 %| 92. 4895 %| * Relative Absolute Error * The  relative absolute error  is very similar to the  relative squared error  in the sense that it is also relative to a simple predictor, which is just the average of the actual values. In this case, though, the error is just the total absolute error instead of the total squared error. Thus, the relative absolute error takes the total absolute error and normalizes it by dividing by the total absolute error of the simple predictor. Mathematically, the  relative absolute error  Ei  of an individual program  i  is evaluated by the equation: * where  P(ij)  is the value predicted by the individual program  i  for sample case  j  (out of  n  sample cases);  Tj  is the target value for sample case  j; andis given by the formula: * For a perfect fit, the numerator is equal to 0 and  Ei  = 0. So, the  Ei  index ranges from 0 to infinity, with 0 corresponding to the ideal.Relative Absolute Squared Error For Wine Quality DataSet| Algorithm| White-wine Quality| Red-wine Quality | K-Star| 67. 2423 %| 64. 5286 %| J48| 64. 577 %| 65. 4857 %| Multilayer Perceptron| 81. 9951 %| 73. 6593 %| * Various Rates * There are four possible outcomes from a classifier. * If the outcome from a prediction is  p  and the actual value is also  p, then it is called a  true positive  (TP). * However if the actual value is  n  then it is said to be a  false positive  (FP). * Conversely, a  true negative  (TN) has occurred when both the prediction outcome and the actual value are  n. And  false negative  (FN) is when the prediction outcome is  n while the actual value is  p. * Absolute Value | P| N| TOTAL| p’| True positive| false positive| P’| n’| false negative| True negative| N’| Total| P| N| | * ROC Curves * While estimating the effectiveness and accuracy of data mining technique it is essential to measure the error rate of each method. * In the case of binary classification tasks the error rate takes and components under consideration. * The ROC analysis which stands for Receiver Operating Characteristics is applied. * The sample ROC curve is presented in the Figure below.The closer the ROC curve is to the top left corner of the ROC chart the better the performance of the classifier. * Sample ROC curve (squares with the usage of the model, triangles without). The line connecting the square with triage is the benefit from the usage of the model. * It plots the curve which consists of x-axis presenting false positive rate and y-axis which plots the true positive rate. This curve model selects the optimal model on the basis of assumed class distribution. * The ROC curves are applicable e. g. in decision tree models or rule sets. * Recall, Precision and F-Measure There are four possible results of classification. * Different combination of these four error and correct situations are presented in the scientific literature on topic. * Here three popular notions are presented. The introduction of the se classifiers is explained by the possibility of high accuracy by negative type of data. * To avoid such situation recall and precision of the classification are introduced. * The F measure is the harmonic mean of precision and recall. * The formal definitions of these measures are as follow : PRECSION = TPTP+FP RECALL = TPTP+FNF-Measure = 21PRECSION+1RECALL * These measures are introduced especially in information retrieval application. * Confusion Matrix * A matrix used to summarize the results of a supervised classification. * Entries along the main diagonal are correct classifications. * Entries other than those on the main diagonal are classification errors. 6. ALGORITHMS * K-Nearest Neighbor Classifiers * Nearest neighbor classifiers are based on learning by analogy. * The training samples are described by n-dimensional numeric attributes. Each sample represents a point in an n-dimensional space. In this way, all of the training samples are stored in an n-dimensional pattern space. When given an unknown sample, a k-nearest neighbor classifier searches the pattern space for the k training samples that are closest to the unknown sample. * These k training samples are the k-nearest neighbors of the unknown sample. â€Å"Closeness† is defined in terms of Euclidean distance, where the Euclidean distance between two points, , * The unknown sample is assigned the most common class among its k nearest neighbors. When k = 1, the unknown sample is assigned the class of the training sample that is closest to it in pattern space. Nearest neighbor classifiers are instance-based or lazy learners in that they store all of the training samples and do not build a classifier until a new (unlabeled) sample needs to be classified. * Lazy learners can incur expensive computational costs when the number of potential neighbors (i. e. , stored training samples) with which to compare a given unlabeled sample is great. * Therefore, they require efficient indexing techniqu es. As expected, lazy learning methods are faster at training than eager methods, but slower at classification since all computation is delayed to that time.Unlike decision tree induction and back propagation, nearest neighbor classifiers assign equal weight to each attribute. This may cause confusion when there are many irrelevant attributes in the data. * Nearest neighbor classifiers can also be used for prediction, i. e. to return a real-valued prediction for a given unknown sample. In this case, the classifier returns the average value of the real-valued labels associated with the k nearest neighbors of the unknown sample. * In weka the previously described algorithm nearest neighbor is given as Kstar algorithm in classifier -> lazy tab. The Result Generated After Applying K-Star On White-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0. 02 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 3307 | 70. 6624 % | Incorrectly Classified Instances| 1373 | 29. 3376 %| Kappa Statistic | 0. 5365| | Mean Absolute Error | 0. 1297| | Root Mean Squared Error| 0. 2428| | Relative Absolute Error | 67. 2423 %| | Root Relative Squared Error | 78. 1984 %| | Total Number Of Instances | 4680 | | * Detailed Accuracy By Class | TP Rate| FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0 | 0 | 0 | 0 | 0. 583 | 0. 004 | 3| | 0. 211 | 0. 002 | 0. 769 | 0. 211 | 0. 331 | 0. 884 | 0. 405 | 4| | 0. 672 | 0. 079 | 0. 777 | 0. 672 | 0. 721 | 0. 904 | 0. 826 | 5| | 0. 864 | 0. 378 | 0. 652 | 0. 864 | 0. 743 | 0. 84 | 0. 818 | 6| | 0. 536 | 0. 031 | 0. 797 | 0. 536 | 0. 641 | 0. 911 | 0. 772 | 7| | 0. 398 | 0. 002 | 0. 883 | 0. 398 | 0. 548 | 0. 913 | 0. 572 | 8| | 0 | 0 | 0 | 0 | 0 | 0. 84 | 0. 014 | 9| Weighted Avg. | 0. 707 | 0. 2 | 0. 725 | 0. 707 | 0. 695 | 0. 876 | 0. 787| | * Confusion Matrix| A | B | C | D | E | F| G | | Class| 0 | 0 | 4 | 9 | 0| 0 | 0 | | | A=3| 0| 30| 49| 62| 1 | 0 | 0| | | B=4| 0 | 7 | 919| 437| 5 | 0 | 0 | | | C=5| 0 | 2 | 201| 1822| 81 | 2 | 0 | || D=6| 0 | 0 | 9 | 389 | 468 | 7 | 0| || E=7| 0 | 0 | 0 | 73 | 30 | 68 | 0 | || F=8| 0 | 0 | 0 | 3 | 2 | 0 | 0 | || G=9| * Performance Of The Kstar With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 6581 %| 100 %| 70. 6624 %| 63. 9221 %| Kappa statistic| 0. 9949| 1| 0. 5365| 0. 4252| Mean Absolute Error| 0. 0575| 0. 0788| 0. 1297| 0. 1379| Root Mean Squared Error| 0. 1089| 0. 145| 0. 2428| 0. 2568| Relative Absolute Error| 29. 8022 %| | 67. 2423 %| 71. 2445 %| * The Result Generated After Applying K-Star On Red-wine Quality Dataset Kstar Options : -B 70 -M a | Time Taken To Build Model: 0 Seconds| Stratified Cross-Validation (10-Fold)| * Summary | Correctly Classified Instances | 1013 | 71. 379 %| Incorrectly Classified Instances| 413 | 28. 9621 %| Kappa Stat istic | 0. 5294| | Mean Absolute Error | 0. 1381| | Root Mean Squared Error | 0. 2592| | Relative Absolute Error | 64. 5286 %| | Root Relative Squared Error | 79. 309 %| | Total Number Of Instances | 1426 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area| Class| | 0 | 0. 001 | 0 | 0 | 0 | 0. 574 | 0. 019 | 3| | 0 | 0. 003 | 0 | 0 | 0 | 0. 811 | 0. 114 | 4| | 0. 791| 0. 176 | 0. 67| 0. 791| 0. 779 | 0. 894 | 0. 867 | 5| | 0. 769 | 0. 26 | 0. 668 | 0. 769 | 0. 715 | 0. 834 | 0. 788 | 6| | 0. 511 | 0. 032 | 0. 692 | 0. 511 | 0. 588 | 0. 936 | 0. 722 | 7| | 0. 125 | 0. 001 | 0. 5 | 0. 125 | 0. 2 | 0. 896 | 0. 142 | 8| Weighted Avg. | 0. 71| 0. 184| 0. 685| 0. 71| 0. 693| 0. 871| 0. 78| | * Confusion Matrix | A | B | C | D | E | F| | Class| 0 | 1 | 4| 1 | 0 | 0 | | | A=3| 1 | 0 | 30| 17 | 0 | 0| | | B=4| 0 | 2| 477| 120 | 4 | 0| | | C=5| 0 | 1 | 103 | 444| 29 | 0| || D=6| 0 | 0 | 8 | 76 | 90 | 2 | || E=7| 0 | 0 | 0 | 7 | 7 | 2| || F=8| Performance Of The Kstar With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 99. 7895 %| 100 % | 71. 0379 %| 70. 7216 %| Kappa statistic| 0. 9967| 1| 0. 5294| 0. 5154| Mean Absolute Error| 0. 0338| 0. 0436| 0. 1381| 0. 1439| Root Mean Squared Error| 0. 0675| 0. 0828 | 0. 2592| 0. 2646| Relative Absolute Error| 15. 8067 %| | 64. 5286 %| 67. 4903 %| * J48 Decision Tree * Class for generating a pruned or unpruned C4. 5 decision tree. A decision tree is a predictive machine-learning model that decides the target value (dependent variable) of a new sample based on various attribute values of the available data. * The internal nodes of a decision tree denote the different attribute; the branches between the nodes tell us the possible values that these attributes can have in the observed samples, while the terminal nodes tell us the final value (class ification) of the dependent variable. * The attribute that is to be predicted is known as the dependent variable, since its value depends upon, or is decided by, the values of all the other attributes.The other attributes, which help in predicting the value of the dependent variable, are known as the independent variables in the dataset. * The J48 Decision tree classifier follows the following simple algorithm: * In order to classify a new item, it first needs to create a decision tree based on the attribute values of the available training data. So, whenever it encounters a set of items (training set) it identifies the attribute that discriminates the various instances most clearly. * This feature that is able to tell us most about the data instances so that we can classify them the best is said to have the highest information gain. Now, among the possible values of this feature, if there is any value for which there is no ambiguity, that is, for which the data instances falling wi thin its category have the same value for the target variable, then we terminate that branch and assign to it the target value that we have obtained. * For the other cases, we then look for another attribute that gives us the highest information gain. Hence we continue in this manner until we either get a clear decision of what combination of attributes gives us a particular target value, or we run out of attributes.In the event that we run out of attributes, or if we cannot get an unambiguous result from the available information, we assign this branch a target value that the majority of the items under this branch possess. * Now that we have the decision tree, we follow the order of attribute selection as we have obtained for the tree. By checking all the respective attributes and their values with those seen in the decision tree model, we can assign or predict the target value of this new instance. * The Result Generated After Applying J48 On White-wine Quality Dataset Time Taken To Build Model: 1. 4 Seconds| Stratified Cross-Validation (10-Fold) | * Summary| | | Correctly Classified Instances| 2740 | 58. 547 %| Incorrectly Classified Instances | 1940 | 41. 453 %| Kappa Statistic | 0. 3813| | Mean Absolute Error | 0. 1245| | Root Mean Squared Error | 0. 3194| | Relative Absolute Error | 64. 5770 %| | Root Relative Squared Error| 102. 9013 %| | Total Number Of Instances | 4680| | * Detailed Accuracy By Class| | TP Rate| FP Rate| Precision| Recall| F-Measure| ROC Area| Class| | 0| 0. 002| 0| 0| 0| 0. 30| 3| | 0. 239| 0. 020| 0. 270| 0. 239| 0. 254| 0. 699| 4| | 0. 605| 0. 169| 0. 597| 0. 605| 0. 601| 0. 763| 5| | 0. 644| 0. 312| 0. 628| 0. 644| 0. 636| 0. 689| 6| | 0. 526| 0. 099| 0. 549| 0. 526| 0. 537| 0. 766| 7| | 0. 363| 0. 022| 0. 388| 0. 363| 0. 375| 0. 75| 8| | 0| 0| 0| 0| 0| 0. 496| 9| Weighted Avg. | 0. 585 | 0. 21 | 0. 582 | 0. 585 | 0. 584 | 0. 727| | * Confusion Matrix | A| B| C| D| E| F| G| || Class| 0| 2| 6| 5| 0| 0| 0| || A=3| 1| 34| 55| 44| 6| 2| 0| || B=4| 5| 50| 828| 418| 60| 7| 0| || C=5| 2| 32| 413| 1357| 261| 43| 0| || D=6| | 7| 76| 286| 459| 44| 0| || E=7| 1| 1| 10| 49| 48| 62| 0| || F=8| 0| 0| 0| 1| 2| 2| 0| || G=9| * Performance Of The J48 With Respect To A Testing Configuration For The White-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 90. 1923 %| 70 %| 58. 547 %| 54. 8083 %| Kappa statistic| 0. 854| 0. 6296| 0. 3813| 0. 33| Mean Absolute Error| 0. 0426| 0. 0961| 0. 1245| 0. 1347| Root Mean Squared Error| 0. 1429| 0. 2756| 0. 3194| 0. 3397| Relative Absolute Error| 22. 0695 %| | 64. 577 %| 69. 84 %| * The Result Generated After Applying J48 On Red-wine Quality Dataset Time Taken To Build Model: 0. 17 Seconds| Stratified Cross-Validation| * Summary| Correctly Classified Instances | 867 | 60. 7994 %| Incorrectly Classified Instances | 559 | 39. 2006 %| Kappa Statistic | 0. 3881| | Mean Absolute Error | 0. 1401| | Root Mean Squa red Error | 0. 3354| | Relative Absolute Error | 65. 4857 %| | Root Relative Squared Error | 102. 602 %| |Total Number Of Instances | 1426 | | * Detailed Accuracy By Class| | Tp Rate | Fp Rate | Precision | Recall | F-measure | Roc Area | Class| | 0 | 0. 004 | 0 | 0 | 0 | 0. 573 | 3| | 0. 063 | 0. 037 | 0. 056 | 0. 063 | 0. 059 | 0. 578 | 4| | 0. 721 | 0. 258 | 0. 672 | 0. 721 | 0. 696 | 0. 749 | 5| | 0. 57 | 0. 238 | 0. 62 | 0. 57 | 0. 594 | 0. 674 | 6| | 0. 563 | 0. 64 | 0. 553 | 0. 563 | 0. 558 | 0. 8 | 7| | 0. 063 | 0. 006 | 0. 1 | 0. 063 | 0. 077 | 0. 691 | 8| Weighted Avg. | 0. 608 | 0. 214 | 0. 606 | 0. 608 | 0. 606 | 0. 718 | | * Confusion Matrix | A | B | C | D | E | F | | Class| 0 | 2 | 1 | 2 | 1 | 0 | | | A=3| 2 | 3 | 25 | 15 | 3 | 0 | | | B=4| 1 | 26 | 435 | 122 | 17 | 2 | | | C=5| 2 | 21 | 167 | 329 | 53 | 5 | | | D=6| 0 | 2 | 16 | 57 | 99 | 2 | | | E=7| 0 | 0 | 3 | 6 | 6 | 1 | | | F=8| Performance Of The J48 With Respect To A Testing Configuration For The Red-wine Qual ity Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 91. 1641 %| 80 %| 60. 7994 %| 62. 4742 %| Kappa statistic| 0. 8616| 0. 6875| 0. 3881| 0. 3994| Mean Absolute Error| 0. 0461| 0. 0942| 0. 1401| 0. 1323| Root Mean Squared Error| 0. 1518| 0. 2618| 0. 3354| 0. 3262| Relative Absolute Error| 21. 5362 %| 39. 3598 %| 65. 4857 %| 62. 052 %| * Multilayer Perceptron * The back propagation algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. * A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. * Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted an d fed simultaneously to a second layer of â€Å"neuronlike† units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used. The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples. * The units in the input layer are called input units. The units in the hidden layers and output layer are sometimes referred to as neurodes, due to their symbolic biological basis, or as output units. * The network is feed-forward in that none of the weights cycles back to an input unit or to an output unit of a previous layer.It is fully connected in that each unit provides input to each unit in the next forward layer. * The Result Generated After Applying Multilayer Perceptron On White-wine Quality Dataset Time taken to build model: 36. 22 seconds| Stratifi ed cross-validation| * Summary| Correctly Classified Instances | 2598 | 55. 5128 %| Incorrectly Classified Instances | 2082 | 44. 4872 %| Kappa statistic | 0. 2946| | Mean absolute error | 0. 1581| | Root mean squared error | 0. 2887| |Relative absolute error | 81. 9951 %| | Root relative squared error | 93. 0018 %| | Total Number of Instances | 4680 | | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | PRC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 344 | 0. 002 | 3| | 0. 056 | 0. 004 | 0. 308 | 0. 056 | 0. 095 | 0. 732 | 0. 156 | 4| | 0. 594 | 0. 165 | 0. 597 | 0. 594 | 0. 595 | 0. 98 | 0. 584 | 5| | 0. 704 | 0. 482 | 0. 545 | 0. 704 | 0. 614 | 0. 647 | 0. 568 | 6| | 0. 326 | 0. 07 | 0. 517 | 0. 326 | 0. 4 | 0. 808 | 0. 474 | 7| | 0. 058 | 0. 002 | 0. 5 | 0. 058 | 0. 105 | 0. 8 | 0. 169 | 8| | 0 | 0 | 0| 0 | 0 | 0. 356 | 0. 001 | 9| Weighted Avg. | 0. 555 | 0. 279 | 0. 544 | 0. 555 | 0. 532 | 0. 728 | 0. 526| | * Confusion Matrix |A | B | C | D | E | F | G | | Class| 0 | 0 | 5 | 7 | 1 | 0 | 0 | | | A=3| 0 | 8 | 82 | 50 | 2 | 0 | 0 | | | B=4| 0 | 11 | 812 | 532 | 12 | 1 | 0 | | | C=5| 0 | 6 | 425 | 1483 | 188 | 6 | 0 | | | D=6| 0 | 1 | 33 | 551 | 285 | 3 | 0 | | | E=7| 0 | 0 | 3 | 98 | 60 | 10 | 0 | | | F=8| 0 | 0 | 0 | 2 | 3 | 0 | 0 | | | G=9| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The White-wine Quality DatasetTesting Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 58. 1838 %| 50 %| 55. 5128 %| 51. 3514 %| Kappa statistic| 0. 3701| 0. 3671| 0. 2946| 0. 2454| Mean Absolute Error| 0. 1529| 0. 1746| 0. 1581| 0. 1628| Root Mean Squared Error| 0. 2808| 0. 3256| 0. 2887| 02972| Relative Absolute Error| 79. 2713 %| | 81. 9951 %| 84. 1402 %| * The Result Generated After Applying Multilayer Perceptron On Red-wine Quality Dataset Time taken to build model: 9. 14 seconds| Stratified cross-validation (10-Fold)| * Summary | Co rrectly Classified Instances | 880 | 61. 111 %| Incorrectly Classified Instances | 546 | 38. 2889 %| Kappa statistic | 0. 3784| | Mean absolute error | 0. 1576| | Root mean squared error | 0. 3023| | Relative absolute error | 73. 6593 %| | Root relative squared error | 92. 4895 %| | Total Number of Instances | 1426| | * Detailed Accuracy By Class | | TP Rate | FP Rate | Precision | Recall | F-Measure | ROC Area | Class| | 0 | 0 | 0 | 0 | 0 | 0. 47 | 3| | 0. 42 | 0. 005 | 0. 222 | 0. 042 | 0. 070 | 0. 735 | 4| | 0. 723 | 0. 249 | 0. 680 | 0. 723 | 0. 701 | 0. 801 | 5| | 0. 640 | 0. 322 | 0. 575 | 0. 640 | 0. 605 | 0. 692 | 6| | 0. 415 | 0. 049 | 0. 545 | 0. 415 | 0. 471 | 0. 831 | 7| | 0 | 0 | 0 | 0 | 0 | 0. 853 | 8| Weighted Avg. | 0. 617 | 0. 242 | 0. 595 | 0. 617 | 0. 602 | 0. 758| | * Confusion Matrix | A | B | C | D | E | F | | Class| | 0 | 5 | 1 | 0 | 0| || A=3| 0 | 2 | 34 | 11 | 1 | 0 | | | B=4| 0 | 2 | 436 | 160 | 5 | 0 | | | C=5| 0 | 5 | 156 | 369 | 47 | 0 | | | D=6| 0 | 0 | 10 | 93 | 73 | 0 | | | E=7| 0 | 0 | 0 | 8 | 8 | 0 | | | F=8| * Performance Of The Multilayer perceptron With Respect To A Testing Configuration For The Red-wine Quality Dataset Testing Method| Training Set| Testing Set| 10-Fold Cross Validation| 66% Split| Correctly Classified Instances| 68. 7237 %| 70 %| 61. 7111 %| 58. 7629 %| Kappa statistic| 0. 4895| 0. 5588| 0. 3784| 0. 327| Mean Absolute Error| 0. 426| 0. 1232| 0. 1576| 0. 1647| Root Mean Squared Error| 0. 2715| 0. 2424| 0. 3023| 0. 3029| Relative Absolute Error| 66. 6774 %| 51. 4904 %| 73. 6593 %| 77. 2484 %| * Result * The classification experiment is measured by accuracy percentage of classifying the instances correctly into its class according to quality attributes ranges between 0 (very bad) and 10 (excellent). * From the experiments, we found that classification for red wine quality using  Kstar algorithm achieved 71. 0379 % accuracy while J48 classifier achieved about 60. 7994% and Multilayer Perceptron classifier ac hieved 61. 7111% accuracy. For the white wine, Kstar algorithm yielded 70. 6624 % accuracy while J48 classifier yielded 58. 547% accuracy and Multilayer Perceptron classifier achieved 55. 5128 % accuracy. * Results from the experiments lead us to conclude that Kstar performs better in classification task as compared against the J48 and Multilayer Perceptron classifier. The processing time for Kstar algorithm is also observed to be more efficient and less time consuming despite the large size of wine properties dataset. 7. COMPARISON OF DIFFERENT ALGORITHM * The Comparison Of All Three Algorithm On White-wine Quality Dataset (Using 10-Fold Cross Validation) Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 1. 08| 35. 14| Kappa Statistics| 0. 5365| 0. 3813| 0. 29| Correctly Classified Instances (%)| 70. 6624| 58. 547| 55. 128| True Positive Rate (Avg)| 0. 707| 0. 585| 0. 555| False Positive Rate (Avg)| 0. 2| 0. 21| 0. 279| * Chart Shows The Best Suited Algorithm For Our Dataset (Measu res Vs Algorithms) * In above chart, comparison of True Positive rate and kappa statistics is given against three algorithm Kstar, J48, Multilayer Perceptron * Chart describes algorithm which is best suits for our dataset. In above chart column of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms. * In above chart you can see that the False Positive Rate and the Mean Absolute Error of the Multilayer Perceptron algorithm is high compare to other two algorithms. So it is not good for our dataset. * But for the Kstar algorithm these two values are less, so the algorithm having lowest values for FP Rate & Mean Absolute Error rate is best suited algorithm. * So the final we can make conclusion that the Kstar algorithm is best suited algorithm for White-wine Quality dataset. The Comparison Of All Three Algorithm On Red-wine Quality Dataset (Using 10-Fold Cross Validation) | Kstar| J48| Multilayer Perceptron| Time (Sec)| 0| 0. 24| 9. 3| Kappa Statistics| 0. 5294| 0. 3881| 0. 3784| Correctly Classified Instances (%)| 71. 0379| 60. 6994| 61. 7111| True Positive Rate (Avg)| 0. 71| 0. 608| 0. 617| False Positive Rate (Avg)| 0. 184| 0. 214| 0. 242| * For Red-wine Quality dataset have also Kstar is best suited algorithm , because of TP rate & Kappa statistics of Kstar algorithm is higher than other two algorithms and FP rate & Mean Absolute Error of Kstar algorithm is lower than other algorithms. . APPLYING TESTING DATASET Step1: Load pre-processed dataset. Step2: Go to classify tab. Click on choose button and select lazy folder from the hierarchy tab and then select kstar algorithm. After selecting the kstar algorithm keep the value of cross validation = 10, then build the model by clicking on start button. Step3: Now take any 10 or 15 records from your dataset, make their class value unknown(by putting ’? ’ in the cell of the corresponding raw ) as shown below. Step 4: Save this data set as . rff file. Step 5: From â€Å"tes t option† panel select â€Å"supplied test set†, click on to the set button and open the test dataset file which was lastly created by you from the disk. Step 6: From â€Å"Result list panel† panel select Kstar-algorithm (because it is better than any other for this dataset), right click it and click â€Å"Re-evaluate model on current test set† Step 7: Again right click on Kstar algorithm and select â€Å"visualize classifier error† Step 8:Click on save button and then save your test model.Step 9: After you had saved your test model, a separate file is created in which you will be having your predicted values for your testing dataset. Step 10: Now, this test model will have all the class value generated by model by re-evaluating model on the test data for all the instances that were set to unknown, as shown in the figure below. 9. ACHIEVEMENT * Classification models may be used as part of decision support system in different stages of wine productio n, hence giving the opportunity for manufacturer to make corrective and additive measure that will result in higher quality wine being produced. From the resulting classification accuracy, we found that accuracy rate for the white wine is influenced by a higher number of physicochemistry attribute, which are alcohol, density, free sulfur dioxide, chlorides, citric acid, and volatile acidity. * Red wine quality is highly correlated to only four attributes, which are alcohol, sulphates, total sulfur dioxide, and volatile acidity. * This shows white wine quality is affected by physicochemistry attributes that does not affect the red wine in general. Therefore, I suggest that white wine manufacturer should conduct wider range of test particularly towards density and chloride content since white wine quality is affected by such substances. * Attribute selection algorithm we conducted also ranked alcohol as the highest in both datasets, hence the alcohol level is the main attribute that d etermines the quality in both red and white wine. * My suggestion is that wine manufacturer to focus in maintaining a suitable alcohol content, may be by longer fermentation period or higher yield fermenting yeast.

Thursday, November 7, 2019

Essay on strategy of Whole Foods Market

Essay on strategy of Whole Foods Market Essay on strategy of Whole Foods Market Essay on strategy of Whole Foods MarketAmong various organizational diagnosis (OD) models, one of the most powerful models is the Nadler-Tushman congruence model. This model takes into accounts both internal and external factors, and helps to assess the alignment between the strategy of the company, its internal and external resources and actions (Falletta, 2005). The purpose of this paper is to analyze the existing strategy of Whole Foods Market and to determine Porters strategy which the company is pursuing now, to identify critical inputs of Whole Foods Market and to evaluate the alignment between these inputs and corporate strategy using the Nadler-Tushman congruence model.Nadler-Tushman Congruence ModelOD model developed by Nadler and Tushman is an invaluable instrument for analyzing organizational changes and assessing the organization in its environment. The key assumption of this model is the functioning of the organization in an open environment; in this context, an organiza tion is influenced by its inputs which include organizational history, resources, environment and strategy, and changes the environment by producing outputs at the individual, group and organizational level (Cameron Green, 2012). The core idea of Nadler-Tushman model is the focus on achieving congruence between organizational inputs, internal processes and factors, and outputs.In congruence model, organizational inputs include environmental factors (which encompass all factors external to the organization), resources (internal factors of the organization and the factors to which the organization has access), history (past behaviors, activities and performance which influence current functioning of the organization) and strategy (current stream of decisions aimed at achieving organizational goals in the organizational context) (Nadler Tushman, 1980).Strategy of Whole Foods MarketCurrent strategic goals announced by Whole Foods include market expansion, and in particular targeting l ow- and middle-income customer groups along with more affluent customers, increase of store footage, sales growth and reduction of expenses (Whole Foods Market, 2014). Among the strategic steps which have recently been undertaken by Whole Foods Market there are: launch of more affordable brands and marketing of organic products as affordable, focus on local sourcing and tailoring the offers to local community needs (Jargon, 2013), opening new stores in less affluent areas and opening smaller-sized stores. Furthermore, Whole Foods aims to reduce expenses by reducing the size of its existing superstores, reducing the amount of spoiled goods and optimizing its supply chain (AdWeek, 2013).In addition to this, Whole Foods reshaped its marketing approach. The company undertakes many marketing efforts such as flash sales of various items available for several hours, advice for customers how to reduce their expenses and advertising healthy food as the important component of health which oug ht not to be expensive (Watrous, 2014). Whole Foods also undertakes price matching against other competitors.One can identify three Porters generic strategies: cost leadership, differentiation and focus. Cost leadership and differentiation strategies refer to industry wide strategies: the former is based on offering lowest prices, while the latter is focused on offering a wide choice of products with unique qualities (Burke, Lake Paine, 2008). Focus strategies can be also targeting either cost or product qualities, but these strategies relate to particular market segments. Historically, Whole Foods was pursuing differentiation strategy, but the increase of competition in organic foods industry forced the company to switch to cost leadership strategy.Inputs of Whole Foods MarketWhole Foods Market emerged in 1980. The company has a rich history as it became the pioneer in organic foods industry. In the 1980s, the company focused on opening new stores, and in the end of 1990s and in t he 2000s Whole Foods primarily expanded through acquisitions. Whole Foods Market managed to establish own standards of quality and own ethical practices pertaining to growing and sourcing foods. These standards were often stricter than those of food industry in general. Whole Foods used to charge a high price premium for ensuring high quality of organic foods. In the context of history input for Nadler-Tushman congruence model, it is important to include such two inputs as high standards of quality (which are highly important for those customers who care about their health and choose organic food) and focus on affluent customers due to high price premiums (which shaped the perception of Whole Foods as the whole paycheck company in the past).Currently market position of Whole Foods is still strong: the company has a large network of stores, its superstores offer a wide range of organic foods, the company works with numerous suppliers of organic foods. Whole Foods expanded internation ally and is now also operating in Canada and in the UK. Key resources which are critical for the organization are its brand reputation associated with ethical practices and high-quality foods and its wide network of large stores.As for the external environment, the major factor which is affecting market position of Whole Foods are the increasing supply of organic foods (offered by private labels, small local stores, large retailers, etc.) and intensive competition in this market segment. These two phenomena can be viewed as one environmental factor increasing competition; this factor is the one that urges Whole Foods to reconsider its strategy and advertising approach.Congruence between inputs and strategyThe conclusions about the congruence of Whole Foods organizational inputs and strategy are ambiguous. On one hand, there is a clear controversy between historical inputs and current strategy: earlier the company positioned itself as a premium-segment brand and focused on attractin g affluent customers. In this way, Whole Foods managed to achieve high profit margins and high stock prices. Furthermore, most customers started associating high-quality organic products with high prices, so lowering prices might affect brand image of Whole Foods and create an impression among customers that the company is sacrificing quality to reduce price. Therefore, both historical inputs (high prices and high quality standards) and one resource input (brand reputation) are not congruent with the companys current strategy of cost/price leadership.Furthermore, the second resource input wide network of large stores is also poorly aligned with the strategy of cost   leadership. Large stores and wide range of various organic products naturally incur high costs and lead to price increase. Therefore, Whole Foods Market has to change its overall internal structure and approaches in order to implement the current strategy.At the same time, the new strategy is perfectly congruent wit h the critical environmental factor intensive competition in the sphere of organic foods. In this context, Whole Foods is attempting to reposition itself at the organic foods market in order to be able to compete with other large market players. In fact, it is the controversy between the key environmental factor with historical and resource inputs which leads to the conflict between the strategy and other inputs.Therefore, it is possible to state that Whole Foods is trying to change its business strategy completely, and therefore its historical developments are not congruent with its current strategic moves. At the same time, it is possible to recommend to Whole Foods to avoid such radical strategy reconstruction since the company might dilute its existing brand and lose its existing strengths without gaining any specific competitive advantages. In other words, it would be better for Whole Foods to pursue differentiation strategy instead of rapid expansion and cost leadership appro ach.

Tuesday, November 5, 2019

Edward Bernays, Father of Public Relations and Propaganda

Edward Bernays, Father of Public Relations and Propaganda Edward Bernays was an American business consultant who is widely regarded as having created the modern profession of public relations with his groundbreaking campaigns of the 1920s. Bernays attained clients among major corporations and became known for boosting their business by causing changes in public opinion. Advertising was already commonplace by the early 20th century. But what Bernays did with his campaigns was significantly different, as he didnt openly seek to promote a particular product the way a typical ad campaign would. Instead, when hired by a company, Bernays would set out to change the opinions of the general public, creating demand which would indirectly boost the fortunes of a particular product. Fast Facts: Edward Bernays Born: November 22, 1891 in Vienna AustriaDied: March 9, 1995 in Cambridge, MassachusettsParents: Ely Bernays and Anna FreudSpouse: Doris Fleishman (married 1922)Education: Cornell UniversityNotable Published Works: Crystallizing Public Opinion (1923),  Propaganda  (1928),  Public Relations  (1945),  The Engineering of Consent  (1955)Famous Quote: Whatever of social importance is done today, whether in politics, finance, manufacture, agriculture, charity, education, or other fields, must be done with the help of propaganda. (from his 1928 book Propaganda) Some of Bernays public relations campaigns failed, but some were so successful that he was able to create a thriving business. And, making no secret of his family relationship to Sigmund Freud- he was the nephew of the pioneering psychoanalyst- his work had the veneer of scientific respectability. Bernays was often portrayed as the father of propaganda, a title he did not mind. He maintained that propaganda was a laudable and necessary component of democratic government. Early Life Edward L. Bernays was born on November 22, 1891, in Vienna, Austria. His family emigrated to the United States a year later, and his father became a successful grain merchant on the New York commodity exchanges. His mother, Anna Freud, was the younger sister of Sigmund Freud. Bernays did not grow up in contact with Freud directly, though as a young man he did visit him. Its unclear how much Freud influenced his work in the publicity business, but Bernays was never shy about the connection and it no doubt helped him attract clients. After growing up in Manhattan, Bernays attended Cornell University. It was his fathers idea, as he believed his son would also enter the grain business and a degree from Cornells prestigious agriculture program would be helpful. Bernays was an outsider at Cornell, which was largely attended by the sons of farming families. Unhappy with the career path chosen for him, he graduated from Cornell intent on becoming a journalist. Back in Manhattan, he became the editor of a medical journal. Early Career His position at the Medical Review of Reviews led to his first foray into public relations. He heard that an actor wanted to produce a play that was controversial, as it dealt with the subject of venereal disease. Bernays offered to help and essentially turned the play into a cause, and a success, by creating what he called the Sociological Fund Committee, which enlisted notable citizens to praise the play. After that first experience, Bernays began working as a press agent and built a thriving business. During World War I he was rejected for military service due to his poor vision, but he offered his public relations services to the U.S. government. When he joined the governments Committee of Public Information, he enlisted American companies doing business overseas to distribute literature about Americas reasons for entering the war. After the end of the war, Bernays traveled to Paris as part of a government public relations team at the Paris Peace Conference. The trip went badly for Bernays, who found himself in conflict with other officials. Despite that, he came away having learned a valuable lesson, which was that wartime work changing public opinion on a grand scale could have civilian applications. Noteworthy Campaigns Following the war, Bernays continued in the public relations business, seeking out major clients. An early triumph was a project for President Calvin Coolidge, who projected a stern and humorless image. Bernays arranged for performers, including Al Jolson, to visit Coolidge at the White House. Coolidge was portrayed in the press as having fun, and weeks later he won the election of 1924. Bernays, of course, took credit for changing the publics perception of Coolidge. One of the most famous Bernays campaigns was while working for the American Tobacco Company in the late 1920s. Smoking had caught on among American women in the years following World War I, but the habit carried a stigma and only a fraction of Americans found it acceptable for women to smoke, especially in public. Bernays began by spreading the idea, through various means, that smoking was an alternative to candy and desserts and that tobacco helped people lose weight. He followed that up in 1929 with something more audacious: spreading the idea that cigarettes meant freedom. Bernays had gotten the idea from consulting with a New York psychoanalyst who happened to be a disciple of his uncle, Dr. Freud. Bernays was informed that women of the late 1920s were seeking freedom, and smoking represented that freedom. To find a way to convey that concept to the public, Bernays hit upon the stunt of having young women smoke cigarettes while strolling in the annual Easter Sunday parade on Fifth Avenue in New York City. Scene at 1929 Freedom Torches event arranged by Edward Bernays.   Getty Images The event was carefully organized and essentially scripted. Debutantes were recruited to be the smokers, and they were carefully positioned near particular landmarks, such as St. Patricks Cathedral. Bernays even arranged for a photographer to shoot images just in case any newspaper photographers missed the shot. The next day, the New York Times published a story on the annual Easter celebrations and a sub-headline on page one read: Group of Girls Puff at Cigarettes as a Gesture of Freedom. The article noted about a dozen young women strolled back and forth near St. Patricks Cathedral, ostentatiously smoking cigarettes. When interviewed, the women said the cigarettes were torches of freedom that were lighting the way to the day when women would smoke on the street as casually as men. The tobacco company was happy with the results, as sales to women accelerated. A wildly successful campaign was devised by Bernays for a longtime client, Procter Gamble for its Ivory Soap brand. Bernays devised a way of making children like soap by initiating soap carving contests. Children (and adults, too) were encouraged to whittle bars of Ivory and the contests became a national fad. A newspaper article in 1929 about the companys fifth annual soap sculpture contest mentioned that $1,675 in prize money was being awarded, and many contestants were adults and even professional artists. The contests continued for decades (and instructions for soap sculpture are still part of Procter Gamble promotions). Influential Author Bernays had started in public relations as a press agent for various performers, but by the 1920s he saw himself as a strategist who was elevating the entire business of public relations into a profession. He preached his theories on shaping public opinion at university lectures and also published books, including Crystallizing Public Opinion (1923) and Propaganda (1928). He later wrote memoirs of his career. His books were influential, and generations of public relations professionals have referred to them. Bernays, however, came in for criticism. He was denounced by the magazine Editor and Publisher as the young Machiavelli of our time, and he was often criticized for operating in deceptive ways. Legacy Bernays has been widely regarded as a pioneer in the field of public relations, and many of his techniques have become commonplace. For instance, the Bernays practice of forming interest groups to advocate for something is reflected daily in the commentators on cable television who represent interest groups and think tanks that seem to exist to confer respectability. Often speaking out in retirement, Bernays, who lived to the age of 103 and died in 1995, was often critical of those who seemed to be his heirs. He told the New York Times, in an interview conducted in honor of his 100th birthday, that any dope, any nitwit, any idiot, can call him or herself a public relations practitioner. However, he said he would be happy to be called the father of public relations when the field is taken seriously, like law or architecture. Sources: Edward L. Bernays. Encyclopedia of World Biography, 2nd ed., vol. 2, Gale, 2004, pp. 211-212. Gale Virtual Reference Library.Bernays, Edward L. The Scribner Encyclopedia of American Lives, edited by Kenneth T. Jackson, et al., vol. 4: 1994-1996, Charles Scribners Sons, 2001, pp. 32-34. Gale Virtual Reference Library.

Saturday, November 2, 2019

One of the Most Rewarding and Captivating Activities Essay

One of the Most Rewarding and Captivating Activities - Essay Example It is pure pleasure to be around children because they cannot be in one mood for a long time: one moment they are sad and the other they are definitely happy. Baking as a hobby is an interesting and useful activity, and it is often regarded as the highest level of culinary skills. When making some sweets, cakes or muffins it interesting to observe how a whole new piece of food appears out of nowhere: one moment you have only ingredients and the other people can taste your pastry. Combining these two joyful things: working with children and baking is a great chance to change the world here and now. Having an opportunity to help the children who live in low-class families and have no possibility to eat enough healthy food is a great chance to influence someone else`s life. At the age of twenty, she got this chance to influence the life of New Mexico community. Growing up really quickly she realized that she does not want to sit at home or play anymore and that she wanted to help other children to have a real childhood. That is how she became a volunteer making food for children who could not afford it. She dedicated one day a week to this work but was really happy to do it. As she was rather young nobody believed that she could do such a work well. On the first day when she got there, the woman who was also working in the kitchen laughed at and said that she had better go join other kids and play. But when she persuaded her to give her a chance and to help the woman soon realized that the young look had nothing to do with experience and expertise. She could only say â€Å"You know everything! What are you going to do at twenty?†. But there was so much to learn ahead that she barely found time for everything. Being a high school student she continued her charity work with children. By this time she has gained a lot of skills that were very useful in work with children especially disabled children. Those kids who have special needs require a lot of attention and care, and communication with them must be exceptionally effective.   Â