Please provide me a assignment which is written as mention in the guidelines pdf and python program...

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Please provide me a assignment which is written as mention in the guidelines pdf and python program for the respective problem statement please be sure it's not plagiarized

Seite 1 von 5 EXAMINATION OFFICE IU.ORG WRITTEN ASSIGNMENT Tasks for Course: DLMDSPWP01 – Programming with Python CONTENT 1. Task ........................................................................................................................................... 2 1.1 Description of the Task ............................................................................................................................... 2 1.2 Details.......................................................................................................................................................... 3 1.3 Additional Task ........................................................................................................................................... 4 1.4 Remarks ...................................................................................................................................................... 4 2. Additional information for the evaluation of the written assignment .............................................. 5 3. Tutorial Support ......................................................................................................................... 5 Seite 2 von 5 EXAMINATION OFFICE IU.ORG 1. TASK The starting point for your term paper will be the course book, the contents of which will serve as the basis for an in-depth examination of one of the following questions. You are expected to research and cite from sources corresponding to your chosen topic. 1.1 Description of the Task You get (A) 4 training datasets and (B) one test dataset, as well as (C) datasets for 50 ideal functions. All data respectively consists of x-y-pairs of values. Structure of all CSV-files provided: X Y x1 y1 ... ... xn yn Your task is to write a Python-program that uses training data to choose the four ideal functions which are the best fit out of the fifty provided (C) *. i) Afterwards, the program must use the test data provided (B) to determine for each and every x-y- pair of values whether or not they can be assigned to the four chosen ideal functions**; if so, the program also needs to execute the mapping and save it together with the deviation at hand ii) All data must be visualized logically iii) Where possible, create/ compile suitable unit-test * The criterion for choosing the ideal functions for the training function is how they minimize the sum of all y- deviations squared (Least-Square) ** The criterion for mapping the individual test case to the four ideal functions is that the existing maximum deviation of the calculated regression does not exceed the largest deviation between training dataset (A) and the ideal function (C) chosen for it by more than factor sqrt(2) In order to give proof of your skills in Python related to this course, you need to adhere to certain criteria when solving the exercise; these criteria are subsequently described under ‘Details.’ Seite 3 von 5 EXAMINATION OFFICE IU.ORG 1.2 Details You are given four training datasets in the form of csv-files. Your Python program needs to be able to independently compile a SQLite database (file) ideally via sqlalchemy and load the training data into a single five- column spreadsheet / table in the file. Its first column depicts the x-values of all functions. Table 1, at the end of this subsection, shows you which structure your table is expected to have. The fifty ideal functions, which are also provided via a CSV-file, must be loaded into another table. Likewise, the first column depicts the x-values, meaning there will be 51 columns overall. Table 2, at end of this subsection, schematically describes what structure is expected. After the training data and the ideal functions have been loaded into the database, the test data (B) must be loaded line-by-line from another CSV-file and – if it complies with the compiling criterion – matched to one of the four functions chosen under i (subsection above). Afterwards, the results need to be saved into another four- column-table in the SQLite database. In accordance with table 3 at end of this subsection, this table contains four columns with x- and y-values as well as the corresponding chosen ideal function and the related deviation. Finally, the training data, the test data, the chosen ideal functions as well as the corresponding / assigned datasets are visualized under an appropriately chosen representation of the deviation. Please create a Python-program which also fulfills the following criteria: − Its design is sensibly object-oriented − It includes at least one inheritance − It includes standard- und user-defined exception handlings − For logical reasons, it makes use of Pandas’ packages as well as data visualization via Bokeh, sqlalchemy, as well as others − Write unit-tests for all useful elements − Your code needs to be documented in its entirety and also include Documentation Strings, known as ”docstrings“ Table 1: The training data's database table: X Y1 (training func) Y2(training func) Y3(training func) Y4(training func) x1 y11 y21 y31 y41 ... ... ... ... ... xn y1n y2n y3n y4n Seite 4 von 5 EXAMINATION OFFICE IU.ORG Table 2: The ideal functions’ database table: X Y1 (ideal func) Y2 (ideal func) ... Ym (ideal func) ... Y50 (ideal func) x1 y11 y21 ... ym1 ... y50_1 ... ... ... ... ... ... ... xn y1n y2n ... ymn ... y50_n The database table of the test-data, with mapping and y-deviation X (test func) Y (test func) Delta Y (test func) No. of ideal func x1 y11 y21 N1 ... ... ... ... xn y1n y2n y3n 1.3 Additional Task Assume that your successfully created project is on the Version Control System Git and has a Branch called develop. On this Branch, all operations of the developer team are combined. Write the Git-commands necessary to clone the branch and? develop on your local PC. Imagine that you have added a new function. Write all necessary Git-commands to introduce this project to the team’s develop Branch. Please note: You need the commands for commit, push. Afterwards,

test-uod0mokb.csv taskwrittenassignmentdlmdspwp01-co3vilpu.pdf ideal-lzzeuk1v.csv train-aloubpe5.csv guidelines-written-assignment-obyunupv.pdf

Sathishkumar · Accepted Answer

COVER PAGE
Table of Contents
1. Introduction………………………………………………………………………………..3
2. Main Body………………………………………………………………………………….8
3. Conclusion………………………………………………………………………………...15
References…………………………………………………………………………………...16
1.Introduction
In recent years, there has been a push to develop better techniques to exhibit big datasets to casual users. This effort has been driven by the need to improve accessibility. The need to broaden people's access to data has been the impetus behind this initiative. Data is continually being produced in brand-new volumes by businesses on a regular basis. Because of this, there is now a much higher quantity of information that can be accessed via the use of the internet. Users have a hard time comprehending such vast databases, navigating through them, and making effective use of the information that is stored inside them.
The process of doing scientific research relies heavily on one's capacity to visualise data. Large datasets are no longer a challenge for computers; they can now process them with ease. The generation of visual representations of data created by computers and the subsequent use of such representations are the fundamental aims of the field of data visualisation. It is a fantastic tool for visually presenting data that comes from a range of sources, and it can do it in a variety of ways. Because of this, analytics can now be shown visually, which makes it much simpler for decision-makers to understand the data. This facilitates faster and more accurate decision-making. After seeing patterns and fully comprehending the material at hand, one arrives at a conclusion with the intention of employing this information as a guide.
There are a few other names for the process that we refer to as data visualisation. Some of these other names include scientific visualisation and information visualisation. In order to leave an impression that is difficult to forget, humans have traditionally depended on graphical representations to transmit the information and messages they want to convey. Visuals have the ability to reveal information that can't be obtained via the use of any of the other senses [1].
1.1 Visualization Techniques
The practise of using a graphical representation of data that is supported by a computer is what is meant by the term "visualisation." The term "visualisation" may also be used to refer to this method. When opposed to static data visualisation, interactive data visualisation gives consumers the ability to choose the presentation format that will be used when the data is shown. This is in contrast to static data visualisation, which does not offer this choice.
The most common types of visualisation approaches are as follows, as seen in Figure 1:
· The link between the several things may be seen in this line graph. It is possible to utilise it to analyse changes that have occurred over a period of time.
· A bar chart is a useful tool for comparing the amounts of items that fall under various categories.
· A scatter plot is a kind of two-dimensional graphic that displays the fluctuation of two different variables.
· The components of a whole may be broken down and analysed using a pie chart.
Figure.1 Common visualisation techniques
The structure of graphs and charts may thus be shown in a number of ways, such as bar charts, pie charts, line graphs, and so on. It is crucial that you have a good grasp of the chart or graph that should be utilised for your data. This understanding may be gained via practise.
In the process of data visualisation, computer graphics are used to depict patterns, trends, and the connections that exist between the many different components of the data. The creation of pie charts, bar charts, scatter plots, and other types of data graphs may be accomplished with as little as a few clicks of the mouse and the use of pull-down menus. When some types of visualisation are conducted, careful attention is paid to the colours that are used in the process. If we are going to use colour to represent data, it is very necessary for us to choose colours that can clearly differentiate between the many aspects of the data that are being represented.
The steps of abstracting and summarising the data are included in the process of visualising the data. The data consists of a variety of necessary components, some of which are geographical variables such as position, size, and shape. These are only few of the fundamental components that make up the data. A system has to first minimise the total quantity of data, then transform the data, and then finally project the original information onto a screen in order for it to be able to effectively visualise data.
It is necessary for it to deliver the results in a format that is user-friendly and to represent the findings visually in the form of charts and graphs. 
1.2 Applications
The primary goals of most approaches to visualisation are to facilitate better decision-making and to act as cognitive enhancers. When planning and constructing a prototype of a data visualisation, one must make sure to let the intended use of the visualisation serve as their guidance. Data visualisation entails more than just displaying numerical values; it also requires choosing and considering the numerical values on which the representation is based [3].
Data visualisation is an important area of computer science that has a broad variety of applications in a variety of different fields. In many different areas of health and research, a variety of application-specific tools have been created in order to perform analysis on distinct datasets.
In the field of public health, having the skills to properly interpret and display data in a way that is easily digestible are absolutely necessary for achieving success in the field of public health monitoring. Researchers in the field of health require access to technologies that are both practical and cutting-edge [4]. In cloud-based medical data visualisations, security is a crucial consideration. If you pick up any contemporary medical or health publication, you are going to see a wide variety of graphical representations.
Calculating the amount of energy that is used in comparison to the amount that is produced is essential for finding the best solution [5].
Environmental science: Since environmental managers are tasked with making judgments based on very intricate data, they have a pressing need for visualisation tools. There is a growing trend toward the employment of visualisation techniques in applied environmental research [6]. It is advantageous to have a variety of programmes at one's disposal for the purpose of exhibiting research findings.
During the first phases of a fraud investigation, it is essential to visualise the data that has been collected. Data visualisation may be used as a proactive detection tool, allowing investigators of fraud to utilise the technique to identify trends that signal the presence of fraudulent activities [7].
The Methods That Are Utilized To Come To A Conclusion In Libraries Because of the flexibility that data visualisation tools provide, librarians have the ability to correctly organise and display information that has been collected from a variety of sources. This ability is made possible by the tools. They get the ability to deliver information in a manner that is both innovative and captivating as a result [8]. The visualisation of library data sheds insight on purchase choices as well as future library requirements and objectives. Students, instructors, and researchers may all benefit from the assistance of librarians in visualising their data [9], since librarians are de facto specialists in data visualisation.
Many different algorithms for the display of information and the software that goes along with them have been created. Users are able to evaluate data much more quickly than ever before because to these software programmes. These include Tableau and TOPCAT [10, 11], which were developed at the California Institute of Technology, as well as ManyEyes and Thoth. They make it simple to understand data visualisations and make the production of such infographics quick. Every instrument comes with its own set of advantages as well as drawbacks.
1.3 Regression and analysis
Academics that study consumer behaviour often make use of regression analysis as one of the most prevalent research methodologies available to them. Another common method is called a factor analysis. In the field of market research, a technique known as regression analysis is a strategy that is used to study the associations that exist between a single independent variable and a single dependent variable. The dependent variable in this situation is the intended outcome, which is an increase in sales, while the independent variables are the many marketing tools that are at our disposal and may be used to work toward obtaining the desired outcome. Perhaps one of our goals is to see a boost in sales, for instance. Income is a dependent variable (e.g., pricing or advertising). The quantity of information that can be obtained through the use of regression analysis may be compared to the findings that can be obtained through the use of very few other research techniques. One of these approaches is regression analysis.
1.It is possible to determine whether or not independent variables have a significant association with a dependent variable by utilising regression analysis, which is one of the primary advantages of adopting this method.
2.

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