How Has Film Influenced Lifestyles And Human Behavior In The 20th Cent

How Has Film Influenced Lifestyles and Human Behavior in the 20th Century? How Has Film Influenced Lifestyles and Human Behavior in the 20th Century? During the 20th century, film has been a powerful media in which to influence people's lifestyles and human behavior. Film is for people who do not enjoy reading or other more stimulating leisure and want to be entertained or escape from everyday life. Movies gave society a great way to see vintage fashion, including how to wear period accessories that accompany the clothing. Movies also gave society a view of actors portraying wartime heroes, rebels or gangsters, which may influence peoples human behavior. The film industry introduced flapper movies in the early days. The flapper wore short hair and a short skirt, with turned-down hose and powdered knees. The flapper must have seemed to her mother like a rebel. Flappers offended the older generation because they defied conventions of acceptable feminine behavior. They used make-up and wore baggy dresses, which often exposed their arms as well as their legs from the knees down. The flapper movies were modern and influenced a revolution in fashion. During the time of the Great Depression, film was a source of cheerful escapism for most. People were out of work, but they did manage to find money to go the movies. Even during the darkest days of the Depression, movie attendance was between 60-75 million per week. The balancing act for film making was to both reflect the realism and cynicism of the Depression period. They also provided escape entertainment to boost the morale of the public by optimistically reaffirming values such as thrift and perseverance. During The Golden Age of Hollywood, movies were under strict enforcement and censorship. Film studios submitted their films for review and if they met the strict standards of decency they could be released. Regulations of the code included censorship of language, references to sex, violence, and morality. Without a seal, films were threatened with negative publicity and potential box-office failure. Movies were not allowed to portray gangsters as heroes. Movies of this time, basically influenced people to have better moral standards. The American film industry was extremely prolific, affluent, powerful and productive during the war years. The world was headed toward rearmament and warfare in the early to mid-1940s, and the movie industry, like every other aspect of life, responded by making movies, producing many war-time favorites. These movies offered escapist entertainment, reassurance, and patriotic themes and morale boosters for the audience. In the period following the war, post-war affluence increased choice of leisure time activities, conformity, middle-class values, a baby boom, the invention of television, drive-in theaters, and a youth reaction to middle-aged cinema. When most of the films were idealized with conventional portrayals of men and women, young people wanted new and exciting symbols of rebellion. The film industry responded by producing a number movies with portrayals of young men and women rebelling against the establishment. "Rebel Without A Cause" was a movie about a rebellious, misunderstood, middle-class youth who had difficulty relating to his parents. This movie influenced the audience that it was okay to act in a rebellious way to get attention. When looking back on the film history of the 20th century you begin to realize the great impact these films had on people's lifestyles and human behavior. Movies influenced they way people dressed and the way people acted. We as, movie goers, must choose what is morally right or wrong and not be influenced by the film industry. We must also choose what is a fashion statement and what is not. The film industry may be protected under the freedom of speech amendment, but we do not have to be influenced by what they project in their movies.

Data mining titanic dataset Essays

Data mining titanic dataset Essays Data mining titanic dataset Paper Data mining titanic dataset Paper Titanic dataset Submitted by: Submission date 8/1/2013 Declaration Author: Contents Dated: 29/12/2012 The database corresponds to the sinking of the titanic on April the 15th 1912. It is part of a database containing the passengers and crew who were aboard the ship, and various attributes correlating to them. The purpose of this task is to apply the methodology of CRISP-DMS and follow the phases and tasks of this model. Using the classification method in rapid miner and both the decision tree and INN algorithms, I will create a training model and try apply the class survived or didnt survive. If I apply a decision tree to the dataset as it is, I get a prediction rate of 78%. I will try various techniques throughout this report to increase the overall prediction rate. Data mining objectives: I would like to explore the pre conceived ideas I have about the sinking of the titanic, and prove if they are correct. Was there a majority of 3rd class passengers who died? What was the ratio of passengers who died, male or female? Did the location of cabins make a difference as to who survived? Did chivalry ring through and did Women and children first actually happen? Data Understanding: Describe the data: Figure Class label: Survive (1 or O) 1 = survived, died. Type = Binomial. Total: 891. Survived: 342, Died: 549 Attributes: 10 attributes 891 rows The dataset have primarily a categorical type of attribute so there is low information content. This might indicate a decision tree would be an appropriate model to use. I can see that the number of rows in the dataset is indeed 10 to 20 times the number of columns, so the number of instances is adequate. There doesnt seem to be any inconsistencys in the data. Pappas: 1st, 2nd, or 3rd class. Type: polynomial. Categorical, 3rd class: 491, 2nd class: 216, 1st class: 184 0 missing Name: Name of Sex: Male, female. Type: binomial. Male: 577, Female: 314 0 missing Age: from 0. 420 to 80. Average age: 29, standard deviation of 14+-, Max was 80. 177 missing Sibs (Siblings on board): Type: integer. Average less than 1, highest 8. This suggested an outlier, but on inspection the names where there were 8 siblings corresponded. (The name was sage, 3rd class passengers, all died. ) O missing Parch: number of parents, children onboard. Type: integer. Average: 0. 3, deviation 0. 8. Max was 6. O missing Ticket: ticket number. Type: polynomial. To me these ticket numbers seem quite random and my first inclination is to discard them. O missing Fare: Cost of ticket. Type: real. Average: 32, deviation +- 49. Maximum 512. There seems to be quite a disparity in the range of values here. Three tickets cost 512, outliers? O missing Cabin: cabin numbers. Type: polynomial. 687 missing From looking at this data I think I can discount one of my initial questions about cabin numbers. If there was more data it might be an interesting factor as regards cabin locations and survival. As it stands the quality of the data is not good, there are Just o many missing entries. I. E. Greater than 40%. So I will delete (filter out) the cabin attribute from the dataset. The age attribute could cause a problem with the amount of fields missing. There are too many to delete. I might use the average of all ages to fill in the blanks. Explore the data: From an initial exploration of the data, I was able to look at various plots and found some interesting results. I have tried to keep my findings to my initial questions that I wanted answered. Was there a majority of 3rd class passengers who died? You can tell from Figure 2 that this was true. This graph Just shows survival by class, 3rd class fairing the worst. Again this is shown with a scatter plot but with the added attribute sex. You can see on the female side of the first class passengers, only a few died. Interestingly it shows that it was mostly male 3rd class passengers who perished, and it is demonstrated that more males then females died. There is a clear division in classes demonstrated. This graph answers my other question. What was the ratio of passengers who died, male or female? From this we can see that mainly males did not survive. Although there were more males on board (577), about 460 perished. From the females (314), about 235 survived. Another attribute that needs attention is the age category. I wanted to find out if the women and children first policy was adhered to, but there are 177 missing age values. This is going to complicate my results on this. From leaving the 177 as they are, I get this graph: but this is not conclusive in Figure 5. I thought that the fare price might indicate a childrens price and therefore allow me to fill in an age, but the fare price doesnt seem to have much pattern. Another idea I thought might help would be to look at the names of passengers, I. . Miss might signify a lower age. (In 1912 the average age of marriage was 22, so anyone with title miss could have an age less than 22. ) Names which include master might indicate a young age as well. Figure 5 also indicates possible outliers on the right hand side. From this graph I could easily see the breakdown of the different class of passenger and where they embarked from. It is obvious that Southampton had the largest number of passengers get on board. Question had the highest proportion of 3rd class passengers compared to 2nd and 1st class at that port, and its also interesting o note that this was an Irish port. This graph further explores the port of embankment and shows the survival rate from each, as well as the different classes. To me it seems that the majority of 3rd class passengers were lost who came from Southampton port, although they did have the highest amount of 3rd class passengers. A closer look at Southampton port. The majority who didnt survive were 3rd class (blue), also noted is the handful of 1st class passengers (green) who died, yet Southampton had the highest number of 1st class passengers to board. See figure 6. Verify data quality There were a number of missing values in the dataset. The highest amount of missing data came from the cabin attribute. As it is higher than 45% (687 missing) I decided to filter out this column. There are also 177 missing values from the age attribute. This amount of missing data is again too large a percentage to ignore and needs to be filled in. I can see that the dataset contains less than 1000 rows, so I think that sampling will not have to be performed. There doesnt seem to be any inconsistencys in the data. There are still 2 missing pieces of information from the embankment attribute. I see that they are 1st class passengers so from my graph on embankment I think I can put her embankment from Churchgoer. The other passenger is a George Nelson, which I will add to Southampton. I decided to filter out names also. I dont see how it can help in the dataset. It may have helped with age, by looking at the title as I said, but for this I Just used the average age to replace the missing values. Another approach to filling in the missing age fields might be linear regression. Remove possible outliers? I can see that there may be some outliers. For instance in the fares attribute, there re three tickets which cost 512 when the average is 32. They were first class tickets, but the difference is huge. Data Preparation: Here is the result of using x validation on the dataset before any data preparation has taken place. I will now sort out the problem of 667 cabin numbers missing. With it being higher than 40%, Vive decided to delete the attribute entirely. Vive also deleted the name attribute, as I dont see how it will help. By deleting cabin, name and ticket, here is the result I get: I replaced the missing age fields with the average of ages, this increased the accuracy lightly and gave these results with x validation: I used detect outliers and picked the top ten and then filtered them out. This gave this result: The class recall for survived has not improved much. Increasing the number of neighbors in the detect outliers operator improved things, also limiting the filter to deleting 5 made a better accuracy. I decided to use specified binning for the ages and broke the ages into three bins. For children aged up to 13, middle aged from 13 to 45, and older from 45 to 80. I tried different age ranges and found that these ranges yielded the best results. It did increase the accuracy. I also used binning for the fares, splitting them into low, mid, and high which also improved results on the confusion matrix. I used detect outlier to find the ten most obvious outliers, and then used a filter to get rid of them. I have decided to remove cabin from the dataset, and also there are 177 missing age values which I have tried various approaches in changing. I changed the ages to the average age, but this gives a spike in the number of ages 29. 7. Example of average age problem: Modeling: I tried to implement both the decision tree and inn algorithms, seeing as the dataset as primarily categorical. I found that inn yielded the best results regarding accuracy. This was set at k=l . The accuracy was not great at 73%. The parameter of K is too small and may be influenced by noise. INN: 5 worked the best at 82. 38%. This seems to be the optimal value for k, and the distance is set right. Class precision is about even on each class. Decision tree: The decision tree algorithm didnt give me as much accuracy, and I found that turning off pre pruning gave me a better accuracy. From the decision tree, the age binning seemed to predict middle aged males (13 to 45) with a low fare well. The class recall for survived was not great at 67. 85%. Generate Test Design I used x-validation to perform cross validation on the data. I initially used 20 for the number of validations, but then found 25 achieved a better result. I used the apply model and performance operators as these are best used for classification tasks and work well with the polynomial attribute. This then presented me with a confusion matrix where I could measure the accuracy of my model by comparing the accuracy, recall and precision. I found that throughout my various testing of operators and valuating the confusion matrix, raising the class recall on true 1 (survived) most difficult. After all my efforts I managed to raise it to 73. 6%. I. E. 91 were incorrectly predicted as surviving. Figure Final result Workspace: From my initial objectives I was able to determine the answers using rapidness. I wanted to find out if those who perished were in the majority 3rd class passengers. I found this to be true, and also that the majority who died were male 3rd class passengers. Female passengers and children fared better than most which leads me t o believe that the rule of women and children first applied. This may have been sighted more to the first and second class passengers as demonstrated in Figure 3. Because the dataset had such a large amount of data missing concerning age, this was more difficult to determine. I found the embarked attribute to be interesting in the graphs I could generate from it. There seemed to be a large number of 3rd class passengers who died that had embarked from Southampton. If all the cabin numbers were present I wonder if Southampton 3rd class passengers had cabins close to where the iceberg hit? Did this have a bearing on their survival? From the different algorithms I used I found that Inn yielded the better results.

Business report on A service experience for local consumers Essay

Business report on A service experience for local consumers - Essay Example The analysis revealed that there are some problems with customer service, product fit, promotional efforts, parking, employee involvement, and parking of Starbucks. Surprisingly, the packaging is the only aspect where there are no objections at all. The recommendations proposed in this report include first, offering discounts, coupons, free benefits, rebates, premiums, lotteries. Second, increase the volume of promotional activities. Third, train and manage employees in such a way that they could associate themselves with the company. Fourth, at off peak one employee must greet the customers at the door and the other must help customers with their parking problems. Table of Contents Introduction 4 Discussion 5 Target Market of Starbucks 5 Pricing 6 Promotion 7 People 8 Product 9 Process 10 Physical Evidence and Place 11 Recommendations 12 Conclusion 12 References 14 Appendices 15 Appendix # 1 – Questionnaire 15 Appendix # 2 – Respondent # 1 18 Appendix # 3 – Resp ondent # 2 19 Appendix # 4 – Respondent # 3 20 Appendix # 5 – Respondent # 4 21 Introduction It was in March 1971 in Seattle, when three people Jerry Baldwin, Zev Siegl, and Gordon Bowker decided to open their coffee shop with the name of Starbucks (Bussing-Burks, pp. 26-34, 2009). The name came from their favorite novel Moby-dick but at that time they did not realized that this name would remain restricted to Seattle, but it is going to rule the hearts and minds of millions of people all around the world (Pride & Ferrell, pp. 36-38, 2007). With 2010 revenue of more than 10.71 billion US dollars, Starbucks is the biggest coffee house on the planet earth. With its 16,858 stores in more than 50 countries, Starbucks is one the corporations that have been able to survive when its fellow brands were failing due to revelations of corporate scandals, manipulations, socially irresponsible behaviour but despite going through all this, it was able to make it through to what it i s today (Armstrong et al., pp. 312-317, 2009). Currently, Starbucks is operating almost 23 stores in various parts of Australia. This report is an attempt to explore, investigate, and critically examine the service experience of the Starbucks shop at 201 Elizabeth St, Sydney, New South Wales. The report would first start by drawing lines to highlight the target market of Starbucks in Australia. The report has collected its data from four different people that fit in the picture of Starbucks’s target market and have who have recently been to Starbucks. The data collection method was primarily through an interview questionnaire, which is a part of this report under the heading of Appendix # 1. Furthermore, the responses of these people, in form of the brief bullet points and notes are also there in the appendix section. This report would be profound importance of to the Starbucks executives of Australia. For the past few years, Starbucks has been struggling in Australia to prod uce the same results that it has been showing in rest of the world. It was in the year 2000, when Starbucks entered the Australian market with hopes that it would capture the market and become the market leader as it is in the US and many other parts of the world. Important here to note is that this was the time when Starbucks was touching its peak. â€Å"Open a store a day and beat the competitors away† was the policy of the company (Michelli, pp. 255-256, 2007). However, Starbucks soon realized that this model is not producing the intended results and in mid 2008,