Topic : Complex Engineering Problem
Subject : Data Science and Analysis
DataSet: Global Hunger Index (GHI_2023)
Sustainable Development Goal: Zero Hunger
Team : 20SW014,20SW034,20SW070
From all the 17 sustainable development goals (SDGs), we have chosen the ZERO HUNGER whose focus is to end hunger, achieve food security, improve nutrition, and promote sustainable agriculture.
INTRODUCTION
Concern Worldwide and Wealth hunger life published the Global Hunger Index (GHI). It was initially published in 2006 . The main target of the Global Hunger Index is to track the hunger or poverty level at international and regional levels in different areas of the world.
The Global Hunger Index (GHI) is calculated based on 4 parameters, which are
- Child Mortality
- Under-Nourishment
- Child Stunting
- Child Wasting
Real World Questions
We have three questions in mind for analysing and visualising the GHI (Global Hunger Index) Dataset. Two of these questions involve the application of Modeling techniques, while the third question focuses on utilising Statistical Analysis.
- The UN target in 2023 is to achieve a GHI of 18.2 for all countries. How many countries are above the target in 2023?
- For the UN to reach the target the mean of all country’s GHI will be close to zero or zero. Predict the year when?
- Let us see the impact of these 4 parameters (Child mortality, Under-nourishment, Child stunting and Child wasting) on the Global Hunger Index (GHI).
1: The UN target in 2023 is to achieve a GHI of 18.2 for all countries. How many countries are above the target in 2023?
Explanation
The target in 2023 is that at least all countries should have a GHI of 18.2. And there are a total of 136 countries in our dataset. So by applying methods it is concluded that 46 countries have a GHI of 18.2 from the total countries taken.
Results
Methodology: Statistical Analysis
Total Countries: 136
Countries having GHI >18.2 in 2023 : 42
2: For the UN to reach the target the mean of all country’s GHI will be close to zero or zero. Predict the year when?
Explanation
The solution we provided is addressing the question of when the mean Global Hunger Index (GHI) for all countries will be close to zero or zero. We did it by performing Linear Regression on historical GHI data to predict when this target might be reached.
The methodology involves data preparation, visualization, machine learning model training, prediction, and evaluation. It uses Linear Regression to model the relationship between mean GHI and years, allowing for predictions about when the mean GHI might approach zero.
The prediction shows that the UN will reach zero hunger in 2051 if the countries maintain their speed between 2000 and 2023. Our prediction model has an accuracy of 93%, meaning we have confidence in the predicted year.
Results
Model Used : Linear Regression Model
Year Predicted when mean of all country’s GHI will be Zero : 2052
Accuracy of Prediction Model: 91%
3: Let us see the impact of these 4 parameters (Child mortality, Under-nourishment, Child stunting and Child wasting) on the Global Hunger Index (GHI).
Explanation
The aim is to assess the impact of four parameters (Child Mortality, Under-nourishment, Child Stunting, and Child Wasting) on the Global Hunger Index (GHI) for the year 2023.
We are doing it by performing linear regression analysis using data and then making predictions based on different combinations of the four parameters. The code combines data from the 4 different sources, prepares it for analysis, performs linear regression to understand the relationship between the selected parameters and GHI, and makes predictions to assess the impact of these parameters on the GHI for the year 2023.
Results
Model Used: Linear Regression Model
Result : Child Wasting appears have a more significant impact than other parameters
Conclusion
In summary, the methodologies used in these questions involved data manipulation, statistical analysis, and machine learning techniques to answer specific queries related to global hunger and GHI. Each question was addressed using relevant data and analytical tools, providing insights into hunger-related issues.
