DEV Community: Dasbang, F. Joseph

Francophone Africa: The Hidden Costs of French Influence

Dasbang, F. Joseph — Wed, 15 Nov 2023 03:45:10 +0000

Introduction:
Francophone Africa, the region of Africa where French is widely spoken, has a complicated and frequently tumultuous relationship with France, its former colonial power. France has retained a considerable political and economic influence over its former African colonies through a variety of tactics, including monetary, military, and diplomatic links. This influence, however, has not always benefited francophone African countries, as it has frequently come at the expense of their sovereignty, growth, and stability. In this study, I will look at historical and contemporary elements that contribute to instability and violence in francophone African countries influenced by France. I plan to use data analysis and visualization tools like Julia, R, and Python to investigate the effects of France’s policies and actions on various aspects of francophone African countries, such as military spending, support for authoritarian regimes, military interventions, the Human Development Index, and so on. I will also examine the consequences and problems of these impacts for francophone Africa’s future.

Research question:
What are the historical and contemporary factors that contribute to the instability and violence in Francophone African countries under France’s influence?

Data Sources:
One of the data sources for the research work is the Stockholm International Peace Research Institute (SIPRI) Military Expenditure Database, which provides consistent and comparable data on military spending for 172 countries since 1988. This database can help us analyze the trends and patterns of military expenditure in francophone African countries and how they relate to France’s influence and security challenges.

Another data source is the Wikipedia article on the Coup Belt, which is a term used to describe the region of sub-Saharan Africa that has experienced a high frequency of coups d’état since the 1960s. This article provides a list of coups and attempted coups in francophone African countries, as well as some background information on the causes and consequences of these events. This data source can help us understand the political instability and violence in francophone African countries and how they are influenced by France’s policies and actions.

A third data source is the Human Development Index (HDI) Data Center, which is a platform that provides access to various indicators of human development for 189 countries and territories. The HDI is a composite index that measures the average achievements in three basic dimensions of human development: a long and healthy life, access to knowledge, and a decent standard of living. This data source can help us assess the development outcomes and challenges in francophone African countries and how they are affected by France’s economic and social interventions.

These data sources can provide us with valuable insights into the hidden costs and consequences of France’s influence in francophone Africa. I’ll use data analysis and visualization tools, such as Julia and R and Python, to process, explore, and communicate these data in an effective and engaging way.

Data Preprocessing:
Importing Julia packages for Data cleanup:

# Load the packages
julia> using CSV, DataFrames, PrettyTables, CSVFiles, Dates 

# Loading CSV file 
julia> MEC = CSV.read("C:\\Users\\Military expenditure by Country.csv", DataFrame, missingstring=["xxx", "..."])
julia> HDI = CSV.read("C:\\Users\\HDI.csv", DataFrame)
julia> AC = CSV.read("C:\\Users\\Africa Coup 57 23.csv", DataFrame) 

# Select the first 4 columns and rows of MEC
julia> MEC_4x4 = MEC[1:4, 1:4]
julia> HDI_2x2 = HDI[1:2, 1:2]
julia> AC_4x4 = AC[1:4, 1:4]

# Print the data in a tabular form
julia> pretty_table(MEC_4x4)
julia> pretty_table(HDI_2x2)
julia> pretty_table(AC_4x4)

# Output
┌──────────────┬──────────┬─────────┬──────────┐
│      Country │     1949 │    1950 │     1951 │
│     String31 │ Float64? │  Int64? │ Float64? │
├──────────────┼──────────┼─────────┼──────────┤
│       France │   1211.3 │    1343 │   2114.5 │
│      Morocco │  missing │ missing │  missing │
│        Benin │  missing │ missing │  missing │
│ Burkina Faso │  missing │ missing │  missing │
└──────────────┴──────────┴─────────┴──────────┘

┌─────────┬────────────────────────────────────┐
│ Country │ Human Development Index (HDI) 2021 │
│  String │                            Float64 │
├─────────┼────────────────────────────────────┤
│  France │                              0.903 │
│   Gabon │                              0.706 │
└─────────┴────────────────────────────────────┘

┌────────────┬──────────┬────────────────────────────┬──────────────────┐
│       Date │  Country │                      Event │    Head of state │
│       Date │ String31 │                     String │         String31 │
├────────────┼──────────┼────────────────────────────┼──────────────────┤
│ 1957-06-01 │    Sudan │ 1957 Sudanese coup attempt │  Abdallah Khalil │
│ 1959-11-17 │   Sundan │ 1959 Sudanese coup attempt │   Ibrahim Abboud │
│ 1963-01-13 │  Dahomey │ 1963 Dahomeyan coup d'état │      Hubert Maga │
│ 1963-10-28 │     Togo │  1963 Togolese coup d'état │ Sylvanus Olympio │
└────────────┴──────────┴────────────────────────────┴──────────────────┘

Replacing the “missing” values with “NA”:

# Replace missing values with NA
julia> MEC = coalesce.(MEC, "NA")

# Output
┌──────────────┬────────┬──────┬────────┐
│      Country │   1949 │ 1950 │   1951 │
│     String31 │    Any │  Any │    Any │
├──────────────┼────────┼──────┼────────┤
│       France │ 1211.3 │ 1343 │ 2114.5 │
│      Morocco │     NA │   NA │     NA │
│        Benin │     NA │   NA │     NA │
│ Burkina Faso │     NA │   NA │     NA │
└──────────────┴────────┴──────┴────────┘

Using R for visualization:

I’ll be visualizing the MEC, HDI and AC data frame in this section.

Firstly, I’ll be using the MEC data frame to give an insight of how the Military expenditure of other Francophone countries is compared to its former Colonizer, France then followed by the HDI and lastly the AC data frames.

I’ll need to reshape the data frame to long format in line with the principles of “tidy data” advocated by Hadley Wickham, which make data manipulation and analysis more efficient. This long format is well-suited for creating time series visualizations and performing various data analyses, by doing this, you make your data more amenable to the capabilities of ggplot2 and other data analysis tools, making it easier to create informative and customizable visualizations.

# loading CSV file into R
# Load the required libraries
library(tidyr)
library(ggplot2)

# Load the data from the CSV file
MEC <- read.csv("C:\\Users\\Military expenditure by Country.csv", header = TRUE, stringsAsFactors = FALSE)

# Reshape the data to a long format
MEC_long <- pivot_longer(MEC, cols = -Country, names_to = "Year", values_to = "Expenditure")

# Create a line chart with rotated X-axis labels and bold values
ggplot(data = MEC_long, aes(x = Year, y = Expenditure, color = Country, group = Country)) +
    geom_line() +
    labs(x = "Year", y = "Expenditure (Billions USD)") +
    ggtitle("Military Expenditure by Country Over Time") +
    theme_minimal() +
    scale_x_discrete(labels = function(x) substr(x, 2, 5)) +
    theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 6, face = "bold"))

Output:

Analysis of the graph:

The graph depicts African countries’ expenditures from 1949 to 2022 in millions of US dollars. It encompasses 23 countries, including France, Morocco, Congo, Burkina Faso, Benin, and Francophone countries. It also includes island nations such as Gambia, Mali, Senegal, and Guinea Bissau.
France stands out in the graph due to a rise in spending over time. It peaked in 2014 at roughly 53,639 million USD. By 2020, it is expected to fall to around 47,371 million USD. France had the highest expenditure among the countries studied.

Morocco’s military budget has also increased. It was 24 million USD in 1956, but it is now the second greatest spender among the seven countries listed. Morocco’s military spending in 2018 totaled approximately $5,378 million USD. Despite the drop, Morocco will still spend about $4,995 million USD on military operations in 2020.
The graph clearly shows the increase in spending over time. Djibouti has consistently maintained expenditure with amounts ranging from 1.8 million USD in 1980 to around 36.3 million USD in 2008, as indicated in the chart.

France spends more on defense than any other Francophone country in Africa. In 2020, France will spend approximately 53.6 billion USD on its military, representing approximately 1.9% of its GDP.

Loading the HDI data frame:

HDI <- CSV.read("C:\\Users\\HDI.csv", DataFrame)

# Output
Table: HDI Data Frame

Country              HDI     GNI   Rank_2020   Rank_2021
------------------  ----  ------  ----------  ----------
France               0.9   45937          28          28
Gabon                0.7   13367         113         112
Equatorial Guinea    0.6   12074         147         145
Cameroon             0.6    3621         150         151

Visualizing and analyzing the Gross National Income per Capita (GNI) for France and Francophone countries using R:

# Select and Rename needed variables and assign them to a new data frame.
assign("HDIs", HDI %>% select(Country,`Human Development Index (HDI) 2021`, `Gross national income (GNI) per capita 2021`, `HDI rank 2020`, `HDI rank 2021`) %>% rename(HDI_2021 = `Human Development Index (HDI) 2021`, GNI_2021 = `Gross national income (GNI) per capita 2021`, Rank_2020 = `HDI rank 2020`, Rank_2021 = `HDI rank 2021`))

# Output Table: HDIs Data Frame

Country              HDI_2021   GNI_2021   Rank_2020   Rank_2021
------------------  ---------  ---------  ----------  ----------
France                    0.9      45937          28          28
Gabon                     0.7      13367         113         112
Equatorial Guinea         0.6      12074         147         145
Cameroon                  0.6       3621         150         151

colnames(HDIs) <- c("Country", "HDI_2021", "GNI_2021", "Rank_2020", "Rank_2021")

# Reshape the data frame to long format
library(tidyr)
HDIs_long <- pivot_longer(HDIs, cols = -Country, names_to = "Variable", values_to = "Value")

# Create a palette with 20 distinct colors
my_palette <- distinctColorPalette(20)

# Create a subset of the data for GNI_2021
GNI_data <- filter(HDIs_long, Variable == "GNI_2021")

# Create a subset of the data for HDI_2021, Rank_2020 and Rank_2021
HDI_data <- filter(HDIs_long, Variable != "GNI_2021")

# Creating a bar chart for GNI_2021
ggplot(GNI_data, aes(x = Country, y = Value, fill = Country)) +
geom_bar(stat = "identity") +
labs(title = "Gross National Income Per Capita for France & Francophone Countries",
x = "Country",
y = "Value",
fill = "Country") +
scale_fill_manual(values = my_palette) + 
theme_minimal() +
theme(text = element_text(size = 6), title = element_text(size = 8), legend.title = element_text(size = 6), legend.text = element_text(size = 5), axis.text.x = element_text(size = 6, angle = 90, hjust=1), axis.text.y = element_text(size = 8)) +
# Change the legend direction to horizontal
guides(fill=guide_legend(ncol=2))

Output:

Insight from the chart:

The graph depicts the GDP per capita for France and Francophone countries. GNP per capita is a measure of the money earned by citizens and residents of a country, regardless of where they live. It is computed by dividing a country’s total GNP by its population.

France vs. Francophone nations: The graph depicts a significant discrepancy in GNP per capita between France and the other Francophone countries. France has the largest GNP per capita, over $40,000, whereas the rest of the world has less than $10,000. Chad has the lowest GNP per capita, which is roughly $2,000. This implies that France is far more economically developed and affluent than the other countries with which it shares a language.

Possible factors: There could be many factors that explain the difference in GNI per capita between France and the other Francophone countries. Some of them are:

_1. History: France was a colonial power that exploited the natural resources and labor of many African and Asian countries. This created a legacy of inequality and underdevelopment in those regions.

Geography: France has a favorable location in Europe, with access to trade routes, markets, and technology. Many of the other Francophone countries are landlocked or have harsh climates, which limit their economic opportunities.
Politics: France has a stable and democratic political system, with strong institutions and rule of law. Many of the other Francophone countries have experienced civil wars, coups, corruption, and human rights violations, which undermine their social and economic development.
Education: France has a high-quality and universal education system, which produces skilled and productive workers. Many of the other Francophone countries have low literacy rates, high dropout rates, and poor quality of education, which affect their human capital and innovation potential._

Visualizing and analyzing the Human Development Index for France and Francophone countries:

# load the dplyr package
library(dplyr)
# Load ggplot2 package
library(ggplot2)

# read the csv file into a data frame called hdi_data
HDI <- read.csv("HDI 2021.csv")

# Create a subset of the data for HDI_2021
HDI_data <- select(HDIs, Country, HDI_2021)

# Define a custom palette with 20 distinct colors
my_palette <- distinctColorPalette(20)

# Generate a horizontal bar chart using ggplot2
ggplot(HDI_data, aes(x = Country, y = HDI_2021, fill = Country)) +
geom_bar(stat = "identity") +
coord_flip() + # Flip the coordinates to make the bars horizontal
labs(title = "Human Development Index for France & Francophone Countries 2021",
x = "Country",
y = "HDI_2021",
fill = NULL) +
scale_fill_manual(values = my_palette) + 
theme_minimal() +
theme(text = element_text(size = 12), title = element_text(size = 10), axis.text.x = element_text(size = 8), axis.text.y = element_text(size = 8)) +
geom_text(aes(label = HDI_2021), hjust = -0.1) # Add the values as text labels

Output:

Analysis from the chart:

The chart shows the Human Development Index (HDI) for France and Francophone countries in 2021. HDI is a measure of the quality of life based on life expectancy, education, and income. Here are some points I can make based on the chart:

France has the highest HDI among the Francophone countries, with a value of 0.9. This means that France has a high level of human development, with long life expectancy, high education, and high income.

_Burkina Faso has the lowest HDI among the Francophone countries, with a value of 0.4. This means that Burkina Faso has a low level of human development, with short life expectancy, low education, and low income.

There is a large gap in HDI between France and the other Francophone countries. The average HDI of the other countries is 0.6, which is much lower than France’s 0.9. This suggests that there is a significant difference in the quality of life between France and its former colonies or allies.

Some factors that may explain the HDI gap are history, geography, politics, and culture. France was a colonial power that exploited the resources and labor of many African and Asian countries, creating inequality and underdevelopment._

Visualizing and analyzing the Africa’s Coup Belt for Francophone and Non-Francophone Countries using Python:

# Importing the rquired libraries
import pandas as pd
from prettytable import PrettyTable
import matplotlib.pyplot as plt
import numpy as np

# Importing CSV file 
Afrofranc = pd.read_csv(r"C:\Users\fdasb\Documents\Project Write\Africa Coup 57 23.csv", encoding='utf-8-sig')

# View headers 
Afrofranc.head()
print(table)
+---------+----------+----------------------------+-----------------+
| Country |   Date   |           Event            |     Outcome     |
+---------+----------+----------------------------+-----------------+
|  Sudan  | 01-06-57 | 1957 Sudanese coup attempt |   Coup failure  |
|  Sundan | 17-11-59 | 1959 Sudanese coup attempt |   Coup failure  |
|  Benin  | 13-01-63 | 1963 Dahomeyan coup d'état | Coup successful |
|   Togo  | 28-10-63 | 1963 Togolese coup d'état  | Coup successful |
+---------+----------+----------------------------+-----------------+

# replace Sundan with Sudan in the Country column
Afrofranc["Country"] = Afrofranc["Country"].replace ("Sundan", "Sudan")

print(table)
+---------+----------+----------------------------+-----------------+
| Country |   Date   |           Event            |     Outcome     |
+---------+----------+----------------------------+-----------------+
|  Sudan  | 01-06-57 | 1957 Sudanese coup attempt |   Coup failure  |
|  Sudan  | 17-11-59 | 1959 Sudanese coup attempt |   Coup failure  |
|  Benin  | 13-01-63 | 1963 Dahomeyan coup d'état | Coup successful |
|   Togo  | 28-10-63 | 1963 Togolese coup d'état  | Coup successful |
+---------+----------+----------------------------+-----------------+

# get a numpy array of the unique values of Country
unique_values = Afrofranc["Country"].unique ()

# print the values of unique_values
print (unique_values)
['Sudan' 'Benin' 'Togo' 'Gabon' 'Central African Republic' "Cote d'ivoire"
 'Nigeria' 'Ghana' 'Sierra Leone' 'Mali' 'Niger' 'Chad' '\xa0Mauritania'
 'Central African Empire' 'Liberia' 'Guinea-Bissau' 'Mauritania'
 'The Gambia' 'Guinea' 'Cameroon' 'Burkina Faso']

# create a vector of Francophone countries
francophone = ["Benin", "Burkina Faso", "Burundi", "Cameroon", 
"Central African Republic", 
"Chad",
"Comoros",
"Congo",
"Djibouti",
"Equatorial Guinea",
"Gabon",
"Guinea",
"Ivory Coast",
"Madagascar",
"Mali",
"Niger",
"Rwanda",
"Senegal",
"Seychelles",
"Togo"]

# create a new column in the data frame with labels for Francophone and Non-francophone countries
Afrofranc["label"] = np.where(Afrofranc["Country"].isin(francophone), "Francophone", "Non-francophone")

Afrofranc.head()
print(table)
+---------+----------+-----------------+-----------------+
| Country |   Date   |     Outcome     |      label      |
+---------+----------+-----------------+-----------------+
|  Sudan  | 01-06-57 |   Coup failure  | Non-francophone |
|  Sudan  | 17-11-59 |   Coup failure  | Non-francophone |
|  Benin  | 13-01-63 | Coup successful |   Francophone   |
|   Togo  | 28-10-63 | Coup successful |   Francophone   |
+---------+----------+-----------------+-----------------+

# Create a bar chart of the number of coups by label and outcome
counts = Afrofranc.groupby(["label", "Outcome"]).count()["Country"]
labels = ["Francophone", "Non-francophone"]
outcomes = ["Coup failure", "Coup successful", "Inconclusive", "Military junta"]
colors = ["#FF0000", "#00FF00", "#FFFF00", "#800080"] # RGB hex codes for colors
x = np.arange(len(labels)) # the label locations
width = 0.1 # the width of the bars

# Reshape counts into a DataFrame
counts = counts.unstack()

fig, ax = plt.subplots()
for i, outcome in enumerate(outcomes):
    ax.bar(x + i * width, counts[outcome], width, label=outcome, color=colors[i])
    # Add annotations for each bar
    for j in range(len(labels)):
        ax.text(x=x[j] + i * width - width / 2, y=counts.loc[labels[j], outcome] + 0.5,
                s=' {:,.0f}'.format(counts[outcome][j]))

# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel("Number of coups")
ax.set_title("Coup Outcomes by Language Group in Africa (1957-2023)")
ax.set_xticks(x + width * 1.5)
ax.set_xticklabels(labels)
ax.legend()

fig.tight_layout()

plt.show()

Chart Output:

Analysis from the chart:

The chart shows the number of coups by language group and outcome in Africa from 1957 to 2023. A coup is a sudden and violent seizure of power from a government, usually by a small group of people. An outcome is the result of a coup, such as success, failure, inconclusive, or military junta.

Based on the chart, Here are some of the points that I found in relation to francophone and France in terms of coup d'état:

Francophone countries have a higher success rate: The image illustrates that Francophone countries have more successful coups (29) than Non-Francophone countries (32). This could imply that political systems in Francophone countries are more unstable, fragile, or violent. It could also be a result of French colonization and interventionism in these countries.

Both groupings of countries have a similar failure rate: The graph also reveals that both Francophone and Non-Francophone countries have the same number of coup failures (14 each). This could indicate that coup attempts in both categories of countries encounter similar challenges, such as a lack of coordination, popular opposition, or international criticism. It could also mean that the existing governments in both groups of countries have comparable degrees of legitimacy, support, or capacity to defend themselves.

Military juntas and inconclusive coups have a different trend: The image shows that Francophone and Non-Francophone countries have a different number of military juntas (1 and 0 respectively) and inconclusive coups (0 and 1 respectively) as a result of coups. This suggests that the outcomes of coups in Francophone nations are more decisive, leading to either regime change or the restoration of order. The consequences of coups in non-Francophone countries, on the other hand, are more uncertain, leaving the political situation unresolved or challenged. This could be due to these countries’ varying levels of institutionalization, polarization, or mediation.

Based on the above analysis, Here are further insight with reference of how France interferes in the political stability of the Francophone countries:

France’s historical legacy: France has a long history of colonialism and interventionism in Africa, especially in its former colonies that use the CFA franc as their currency. France has often supported authoritarian leaders and regimes that serve its economic and strategic interests, while ignoring or undermining the democratic aspirations and human rights of the African people. This has created resentment and distrust among many Africans, who see France as a neo-colonial power that exploits and manipulates their countries.

France’s military presence: France has maintained a significant military presence in Africa, with more than 3,000 troops deployed in Senegal, Ivory Coast, Gabon and Djibouti. France has also intervened militarily in several conflicts and crises, such as in Mali, Burkina Faso, and the Central African Republic, to fight against Islamist militants, protect its allies, and restore order. However, France’s military actions have also been criticized as being self-serving, ineffective, or counterproductive, as they have sometimes aggravated the local grievances, fueled the anti-French sentiment, or enabled the continuation of bad governance.

France’s diplomatic influence: France has tried to exert its diplomatic influence in Africa, both bilaterally and multilaterally, through various initiatives and platforms, such as the Africa-France Summit, the G5 Sahel, and the UN Security Council. France has also supported the regional organizations, such as the Economic Community of West African States (ECOWAS), in their efforts to promote democracy and peace in Africa. However, France’s diplomatic role has also been challenged by the emergence of other actors, such as China and Russia, who offer alternative models of partnership and development to the African countries. Moreover, France’s diplomatic stance has sometimes been inconsistent or contradictory, as it has claimed to respect the sovereignty and non-interference of the African countries, while also interfering in their domestic affairs when it suits its needs.

Based on the data sources from SIPRI, Wikipedia, and UNDP, and in response to the research question, here is the following summary and conclusion:

Summary: The historical and contemporary factors that contribute to the instability and violence in Francophone African countries under France’s influence are complex and interrelated. They include:

Poverty and underdevelopment: Most of the Francophone African countries rank low on the Human Development Index, which measures life expectancy, education, and income per capita. Food insecurity, malnutrition, disease, and environmental degradation are also issues. Poverty and underdevelopment foster social unrest, political upheaval, and violent conflict.

Colonial legacy and neo-colonialism: France colonized the Francophone African countries, exploiting their natural resources, imposing its culture and language, and suppressing nationalism movements. After independence, France maintained its economic and political influence over its former colonies through various mechanisms, such as the CFA franc, the pact document, the military bases, and the interventions . Many Africans see this as neocolonialism and dominance, and they hate and oppose France’s meddling in their affairs.

Ethnic diversity and marginalization: There are hundreds of different ethnic groups and languages in the Francophone African countries. However, colonial borders did not represent ethnic realities, and post-colonial republics frequently privileged some ethnic groups over others, resulting in grievances and tensions among marginalized communities. Political leaders and armed organizations have used ethnic diversity and marginalization to rally support and fuel bloodshed.

Weak governance and corruption: The Francophone African countries have experienced various forms of political instability, such as coups, civil wars, and authoritarian regimes. They are also plagued by poor governance and corruption, which jeopardize the rule of law, the delivery of public services, the accountability of institutions, and citizen trust. Weak governance and corruption have aided in the deterioration of social contracts, the formation of parallel organizations, and the escalation of violence.

Small arms proliferation and regional spillovers: The extensive availability and circulation of small arms and light weapons, which allow and exacerbate the use of violence, has an impact on Francophone African countries. Regional dynamics and spillovers, such as cross-border migrations of refugees, militants, and criminals, transnational networks of trafficking and smuggling, and the contagion effects of nearby conflicts, also have an impact. Proliferation of small guns and regional spillovers have enhanced the complexity and intensity of warfare.

Conclusion: The political instability and violence in Francophone African countries under France’s influence are the result of various and interrelated circumstances, which have historical and contemporary elements. These elements interact and reinforce one another, resulting in a vicious cycle of poverty, underdevelopment, neocolonialism, ethnic marginalization, poor governance, corruption, small arms proliferation, and regional spillovers. To address these factors and prevent further violence, a comprehensive and holistic approach is required, involving the participation and cooperation of all stakeholders, including Francophone African countries, France, regional and international organizations, and civil society. Such an approach should attempt to promote regional democracy, human rights, development, peace, and security.

Prompt Engineering 101: Learn the Skills You Need in 2023Learning Approach

Dasbang, F. Joseph — Thu, 05 Oct 2023 12:20:52 +0000

Introduction
This article compiles 10 abilities necessary to succeed as a prompt engineer.
Designing and creating efficient inputs for generative AI models, like GPT-3, that generate outputs in natural language is known as prompt engineering. Prompt engineers are required to create generative AI models that are relevant, accurate, coherent, and entertaining for a variety of applications and domains, as well as to optimize the interface between humans and the models. Prompt engineering calls for a variety of abilities, including:

Natural language processing (NLP): This is the use of a variety of methods and tools to comprehend and modify natural language input, such as text or speech. As rapid engineering entails generating natural language outputs from generative AI models, NLP is crucial.
Innovation and curiosity: These are the qualities that inspire engineers to investigate the potential and constraints of generative AI models and to devise fresh and useful interfaces. To create inputs that cause the models to produce the best results, prompt engineers must be imaginative and inquisitive.
Effective collaboration and communication:
Effective collaboration and communication with various teams and stakeholders, including developers, designers, product managers, and end users, are abilities of prompt engineers. To comprehend the needs and expectations of various applications and to offer feedback and suggestions for development, prompt engineers must interact and communicate efficiently.
Critical thinking and problem-solving skills:
Analyzing, assessing, and synthesizing information as well as using logic and reasoning to solve problems are critical thinking and problem-solving skills. When working with generative AI models, prompt engineers frequently run into flaws, inconsistencies, or unexpected behaviors' from the models, which requires them to think critically and address problems.
Data analysis and visualization: These are the competencies to collect, process, and present data in meaningful ways, using various tools and methods. Data analysis and visualization are useful for prompt engineering, as they help prompt engineers to measure and monitor the performance and quality of generative AI models, and to identify areas for improvement.
Domain knowledge: This is the familiarity with the specific field or industry that the generative AI model is intended for, such as marketing, education, healthcare, or entertainment. Domain knowledge is important for prompt engineering, as it helps prompt engineers to understand the context and purpose of the model's outputs, and to tailor the prompts accordingly.
Ethics and responsibility: These are the principles and values that guide prompt engineers to act in a moral and accountable manner when working with generative AI models. Ethics and responsibility are crucial for prompt engineering, as they ensure that prompt engineers respect the rights and interests of all parties involved, and that they avoid or mitigate any potential harm or misuse of the model's outputs.
Learning agility: This is the willingness and ability to learn new skills and knowledge quickly and effectively, especially in changing or uncertain situations. Learning agility is beneficial for prompt engineering, as it enables prompt engineers to adapt to the fast-paced and dynamic nature of generative AI development, and to keep up with the latest trends and innovations in the field.
programming: Writing code using one or more programming languages, such as Python, JavaScript, or SQL, is known as programming. Programming is useful for prompt engineering because it enables prompt engineers to interface the generative AI model with other platforms and systems and automate or customize various components of the prompt generating process.
User experience (UX) design: This is the process of developing goods or services that are user-friendly, reachable, and fulfilling for the target users. UX design is important to prompt engineering because it affects how prompt engineers format and arrange the prompts as well as how they assess the usability and aesthetic appeal of the model's outputs.

If you want to learn more about prompt engineering or become a prompt engineer, you can look at the following resources:

a. The Art of Prompt Engineering with ChatGPT - This book is designed to help you learn the art of working with ChatGPT in such a way that you get much better.

b. Prompt Engineering for Generative AI- With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice.

c. Prompt Engineering Academy - An online course that offers a comprehensive curriculum on prompt engineering.

Predicting Poverty Reduction in Nigeria: A Machine Learning Approach

Dasbang, F. Joseph — Wed, 27 Sep 2023 23:32:07 +0000

Introduction

The health, education, and general well-being of Nigeria's population, especially its young people, are severely impacted by multifaceted poverty and child deprivation. In Nigeria, poverty is defined as having insufficient access to essential goods and opportunities. To answer the crucial research issue, "Can machine learning methods effectively predict and prescribe poverty reduction in Nigeria with limited datasets?" this data science project was started. Data collection, preprocessing, model creation, and evaluation were all steps in my approach. I will evaluate the model's performance, draw conclusions, and offer suggestions for further initiatives in this final section.

Significance of Machine Learning
Utilizing cutting-edge machine learning methods offers a crucial chance to thoroughly understand and handle these complex problems. Modern algorithms can power predictive and prescriptive studies, which can offer priceless insights for resource allocation and wise policy decisions.

Research Question
"Can machine learning methods effectively predict and prescribe poverty reduction in Nigeria with limited datasets?"

Objectives
The main goal of this project is to develop an innovative framework that smoothly combines recurrent neural network (RNN) and Ensemble learning techniques in order to achieve the following:

Create predictive models using ensemble learning and recurrent neural networks (RNN) to predict multidimensional poverty in Nigerian subnational regions using a limited dataset.
Assess the models' accuracy and efficacy as well as the insight they generate.

Data Sources and Preprocessing
In this project, I used two datasets:

The Subnational multidimensional poverty data from Humanitarian Data Exchange published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford global Multidimensional Poverty Index (MPI) which measures multidimensional poverty in Nigeria.
The Multiple Indicator Cluster Survey (MICS) 2016–17, which is a household survey conducted by UNICEF that covers various indicators related to health, education, water and sanitation.

Installing Libraries:
In addition to Python, you'll need the Matplotlib plotting package for visualization, pandas data analysis module and Tensorflow for modeling. JupyterLab will be used to run the codes. You can set them up using:

conda install pandas matplotlib.pyplot

#or

pip install pandas matplotlib.pyplot

Importing datasets:

#import csv
import pandas as pd
mpi_df = pd.read_csv("C:\Users\Documents\MPI.csv")
mics_df = pd.read_csv("C:\Users\Documents\MICS.csv")

Viewing the head of the MPI dataset:

# Viewing the head of the MPI dataset
mpi_data.head()

# Viewing the head of the MICS dataset
mics_data.head()

#Output
+--------------------+------+----------------+--------------------+
| Subnational Region | Year | MPI of Nigeria |Population size by region           |
+--------------------+------+----------------+--------------------+
|        Abia        | 2018 |     0.254      |        2,966        |
|      Adamawa       | 2018 |     0.254      |        4,412        |
|     Akwa Ibom      | 2018 |     0.254      |        4,487        |
|      Anambra       | 2018 |     0.254      |        6,915        |
+--------------------+------+----------------+--------------------+

┌────────────────────┬───────────┬──────────┐
│ Subnational Region │ Nutrition │   Health │
│          String15? │ String15? │ Float64? │
├────────────────────┼───────────┼──────────┤
│               Abia │      34.6 │     46.5 │
│            Adamawa │     35.0  │     64.6 │
│          Akwa Ibom │      29.3 │     77.4 │
└────────────────────┴───────────┴──────────┘

Dropping Unwanted headers and Convert from Float to Integer:

mpi_df.drop("Unnamed: 16", axis=1, inplace=True)
mics_df.drop("Unnamed: 17", axis=1, inplace=True)

# Replace NaN values in the "Year" column with 0
mpi_df['Year'].fillna(0, inplace=True)

# Convert the "Year" column to integers
mpi_df['Year'] = mpi_df['Year'].astype(int)

Generating a bar chart using Matplot to show how multidimensional poverty spread across the Subnational Regions:

import matplotlib.pyplot as plt
import numpy as np

# Create a list of unique colors for each state
colors = [
    'red', 'green', 'blue', 'yellow', 'orange', 'purple', 'brown', 'pink',
    'cyan', 'magenta', 'lime', 'teal', 'lavender', 'turquoise', 'gold', 'maroon',
    'olive', 'navy', 'chocolate', 'limegreen', 'peru', 'indigo', 'deeppink', 'darkcyan',
    'lightcoral', 'mediumblue', 'darkgreen', 'darkred', 'darkviolet', 'saddlebrown', 'seagreen',
    'dodgerblue', 'lightgray', 'crimson', 'royalblue', 'indianred', 'darkslategray', 'skyblue'
]

# Create a list of Subnational Regions and their corresponding Number of MPI poor by region
subnational_regions = [
    'Abia', 'Adamawa', 'Akwa Ibom', 'Anambra', 'Bauchi', 'Bayelsa', 'Benue', 'Borno', 
    'Cross River', 'Delta', 'Ebonyi', 'Edo', 'Ekiti', 'Enugu', 'FCT', 'Gombe', 'Imo', 
    'Jigawa', 'Kaduna', 'Kano', 'Katsina', 'Kebbi', 'Kogi', 'Kwara', 'Lagos', 'Nasarawa', 
    'Niger', 'Ogun', 'Ondo', 'Osun', 'Oyo', 'Plateau', 'Rivers', 'Sokoto', 'Taraba', 'Yobe', 'Zamfara'
]

mpi_poor_values = [
    277, 2759, 933, 595, 5520, 503, 2208, 4504, 791, 936, 2400, 553, 551, 904, 406, 3085, 676, 
    5930, 6499, 8993, 9049, 4906, 890, 1428, 511, 1213, 4739, 554, 766, 997, 1407, 2236, 1096, 
    4053, 2846, 5955, 5031
]

# Create the bar chart with unique colors for each state
plt.figure(figsize=(12, 8))
bars = plt.barh(subnational_regions, mpi_poor_values, color=colors)

# Adding data labels at the tip of each bar
for bar, num_mpi_poor in zip(bars, mpi_poor_values):
    width = bar.get_width()
    plt.text(width + 20, bar.get_y() + bar.get_height() / 2, num_mpi_poor, va='center', fontsize=10)

# Remove x-axis and ticks
plt.gca().get_xaxis().set_visible(False)

plt.title('Number of MPI Poor by Subnational Region (Year 2018)')

# Save the chart as a PNG file
plt.savefig('mpi_poor_by_region.png', bbox_inches='tight')

# Display the chart
plt.show()

Chart Output:

Generating Stacked chart, showing how deprivation form Multiple factors such as Nutrition, Housing, Sanitation, Education, etc. cause poverty:

import matplotlib.pyplot as plt

# Plot the stacked bar chart
ax = mics_df.plot(kind="bar", stacked=True, figsize=(12, 8))

# Create a dictionary to map numeric values to state names
state_names = {
    0: "Abia", 1: "Adamawa", 2: "Akwa Ibom", 3: "Anambra", 4: "Bauchi", 5: "Bayelsa", 6: "Benue", 7: "Borno",
    8: "Cross River", 9: "Delta", 10: "Ebonyi", 11: "Edo", 12: "Ekiti", 13: "Enugu", 14: "FCT", 15: "Gombe",
    16: "Imo", 17: "Jigawa", 18: "Kaduna", 19: "Kano", 20: "Katsina", 21: "Kebbi", 22: "Kogi", 23: "Kwara",
    24: "Lagos", 25: "Nasarawa", 26: "Niger", 27: "Ogun", 28: "Ondo", 29: "Osun", 30: "Oyo", 31: "Plateau",
    32: "Rivers", 33: "Sokoto", 34: "Taraba", 35: "Yobe", 36: "Zamfara"
}

# Create a custom legend for Subnational Region names
custom_legend = [
    plt.Line2D([0], [0], marker='o', color='w', label=f'{i} - {state_names[i]}',
               markersize=10, markerfacecolor='C0')
    for i in range(len(mics_df.index))
]

# Create a legend for the Factors
factors_legend = ax.legend(handles=custom_legend, title="Subnational Regions", bbox_to_anchor=(1.05, 1), loc='upper left')

# Add a legend for the Factors to the right
ax.legend(title="Factors", bbox_to_anchor=(1.05, 0), loc='lower left')

# Add title and labels
plt.title("Multiple Indicator Cluster by Subnational Region")
plt.xlabel("Subnational Region")
plt.ylabel("")

# Show the chart
plt.show()

Chart Output:

Merging data frame:

# Merging both data frame using common header
merged_df = pd.merge(mpi_df, mics_df, on="Subnational Region")

# Viwing the heder in a tabular form by selecting desired header
selected_headers = ['Subnational Region', 'Year', 'MPI of Nigeria', 'Education']
num_rows_to_display = 4
table = PrettyTable(selected_headers)
for _, row in merged_df[selected_headers][:num_rows_to_display].iterrows():
    table.add_row(row)
print(table)

#Output indicationg both data frame were successfully merged
+--------------------+------+----------------+-----------+
| Subnational Region | Year | MPI of Nigeria | Education |
+--------------------+------+----------------+-----------+
|        Abia        | 2018 |     0.254      |    9.9    |
|      Adamawa       | 2018 |     0.254      |    49.9   |
|     Akwa Ibom      | 2018 |     0.254      |    15.6   |
|      Anambra       | 2018 |     0.254      |    7.6    |
+--------------------+------+----------------+-----------+

Importing libraries for training model:
At this point, I'll be training a machine learning model using Recurrent Neural Network (RNN) to predict and prescribe poverty reduction in Nigeria using the merged dataset containing various socio-economic indicators, I'll then use Ensemble method to improve predictive performance and robustness of the model and finally apply early stopping to prevent overfitting.

import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
from sklearn.utils.class_weight import compute_class_weight

# Define categorical and numerical features
categorical_features = [
    'Subnational Region',
    'Year of the survey',
]
numerical_features = [
    'Population 2019',
    'Population 2020',
    'Population share by region',
    'Population size by region',
    'Number of MPI poor by region',
    'Nutrition',
    'Health',
    'Water',
    'Sanitation',
    'Housing',
    'Information',
    'Education',
    'Water.1',
    'Sanitation.1',
    'Housing.1',
    'Information.1',
    'Education.1',
    'Water.2',
    'Sanitation.2',
    'Housing.2',
    'Information.2',
    'Intensity of deprivation among the poor',
    'Vulnerable to poverty',
    'In severe poverty',
]

Encode categorical features:
I'll need to encode categorical features because Categorical data needs to be converted to numerical format for machine learning algorithms to process them.

# Encode categorical features
label_encoders = {}
for feature in categorical_features:
    if feature != 'Year of the survey':
        le = LabelEncoder()
        merged_df[feature] = le.fit_transform(merged_df[feature])
        label_encoders[feature] = le

Split the data into features and target:
To separate the input features (independent variables) from the target variable (dependent variable)

# Split the data into features and target
X = merged_df.drop(columns=['Multidimensional Poverty Index'])
y = merged_df['Multidimensional Poverty Index']

Standardize numerical features:
Standardization makes different numerical features comparable by scaling them to have a mean of 0 and a standard deviation of 1.

# Standardize numerical features
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X[numerical_features] = scaler.fit_transform(X[numerical_features])

# Specify all the columns you want from 'merged_df'
selected_columns = ['Year', 'Subnational Region', 'MPI of Nigeria',
       'Multidimensional Poverty Index',
       'Population in multidimensional poverty',
       'Intensity of deprivation among the poor', 'Vulnerable to poverty',
       'In severe poverty', 'Year of the survey', 'Population 2019',
       'Population 2020', 'Population share by region',
       'Population size by region', 'Number of MPI poor by region',
       'Nutrition', 'Health', 'Water', 'Sanitation', 'Housing', 'Information',
       'Education', 'Water.1', 'Sanitation.1', 'Housing.1', 'Information.1',
       'Education.1', 'Water.2', 'Sanitation.2', 'Housing.2', 'Information.2']

# Create a new DataFrame using all the selected columns from 'merged_df'
new_df = merged_df[selected_columns]

Identify the target variable:
To determine the variable you want to predict or model.

# Identify the target variable
target_variable = 'Multidimensional Poverty Index'

# Convert the target variable to string type
new_df[target_variable] = new_df[target_variable].astype(str)

# Check the unique values in the target variable
unique_values = new_df[target_variable].unique()

Check if any class has only one member:
To ensure class balance to prevent issues with imbalanced datasets.

# Check if any class has only one member
classes_with_one_member = [val for val in unique_values if new_df[target_variable].value_counts()[val] == 1]

Now that I've identified and flagged the classes with only one member, I'll need to combine the rare classes with a more frequent class. However, I need to define a logical rule, this rule should specify which rare classes will be combined and how they will be combined. I need to combine all rare classes with a frequency below a certain threshold into a single class called "Combined Rear Class"

if len(classes_with_one_member) > 0:
    print(f"Classes with only one member: {classes_with_one_member}")

    # Combine Rare Classes
    rare_class_threshold = 5
    for val in classes_with_one_member:
        if new_df[target_variable].value_counts()[val] < rare_class_threshold:
            # Combine the rare class with a more frequent class
            new_df[target_variable] = new_df[target_variable].replace(val, 'Combined Class')

# Output
Classes with only one member: 
['0.33', '0.45', '0.17', '0.34', '0.08', '0.19', '0.31', '0.4', '0.59', '0.15', '0.21', '0.02', '0.16', '0.38', '0.26', '0.06', '0.56', '0.54', '0.48']

Split the data into training and test sets:
Create separate datasets for model training and evaluation to assess model performance.

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
    new_df.drop(columns=[target_variable]),  # Features excluding the target variable
    new_df[target_variable],  # Target variable
    test_size=0.2,  # Adjust the test size as needed
    random_state=0,
    stratify=new_df[target_variable]  # Ensure stratified splitting
)

Now I can proceed with model training and evaluation.
Check the distribution of the combined target variable:
Understand the distribution of the target variable to identify potential issues or biases in the dataset.

combined_target = new_df[target_variable]

# Check the distribution of the combined target variable
class_distribution = combined_target.value_counts()

print(class_distribution)

# Output
Combined Class    19
0.09               7
0.12               3
0.04               2
0.49               2
0.05               2
0.37               2
Name: Multidimensional Poverty Index, dtype: int64

The "Combined Class" has 19 samples, which is more than the separate classes '0.12,' '0.04,' '0.49,' '0.05,' and '0.37.' This implies that the "Combined Class" is no longer the smallest class and is more balanced when compared to these specialized classes. I may not need to calculate class weights in this situation because the class distribution is generally balanced after combining unusual classes. So go ahead and train the model.

Now I'll need to define the RNN Model with a function that defines the RNN model's architecture. It's a straightforward RNN model with an embedding layer, an LSTM layer, and a dense layer with sigmoid activation.

# Define the RNN Model:

def create_rnn_model(input_dim):
    model = tf.keras.models.Sequential([
        tf.keras.layers.Embedding(input_dim, 128),
        tf.keras.layers.LSTM(64),
        tf.keras.layers.Dense(1, activation="sigmoid")
    ])
    model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
    return model

Create an Ensemble of RNNs:
In this section, I'll use stratified k-fold cross-validation to build an ensemble of RNNs. Multiple RNN models are trained on distinct subsets of the training data by the code.

# Create an Ensemble of RNNs
from sklearn.preprocessing import LabelEncoder

# Create a label encoder instance
label_encoder = LabelEncoder()

# Fit the label encoder on the target variable and transform it
y_train_encoded = label_encoder.fit_transform(y_train)
y_test_encoded = label_encoder.transform(y_test)

# Check for values outside the expected range of 0 - 29
min_expected_value = 0
max_expected_value = 29

# Identify values outside the range
out_of_range_indices = (X_train_fold < min_expected_value) | (X_train_fold > max_expected_value)

# If any values are outside the range, adjust them to the nearest boundary value
if out_of_range_indices.any().any():
    X_train_fold = np.clip(X_train_fold, min_expected_value, max_expected_value)

# Create a new model with the adjusted embedding size
new_embedding_size = 65  # You can adjust this size as needed
model = create_rnn_model(input_dim=new_embedding_size)

# Train the model with the adjusted data
model.fit(X_train_fold, y_train_fold, epochs=10)
ensemble_models.append(model)

# Output
Epoch 1/10
1/1 [==============================] - 6s 6s/step - loss: 0.8241 - accuracy: 0.0435
Epoch 2/10
1/1 [==============================] - 0s 21ms/step - loss: 0.6181 - accuracy: 0.0435
Epoch 3/10
1/1 [==============================] - 0s 30ms/step - loss: 0.4171 - accuracy: 0.0435
Epoch 4/10
1/1 [==============================] - 0s 26ms/step - loss: 0.2095 - accuracy: 0.0435
Epoch 5/10
1/1 [==============================] - 0s 31ms/step - loss: -0.0125 - accuracy: 0.0435
Epoch 6/10
1/1 [==============================] - 0s 25ms/step - loss: -0.2572 - accuracy: 0.0435
Epoch 7/10
1/1 [==============================] - 0s 39ms/step - loss: -0.5360 - accuracy: 0.0435
Epoch 8/10
1/1 [==============================] - 0s 32ms/step - loss: -0.8657 - accuracy: 0.0435
Epoch 9/10
1/1 [==============================] - 0s 24ms/step - loss: -1.2690 - accuracy: 0.0435
Epoch 10/10
1/1 [==============================] - 0s 31ms/step - loss: -1.7772 - accuracy: 0.0435he model has been successfully trained with the adjusted data. The training has completed for 10 epochs, and the accuracy and loss values have been computed for each epoch.

The model has been successfully trained. The training has completed for 10 epochs, and the accuracy and loss values have been computed for each epoch. I can now proceed with ensemble of models.

# Check for values less than 0
min_values = np.min(X_train, axis=0)
values_below_0 = min_values < 0
print("Columns with values below 0:", np.where(values_below_0)[0])

# Check for values greater than or equal to 65
max_values = np.max(X_train, axis=0)
values_above_or_equal_to_65 = max_values >= 65
print("Columns with values above or equal to 65:", np.where(values_above_or_equal_to_65)[0])

# Output
Columns with values below 0: [ 4  5  6 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28]
Columns with values above or equal to 65: [0 3 7]

Based on the output its clear there are columns that the code identify that are problematic or having out-of-range values. To solve this issue, one sure way is to adjust the clipping range of the values. (Clipping is a technique that limits the values of your data to a specified range, so by adjusting the clipping range, you can control how values outside that range are handled)

# Check X_val_clipped
max_value_val = np.max(X_val_clipped.values)
if max_value_val >= 65:
    print("Warning: X_val_clipped contains values greater than or equal to 65.")
else:
    print("All values in X_val_clipped are less than 65.")

# Check X_train_clipped
max_value_train = np.max(X_train_clipped.values)
if max_value_train >= 65:
    print("Warning: X_train_clipped contains values greater than or equal to 65.")
else:
    print("All values in X_train_clipped are less than 65.")

# Output
Warning: X_train_clipped contains values greater than or equal to 65.
Warning: X_val_clipped contains values greater than or equal to 65.

# Adjust clipping range for X_val
X_val_clipped = np.clip(X_val, 0, 65)

# Adjust clipping range for X_train
X_train_clipped = np.clip(X_train, 0, 65)

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train_clipped)
X_val_scaled = scaler.transform(X_val_clipped)

for model in ensemble_models:
    model.fit(X_train_scaled, y_train, epochs=10, batch_size=32)  # Adjust epochs and batch_size as needed

# Output
Epoch 1/10
1/1 [==============================] - 2s 2s/step - loss: 0.8182 - accuracy: 0.0000e+00
Epoch 2/10
1/1 [==============================] - 0s 34ms/step - loss: 0.8372 - accuracy: 0.0000e+00
Epoch 3/10
1/1 [==============================] - 0s 23ms/step - loss: 0.8499 - accuracy: 0.0000e+00
Epoch 4/10
1/1 [==============================] - 0s 30ms/step - loss: 0.8544 - accuracy: 0.0000e+00
Epoch 5/10
1/1 [==============================] - 0s 27ms/step - loss: 0.8493 - accuracy: 0.0000e+00
Epoch 6/10
1/1 [==============================] - 0s 38ms/step - loss: 0.8339 - accuracy: 0.0000e+00
Epoch 7/10
1/1 [==============================] - 0s 30ms/step - loss: 0.8096 - accuracy: 0.0000e+00
Epoch 8/10
1/1 [==============================] - 0s 27ms/step - loss: 0.7786 - accuracy: 0.0000e+00
Epoch 9/10
1/1 [==============================] - 0s 34ms/step - loss: 0.7442 - accuracy: 0.0000e+00
Epoch 10/10
1/1 [==============================] - 0s 35ms/step - loss: 0.7093 - accuracy: 0.0000e+00

I have successfully trained the ensemble of models on the scaled training data. However, the training accuracy is very low (0.0000e+00), which could indicate an issue with the model or the dataset. To mitigate the issue, I'll need to implement Early Stopping to prevent overfitting, Early stopping monitor a validation metric (e.g., validation loss or accuracy) during training and stop training if the metric doesn't improve for a certain number of epochs. But, before that is implemented, I'll need to do some checks to see how well the model is, I will check for the Data Shape and Types, Shuffling Data and finally carry out Random Sample to inspect it visually and ensure that it looks as expected.

#Data Shape and Types:Check the shapes and data types of your input features (X_train_scaled) and labels (y_train). Ensure they match your expectations.
print("X_train_scaled shape:", X_train_scaled.shape)
print("y_train shape:", y_train.shape)
print("Data types - X_train_scaled:", X_train_scaled.dtype, "y_train:", y_train.dtype)

#Output
X_train_scaled shape: (29, 29)
y_train shape: (29,)
Data types - X_train_scaled: float64 y_train: float64

#Shuffling Data:Verify that your data is properly shuffled. You can check this by examining the first few rows of your training data to see if they appear in a random order.
print("First 10 labels in y_train:", y_train[:10])

# Output
First 10 labels in y_train: 24    0.02
16    0.05
8     0.12
15    0.49
12    0.09
32    0.06
9     0.08
19    0.37
0     0.04
31    0.26
Name: Multidimensional Poverty Index, dtype: float64

#Random Sample:Take a random sample of your data to inspect it visually and ensure that it looks as expected.
random_sample_idx = np.random.randint(0, X_train_scaled.shape[0], size=5)
print("Random sample from X_train_scaled:", X_train_scaled[random_sample_idx])
print("Corresponding labels from y_train:", y_train[random_sample_idx])

# Output
Random sample from X_train_scaled: [[0.         0.58333333 0.         1.         1.         0.
  1.         0.         0.         0.         0.         0.
  0.35622583 0.66777519 0.97879899 0.33426738 0.20822011 1.
  1.         0.93903243 0.34751037 0.44649022 1.         1.
  0.81675733 0.27351325 0.23961753 1.         1.        ]
 [0.         1.         0.         1.         0.76026385 0.
  0.71200836 0.         0.         0.         0.13272364 0.13319718
  0.37564938 0.58164283 1.         0.47887324 0.74235734 0.73489543
  0.54481547 0.78783285 0.53056176 0.72628637 0.70538342 0.39344262
  0.62149874 0.54258242 0.72541744 0.70660793 0.35122921]
 [0.         0.88888889 0.         0.17630628 0.         0.30709683
  0.         0.         0.         0.         0.23793421 0.23699529
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.        ]
 [0.         0.08333333 0.         0.07328294 0.         0.
  0.         0.         0.         0.         0.17537658 0.17520745
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.        ]
 [0.         0.77777778 0.         0.29444627 0.         0.65612108
  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.         0.42815897 0.
  0.         0.         0.         0.44040769 0.         0.
  0.         0.         0.         0.         0.        ]]
Corresponding labels from y_train: 14    0.12
16    0.05
5     0.12
18    0.31
28    0.09
Name: Multidimensional Poverty Index, dtype: float64

Based on the various data check outputs:

Data Shape and Types: The shapes of X_train_scaled and y_train match expectations and are fine. Both X_train_scaled and y_train have data types of float64, which is fine.
Shuffling Data: The first ten labels in y_train do not appear to be in any particular sequence. This implies that the data may have been shuffled or randomly organized, which is beneficial for training models.
Random Sample: The random sample's feature values are appropriate, and the labels are in the anticipated format (floats). Now, Based on these checks, the data appears to be correctly prepared, shuffled, and in the expected format, which is suitable for continues machine learning training. I am now free to implement Early Stopping.

#Early Stopping: Implement early stopping to prevent overfitting. Monitor a validation metric (e.g., validation loss or accuracy) during training and stop training if the metric doesn't improve for a certain number of epochs.
from keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
model.fit(X_train_scaled, y_train, epochs=100, batch_size=32, validation_data=(X_val_scaled, y_val), callbacks=[early_stopping])

# Output
Epoch 1/100
1/1 [==============================] - 1s 931ms/step - loss: 0.6762 - accuracy: 0.0000e+00 - val_loss: 0.6599 - val_accuracy: 0.0000e+00
Epoch 2/100
1/1 [==============================] - 0s 76ms/step - loss: 0.6463 - accuracy: 0.0000e+00 - val_loss: 0.6430 - val_accuracy: 0.0000e+00
Epoch 3/100
1/1 [==============================] - 0s 79ms/step - loss: 0.6202 - accuracy: 0.0000e+00 - val_loss: 0.6296 - val_accuracy: 0.0000e+00
Epoch 4/100
1/1 [==============================] - 0s 72ms/step - loss: 0.5979 - accuracy: 0.0000e+00 - val_loss: 0.6193 - val_accuracy: 0.0000e+00
Epoch 5/100
1/1 [==============================] - 0s 93ms/step - loss: 0.5793 - accuracy: 0.0000e+00 - val_loss: 0.6118 - val_accuracy: 0.0000e+00
Epoch 6/100
1/1 [==============================] - 0s 82ms/step - loss: 0.5638 - accuracy: 0.0000e+00 - val_loss: 0.6066 - val_accuracy: 0.0000e+00
Epoch 7/100
1/1 [==============================] - 0s 83ms/step - loss: 0.5511 - accuracy: 0.0000e+00 - val_loss: 0.6033 - val_accuracy: 0.0000e+00
Epoch 8/100
1/1 [==============================] - 0s 85ms/step - loss: 0.5407 - accuracy: 0.0000e+00 - val_loss: 0.6016 - val_accuracy: 0.0000e+00
Epoch 9/100
1/1 [==============================] - 0s 84ms/step - loss: 0.5322 - accuracy: 0.0000e+00 - val_loss: 0.6013 - val_accuracy: 0.0000e+00
Epoch 10/100
1/1 [==============================] - 0s 99ms/step - loss: 0.5255 - accuracy: 0.0000e+00 - val_loss: 0.6021 - val_accuracy: 0.0000e+00
Epoch 11/100
1/1 [==============================] - 0s 83ms/step - loss: 0.5202 - accuracy: 0.0000e+00 - val_loss: 0.6039 - val_accuracy: 0.0000e+00
Epoch 12/100
1/1 [==============================] - 0s 102ms/step - loss: 0.5161 - accuracy: 0.0000e+00 - val_loss: 0.6065 - val_accuracy: 0.0000e+00
Epoch 13/100
1/1 [==============================] - 0s 105ms/step - loss: 0.5131 - accuracy: 0.0000e+00 - val_loss: 0.6098 - val_accuracy: 0.0000e+00
Epoch 14/100
1/1 [==============================] - 0s 81ms/step - loss: 0.5110 - accuracy: 0.0000e+00 - val_loss: 0.6135 - val_accuracy: 0.0000e+00
<keras.src.callbacks.History at 0x22d6fb9e950>

Now I've successfully implemented early stopping in the model training process. This will help prevent overfitting and ensure that the model generalizes well to new data. Now let's finalize.

Model Evaluation

**
Model Performance Metrics
Using a constrained dataset encompassing multiple socioeconomic indices, I constructed a machine learning model to forecast and prescribe poverty reduction in Nigeria. To avoid overfitting, the model was trained using early stopping. I used the following metrics to assess its performance:

Loss: The loss function (mean squared error) measures the discrepancy between the predicted and actual values. It quantifies how well the model fits the data.

Accuracy: While accuracy is not a typical metric for regression tasks, we calculated it to provide a general idea of the model's performance.

Evaluation Results

**
Both training and validation datasets were used to train and evaluate the model. The following are the primary evaluation findings:

Training Loss: The training loss decreased consistently over the epochs, reaching a value of 0.5110.

Validation Loss: The validation loss also decreased, with a final value of 0.6135.

Training Accuracy: The training accuracy was reported as 0.0 due to the regression nature of the task.

Validation Accuracy: The validation accuracy was also reported as 0.0, as accuracy is not a suitable metric for regression tasks.

Conclusions

**
Based on the evaluation results, we can draw the following conclusions:

Model Training: The decreased training loss indicates that the machine learning model effectively learned from the training data. This implies that the model is capable of detecting patterns in the data.

Validation Performance: While the validation loss of the model decreased during training, the final validation loss is still very significant. This suggests that the model may not generalize effectively to new, previously unknown data. The poor accuracy scores emphasize the difficulties in utilizing typical classification metrics for regression problems.

Objective Achievement: Due to the relatively substantial validation loss, the primary goal of forecasting and prescribing poverty reduction in Nigeria may not have been entirely realized. The performance of the model implies that more advanced approaches or extra data may be necessary to enhance predictions..

Recommendations

**
Based on the findings and conclusions, here are some recommendations for further work and improvements:

Hyperparameter Tuning: Experiment with various hyperparameters such as model architecture, learning rate, and epoch count to uncover configurations that result in superior model performance.

Feature Engineering: Investigate additional features or technical strategies for extracting more useful information from data. Methods for feature selection and dimensionality reduction may also be useful.

Collect More Data: A bigger sample size and more relevant features in the dataset could increase model generalization. Furthermore, gathering data unique to poverty reduction activities in Nigeria may improve projections.

Time-Series Analysis: Investigate time-series analysis tools for incorporating temporal trends into poverty alleviation activities.

Ensemble Models: Experiment with ensemble models, such as random forests or gradient boosting, to see if they can better capture complicated relationships in data.

External Data Sources: To increase the dataset's richness and diversity, incorporate data from external sources such as government publications, surveys, or satellite photography.

Interdisciplinary Collaboration: Collaborate with experts in economics, social sciences, and poverty reduction to obtain a better understanding of the variables that contribute to poverty and viable policy solutions.

Ethical Considerations: When using machine learning to societal concerns such as poverty alleviation, keep ethical implications in mind. Make certain that the models do not inject prejudice or unfairness into decision-making.

Final Remarks

**
The study made substantial progress in its investigation of the use of machine learning approaches to forecast and prescribe poverty reduction in Nigeria. However, there are some critical considerations to overcome in order to adequately answer the study question:

Limited Datasets: When training machine learning models, using restricted datasets can be difficult. While the experiment used available data, the model's performance and generalization capabilities may have suffered due to the small dataset size.

Model Performance: The evaluation findings show that the model had difficulty obtaining high accuracy while minimizing validation loss. This shows that the model's prediction performance may need to be improved further.

Regression Task: Predicting and prescribing poverty reduction is a regression task in the study question. Traditional classification criteria, such as accuracy, may not be ideal for evaluating regression models. The emphasis should be on minimizing the loss function and enhancing the model's ability to predict accurately.

Generalization: Machine learning models strive to generalize well to previously unseen data. The project's findings indicate that the model may not generalize well to new, previously unreported data. This is an important consideration when using machine learning approaches in real-world applications.

Data Limitations: Data restrictions, such as data quality, representativeness, and the availability of important features, also have an impact on the project's success in answering the research question.

In conclusion, while the project produced useful contributions and provided insights into the application of machine learning approaches for poverty prediction in Nigeria, additional work may be required to adequately answer the research topic, particularly given the limits of restricted datasets. Extending the dataset, refining the model, and investigating new variables and approaches may improve the project's ability to predict and prescribe poverty-reduction strategies.