Development Tools:
The project was done using Python and Jupyter Notebook, with multiple libraries, the main one being Tensorflow. Other libraries include, but are not limited to:
Keras is built on top of TensorFlow and can be accessed or imported through TensorFlow.
Project-related information:
For in-depth information on the complete project, click the "Visit" button above.
In summary, the purpose of this project is to build a model that can predict the life expectancy of a person to a reasonable extent of accuracy, based on a set of 20 input parameters, namely:
- Developed or Developing status of the country the person is from
- Life Expectancy in age
- Adult Mortality Rates of both sexes (probability of dying between 15 and 60 years per 1000 population)
- Number of Infant Deaths per 1000 population
- Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol)
- Expenditure on health as a percentage of Gross Domestic Product per capita(%)
- Hepatitis B (HepB) immunization coverage among 1-year-olds (%)
- Measles - number of reported cases per 1000 population
- Average Body Mass Index of the entire population
- Number of under-five deaths per 1000 population
- Polio (Pol3) immunization coverage among 1-year-olds (%)
- General government expenditure on health as a percentage of total government expenditure (%)
- Diphtheria tetanus toxoid and pertussis (DTP3) immunization coverage among 1-year-olds (%)
- Deaths per 1 000 live births HIV/AIDS (0-4 years)
- Gross Domestic Product per capita (in USD)
- The population of the country
- Prevalence of thinness among children and adolescents for Age 10 to 19 (% )
- Prevalence of thinness among children for Age 5 to 9(%)
- Human Development Index in terms of income composition of resources (index ranging from 0 to 1)
- Number of years of Schooling(years)