Life Expectancy and Its Factors an Analysis - IT Computer Science Assignment Help

Assignment Task

Task 

Abstract
The report comprises of a detailed analysis of Life Expectancy data acquired from World Health organisation [WHO]. The primary aim of the exercise is to identify the factors that influence life expectancy and build a model to predict life expectancy using these factors. The report explains the research question with help from previous works and literature in the Background section. The Methodology and Findings section covers the step-by-step process carried out right from exporting data, cleaning the dataset, building visualizations, and building the regression models. It also comprises of the findings from the models. All the regression models are then compared in the Comparison section to select the best model. The final summary of the analysis is then published in the Conclusion section. References for the report are mentioned in the penultimate section. Additional R code is provided in the Appendix.

Background
Life Expectancy is the average duration a human being is expected to live. Multiple factors influence the life cycle of human beings, which in turn contribute to this average value. It is a key tool in accessing the health across the community (Roser et al., 2019). This analysis is carried out to the answer the question “What factors affect the life expectancy, and to build an ideal model to predict the life expectancy based on these factors”. Previous studies (Chan and Devi, 2012) show that, citizens with higher education and a better income tend to remain healthy. Thus, schooling and Income in the dataset could be considered for further analysis. 

Mortality is defined as the death rate (Shiel, 2018) and as one would expect, higher the mortality, lower the life expectancy. It has been confirmed in a study (Woolf and Schoomaker, 2019) that, increase in mortality has resulted in a dip in life expectancy. Vaccination plays a key role in reducing the risk of infections, which would mean people are less susceptible to diseases (Andre et al., 2008). Consequently, the life expectancy is directly proportional to the percentage of people vaccinated against diseases like Hepatitis B, Polio and Diphtheria. A developed country would posses better medical infrastructure and amenities in comparison to a developing country. Better medical infrastructure means better health of the population. It is expected that a developed country has more life expectancy then a developing country (Rosenberg, 2019). 

Alcohol consumption proves to be harmful in most cases, leading to liver cancer and heart attacks. Countries with high alcohol intake are more susceptible to many fatalities, in turn reducing the life expectancy (Notzon et al., 1998). A study (Rabbi, 2013) states that infants who live through early life risks tend to have a higher life expectancy. Countries with lower infant deaths could see the possibility in an increase of life expectancy. HIV/Aids deaths reduce the life expectancy of a country. An article (Dejian et al., 1997) states that elimination of HIV deaths from the data significantly increased the life expectancy. This suggests that HIV deaths is inversely proportional to the life expectancy data of a country. Many organisations like WHO are conducting studies to analyse the life expectancy data. The primary aim of these studies is to identify ways in which the countries can increase their life expectancy. These factors will be further examined in the following sections using various Regression Analysis techniques. 

Methodology and Findings
The working directory in the R studio is checked and set to the desired path. The dataset obtained from Kaggle is then loaded in the R studio. The basic outline of the analysis is as follows:
1. The required libraries are loaded, and the data is read into the R studio.
2. The dataset is summarised using the Summary() function.
3. Data Quality issues are addressed.
4. Histograms are plotted for the chosen dependent variables.
5. The dataset is further adjusted by imputing missing values and trimming extreme values.
6. Training and Test datasets are generated.
7. Regression Models are built and compared.

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