Big Data and Analytics - Assignment
Added on 2021-05-31
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Big Data and AnalyticsAssignment 1- Data Analysis
Table of ContentsAbstract................................................................................................................................3Introduction..........................................................................................................................3Background..........................................................................................................................3Dashboard/report..................................................................................................................4Research...............................................................................................................................5Recommendations................................................................................................................4Reflection.............................................................................................................................5References............................................................................................................................52 | Page
Big Data and AnalyticsAssignment 1Data AnalysisAbstractThe research study is arranged for the analysis of big data by using the IBM Watson Data analytics tool. From this analysis, it is observed that the highest CFL count is noted in the month of October, median CFL count is observed in the month of March, while lowest CFL count is observed in the month of November. It is observed that the CFL count for the estimated age of sixty and over is highest and it is given as 190k. There is a 55% growth in the CFL count from the year 2012 to year 2015. It is observed that 2458 is the lowest total bathrooms by estimated age fifteen to nineteen. The top CFL count is observed for the Dark colour. The highest total flour count is observed in the month of October and it is given as 22.8k. Halogen count is observed highest in the month of October and it is observed lowest in the month of February. The median Halogen count is observed for the month of March. For optimization of the energy use, we need to implement several things such as use of efficient and modified electrical machines, use of CFL lights, etc. For the reduction in CO2 emissions Coal energy consumption should be minimized, because coal energy consumption produce CO2 emissions in a large proportion. The predictive model for the future energy use and CO2 emissions should include thenuclear energy, wind energy, solar energy, biomass energy, etc. Power usage is increasing from the year 2012 to year 2014 and again it decreases from year 2014 to year 2015. A linear relationship exists between the LED count and CFL count. Most affected factors for the prediction model are observed as suburban type, size, incandescent count, and living rooms.Introduction We know that the analysis of different data sets is required for taking decisions regarding the business, management, etc. Now a day, industries and businesses generates a big data and analysis of these big data sets is required for understanding the characteristics of the production or service. For the analysis of these types of big data sets, we need to use different statistical tools and techniques for the analysis. It becomes necessary to analyse the data from different industries for making effective decisions. Also, this data analysis provides the proper estimates for future use. Here, we have to analyse one such a big data set by using the IBM Watson analytics tool. This data set is related to power use or energy consumption by different types of 3 | Page
users. Statistical data analysis plays an important role in this new era of businesses and industries. It is important to use different statistical software’s for the analysis of big data. For optimization of the energy use, we need to implement several things such as use of efficient and modified electrical machines, use of CFL lights, etc. For the reduction in CO2 emissions Coal energy consumption should be minimized, because coal energy consumption produce CO2 emissions in a large proportion. The predictive model for the future energy use and CO2 emissions should include the nuclear energy, wind energy, solar energy, biomass energy, etc. Background The Federation University conduct a Solar Cities project for study of consumption of energy. This project involved the recruitment of the different households and businesses across the Loddon Mallee and Grampians regions. During this research study, changes in energy consumption were monitored by the researchers. Researchers find out all related factors which affects the energy consumption. Researchers also find out the relationship exists between the energy consumption and different variables that could influence energy consumption. These possible factors were divided into set of their features. Then researchers were taken the measurements for these factors. Given data set includes the sets of features such as adoption of solar energy technologies, geographic characteristics, physical characteristics of the dwellings, including such things as the dwellings age, size, number of stories , number of lights, insulation, etc. The main goal of this research study or project is to understand the drivers of power consumption, For this research study, researchers wants to find out the combination of features which could useful in the reduction of energy consumption. Also, researchers want to predict the model for future demand of energy use and CO2 emissions. Here, we have to study different patterns of energy uses and CO2 emissions for the given data set. Also, we will develop a predictive model for future energy use by using the IBM Watson Analytics tool. We have to analyse entire data set by using IBM Watson Analytics tool and then we have to make some discoveries. We have to study any useful facts from this data set, interesting insights, trends, and patterns regarding the energy consumption. Dashboard/ReportIn this section we have to analyse the given big data set by using IBM Watson Analytics tool. Given data set for the energy consumption have different variables and the list of these variables is summarised as below:Variable 1SUBURB 4 | Page
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