Global Hunger Index Analysis

 Global Hunger Index Analysis 

1. Introduction

Objective: Analyze the Global Hunger Index (GHI) data and correlate it with factors like GDP, population, and other socioeconomic indicators.
Purpose: Understand the underlying factors contributing to hunger and provide actionable insights to policymakers.

2. Project Workflow

1. Problem Definition:
   - Investigate the relationship between hunger levels and socioeconomic factors.
   - Key questions:
     - What factors contribute most to high hunger index values?
     - How does GHI vary with GDP and population?
2. Data Collection:
   - Source: GHI data from official reports, World Bank GDP datasets, and population data.
3. Data Preprocessing:
   - Clean, merge, and format datasets for analysis.
4. Analysis:
   - Perform correlation analysis between GHI and socioeconomic indicators.
5. Visualization:
   - Create charts and heatmaps to showcase findings.
6. Insights and Recommendations:
   - Develop insights for addressing hunger based on data.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Visualization: Matplotlib, Seaborn, Plotly
  - Statistical Analysis: Scipy, Statsmodels

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn plotly scipy statsmodels
```

Step 2: Load and Explore Datasets

Load the datasets (GHI, GDP, Population):
```
import pandas as pd

ghi_data = pd.read_csv("ghi_data.csv")
gdp_data = pd.read_csv("gdp_data.csv")
population_data = pd.read_csv("population_data.csv")

print(ghi_data.head())
print(gdp_data.head())
print(population_data.head())
```

Step 3: Preprocess and Merge Data

Clean and merge the datasets:
```
merged_data = pd.merge(ghi_data, gdp_data, on='Country')
merged_data = pd.merge(merged_data, population_data, on='Country')

print(merged_data.head())
```
Handle missing values and normalize data:
```
merged_data.dropna(inplace=True)
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
merged_data[['GDP', 'Population', 'GHI']] = scaler.fit_transform(merged_data[['GDP', 'Population', 'GHI']])
```

Step 4: Analyze Correlations

Perform correlation analysis:
```
correlation_matrix = merged_data.corr()
print(correlation_matrix)
```
Visualize the correlation matrix:
```
import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()
```

Step 5: Visualization

Create scatter plots for key relationships:
```
sns.scatterplot(data=merged_data, x='GDP', y='GHI')
plt.title('GHI vs GDP')
plt.show()

sns.scatterplot(data=merged_data, x='Population', y='GHI')
plt.title('GHI vs Population')
plt.show()
```

Step 6: Insights and Recommendations

1. Interpret the correlation coefficients and scatter plots.
2. Identify patterns or anomalies in the data.
3. Recommend strategies for reducing hunger based on findings.

5. Expected Outcomes

1. A comprehensive understanding of the relationship between hunger, GDP, and population.
2. Visualizations highlighting key trends and correlations.
3. Data-driven recommendations for addressing hunger effectively.

6. Additional Suggestions

- Include additional socioeconomic factors like education levels, healthcare access, and employment rates.
- Perform time-series analysis to observe trends over the years.
- Develop an interactive dashboard for stakeholders to explore the data.