Exploratory Data Analysis (EDA) - Computer Science Assignment Help

Assignemnt Task

1 Objectives The goal of this assignment is to help you understand data manipulation and visualisation tools for machine learning. The purpose is to implement common data handling methods on real-world observations. Tb validate the effectiveness of the implemented methods, you are also required to perfonn data analysis taslcs to draw useful conclusions. In particular. the following topics should be reviewed: 
 

• Cross Industry S.. lard Process for Data Mining (CRISP-DM)

• Exploratory Data Analysis (EDA)

• Data Preparation

• Feature Manipulation 
 

These topics are (to be) covered in weelcs 4-6, but will also involve content from previous weeks. Research into online resources for AI and machine learning is encouraged. You are required to complete the following questions using data mining/machine learning tools introduced 00 lectures - Python and/or Orange (https://orangedatamining. . For each part, make sure you finish reading all the questions before you start working on it, and your report for the whole assignment should not exceed 10 pages (note dist this is a *maximum*, not a goal/target) with font size no smaller than 10. 
 

2 Question Description Happiness plays an important role in human emotion and personal growth. The World Happiness Report is an annual publication of the United Nations Sustainable Development Solutions Network, which may be a point of interest survey of the state of worldwide bliss. The report and related data are publicly available in their website (https ilworldhappiness .report). The report contains articles a. rankings of national happiness based on respondent ratings of their own lives. The data is adopted from the Gallup World Poll. The poll aslced living evaluation questions, known as the Can. Ladder, which asks respondents to rate their lives in a range of 0 to 10. Who makes the world, happiest countries so happy? In this assignment, let's try to answer this question by finding the most important factors that affect the happiness score in the past several years (Part 1 uses data from 2017 to 2019, and Part 2 uses data from 2021). The columns following the happiness score ("Ladder Sorel estimate the extent to which each of the s. factors - economic production, social support, life expectancy, freedom, absence of corruption, and generosity - coOribute to making life evaluations higher in one country than they are in Dystopia, a hypothetical country that has values equal to the world's lowest national averages for each of the six factors. Your taslcs in this assignment is to use data from years to discern the relationship between the happiness score

("Ladder Score.) with the six factors and residual (the last feature in the data). 
 

2.1 Part 1: Understanding Data From World Happiness Report The first part of this assignment is to explore the data. The task is to use CRISP-DM, EDA and data manipulation to define the machine learning tasks, understand, and prepare the data.

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