How AI and predictive modelling can transform public services in Nigeria

BY NWACHUKWU CHIGOZIRIM 

Nigeria faces enormous public service challenges from traffic congestion in high urbanised areas to insecurity, healthcare delays, and inconsistent public planning. But with the right use of artificial intelligence (AI) and predictive modelling, the country can shift from reactive decision-making to proactive, data-driven governance.

My interest in this subject began while analysing a 2,000,000-row demographic dataset as part of a research project. The dataset covered Nigerian-style household structures: age, marital status, occupation, religion, commuting patterns, and more. Cleaning and modelling this data revealed just how powerful AI can be in anticipating real-world problems before they escalate.

Below, I explore how AI could transform public services in Nigeria — supported by real insights from my work in data cleaning, machine learning, and pattern prediction.

Traffic and Urban Mobility: Patterns Hidden in the Data

During my dataset analysis, I observed how age, occupation, and household location strongly influenced commuting patterns. Using crosstab analysis and machine learning clustering, I found:
• Young adults (18–35) showed the highest commuting rates
• Households with school-aged children had predictable morning mobility spikes
• Certain occupations (e.g., traders, artisans) showed irregular movement patterns.

These patterns matter. If Nigeria integrated similar real-world data into an AI system, predictive models could:

• Forecast traffic surges
• Recommend better bus scheduling
• Identify neighbourhoods likely to experience congestion
• Suggest new road constructions based on mobility probability

AI-based traffic optimisation already seen in Nairobi, Dubai, and Singapore could save Nigeria billions of naira in lost productivity.

Healthcare: Using Predictive Models to Anticipate Disease Patterns

My work involved cleaning a dataset with incomplete medical and demographic attributes. Techniques like:
• Missing value imputation
• Outlier detection
• Random forest classification for prediction… revealed how data can predict trends even when information is imperfect — a common Nigerian reality.

Applied nationally, AI could:
• Predict malaria outbreaks using rainfall and mobility data
• Forecast hospital overcrowding
• Identify communities with high childbirth rates (my dataset included birth-age clusters)
• Detect regions at risk of waterborne diseases

AI isn’t a replacement for doctors, but it can allow hospitals and governments to prepare before cases explode.

Public Safety Security: AI as a Force Multiplier

With my background in analysing demographic data and my interest in security operations, I found that combining:
• Age distribution
• Household structure
• Movement variables…can highlight areas where security risks may be higher.

… and predictive modelling could help Nigeria:
• Identify crime hotspots in advance

• Use CCTV footage and pattern detection to flag unusual activity
• Optimise placement of patrol teams
• Predict high-risk areas during holidays or major events

Given the manpower constraints faced by security agencies, AI acts as a force multiplier, improving response times and reducing preventable incidents.

Education: Using Data to Understand Student Dropout Patterns

By examining household ages, relationships, and occupation data, I noticed strong correlations between:
• Parental unemployment
• Single-parent households
• Low household age averages

…and higher dropout probabilities. With AI, Nigeria could:

• Predict dropout risks early
• Allocate learning support resources
• Improve school attendance monitoring
• Provide targeted intervention for high-risk communities

This could significantly reduce Nigeria’s 20+ million out-of-school children challenge.

Strengthening Government Planning Through Clean Data and Machine Learning

A key part of my analysis involved cleaning thousands of inconsistent rows using:
• .isna(), value counts, imputation
• Corrections for birth/death inconsistencies
• Prediction of missing religion categories using RandomForest
• Standardisation of occupations

This process taught me something crucial: Nigeria’s biggest challenge is not the lack of data… it is the lack of clean, usable data. With proper data pipelines, AI can:
• Improve budget forecasting
• Detect payroll fraud
• Reduce corruption in procurement
• Support evidence-based policy making

Predictive analytics can show the government what will happen, not just what has already happened.

Agriculture: From Yield Prediction to Food Security

Using the modelling skills from my dataset (classification, regression, and probability analysis), it’s clear that AI can radically improve Nigerian agriculture.

AI can help predict:
• Rainfall timing
• Pest infestation risk
• Yield projections
• Optimal planting cycles

Smallholder farmers could receive advice on low-data mobile apps — improving national food stability.

The Real Barrier: Nigeria Needs Better Data Infrastructure

My own challenges cleaning a 2,000,000-row dataset reflect the national challenge:
• Missing values
• Inconsistent formats
• Unstructured entries
• Lack of digitisation

For AI to work in Nigeria, we need:

• Nationwide data collection standards
• Digital public records
• AI regulatory frameworks
• Data protection enforcement
• Training for government workers

Without this foundation, even the best algorithms will fail.

Conclusion: Nigeria Can Leapfrog with Data and AI

Nigeria has the population, talent, and urgency for AI-driven public service reform. What we need now is:
• Intentional investment
• National data transparency
• Partnerships with tech innovators
• Commitment to ethical AI

My own experience analysing large datasets and using machine learning models shows that prediction is possible even when the data is imperfect. If Nigeria adopts AI responsibly, the country can move from reactive crisis management to proactive national planning.

AI is not magic — but in the right hands, it is transformational