Omdena Chapter Page: Nigeria

Omdena Nigeria Chapter - Omdena Chapters

Welcome to the Nigeria Chapters!


There are 5 active chapters in Nigeria:

1. Uyo, Nigeria

2. Osun, Nigeria

3. Abuja, Nigeria

4. Port Harcourt, Nigeria

5. Kaduna, Nigeria



Apply here to be a chapter lead for other cities and/or universities in Nigeria

Read About Our Upcoming Project(s) Below!

Kaduna, Nigeria Chapter

Project Starts: June 20th

 Duration: 4 Weeks

Kaduna, Nigeria Chapter – Analysis of Climate Change in Nigeria

By the Lead of Kaduna, Nigeria Chapter – Abdulazeez Jimoh



The Background / The Problem 

Climate change refers to long-term shifts in temperatures and weather patterns. These shifts may be natural, but since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels (like coal, oil, and gas), which produces heat-trapping gases. 🙷

~ the United Nation


The effect of climate change is evident across the world and in many sectors important to society, such as human health, agriculture, food security, water supply, transportation, energy, and biodiversity and ecosystems; these effects are expected to become increasingly disruptive in the coming decades.



The Project Goals

  • The goal of this project is to data provided by the World Bank Group that covers climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, energy use, and other indicators relevant to climate change to;
    • Identify indicators that have a high impact on climate change.
    • Create a dashboard of the climate change analysis.
    • Use machine learning to forecast the effect of climate change.


The Learning Outcomes

  1. By the end of this project, collaborators will learn how to;
    • Create a dashboard of the climate change analysis.
    • Build and deploy machine learning model


The Tasks & Timeline 

Week 1 Week 2 Week 3 Week 4
Port Harcourt, Nigeria Chapter

Project Starts: June 26th

 Duration: 4 Weeks

Port Harcourt, Nigeria Chapter – Detecting Air Pollution in Nigeria using Machine Learning and Satellite Imagery

By the Lead of Port Harcourt, Nigeria Chapter – Gigi Keneth

The Background 

Nigeria has the largest number of deaths in Africa due to air pollution. Contributors to this challenge being vehicle emissions, generator fumes, bush burning, crude oil exploration, etc. 

To have a better understanding of the problem and how it can be fixed, it is important to understand which regions are affected by this the most.




The Problem 

Air contamination kills several people annually and Nigeria has high rates of unhealthy air quality across the African continent.

Learning about air pollution and identifying regions affected will help Nigerians better understand how to manage their health and remedy the situation.


The Project Goals

  • Get a list of regions in Nigeria suffering from air pollution
  • Identifying regions in the country that require immediate attention by the government
  • Identifying possible causes for the air high levels of air pollution and providing recommendations to remedy the situation

The Learning Outcomes

  1. Processing satellite imagery
  2. Carrying out image segmentation
  3. Creating machine learning-driven heat maps to identify regions with high levels of air pollution

The Tasks & Timeline 

Week 1 Week 2 Week 3 Week 4
Lagos, Nigeria Chapter

Project Starts: August 13th, 2022

 Duration: 5 Weeks

Addressing the Issue of Pipe-borne Water Availability in Lagos, Nigeria

By the Lead of Lagos, Nigeria Chapter – Opeyemi , Olamide


The problem statement:

Inadequate pipe-borne water supply in Lagos, Nigeria, can be attributed to leakages in pipes, a poor road water supply network system, and a lack of maintenance culture.

Addressing the Issue of Pipe-borne Water Availability in Lagos, Nigeria

Addressing the Issue of Pipe-borne Water Availability in Lagos, Nigeria


The project goals:

1. Recognizing the difficulties in providing pipe-borne water.

2. Identifying areas with/without access to pipe-borne water.

3. Understanding the road network to access the pipe-borne water system.

4. Identifying potential solutions for pipe-borne transportation system


The impact:

  • To investigate issues with and potential remedies for pipe-borne water access.
  • To assist the government in redesigning the best road network for the pipe-borne water network system.
  • To assist the government in locating pipe-borne water leaks in the water supply network.


The learning outcomes are as follows:

1. Data collection

2. Data Preprocessing

3. Feature Engineering

4. Data visualization

5. Building Machine Learning Models


The tasks & timeline:

Week 1 – Data Collection
Week 2 Data Pre-Processing
Week 3 – Feature Engineering
Week 4 – Data Visualization
Week 5 – Building Machine Learning Model
Ondo, Nigeria Chapter

Project Starts: August 25th, 2022

 Duration: 8 Weeks

Ondo, Nigeria Chapter - Predicting Students' Performance Using Machine Learning Models

By the Lead of Ondo, Nigeria Chapter – Oluseye Jeremiah


The background:

Student performance has been a global concern since it is influenced by a variety of causes and environments that vary by place. Student performance in certain places might be influenced by regional difficulties for a variety of reasons. Machine learning can be used to determine whether a student’s performance is poor or high, and it can also provide solutions by comparing low-performing students to high-performing students and observing what each of them accomplishes differently. Different prediction models will be used to guarantee that each model’s accuracy is adequate.


The issues:

We can forecast university students’ grades and overall performance using machine learning: by collecting data from open information databases such as Kaggle, UCL, and university databases, we can gain an understanding of the factors that influence student success in each region. Some children perform poorly as a result of traumas experienced at home; others are responsible for funding their own education, so they don’t have much time to study, and still others are simply lazy.


The goals of the project are:

1. Using various visuals, analyze the various performances of students.
2. Develop a model that can predict student performance and the underlying cause of that performance.
3. Make an attempt to devise a study plan that all students can adhere to.
4. Create a detailed report with analytical visualization and briefs on these major items.


The learning outcomes are as follows:

1. Collection of data
2. Pre-processing of Data
3. Exploratory Data Analysis
4. Modelling
5. Model deployment into a possible API
6. Visualization and Publication


The tasks & timeline:

Week 1

– Data Collection (pre-week 1 even)

– Data Pre-Processing

Week 2 Data Pre-Processing
Week 3

– Exploratory Data Analysis

– Modelling

Week 4 – Modelling (cont)
Week 5 – Possible deployment into API
Week 6 Visualisation and publication
Week 7 – Visualization and publication (cont.)
Week 8 – Visualization and publication (cont.)
Week 9 Wrap up
Week 10 – Wrap up
UNILAG Lagos, Nigeria Chapter

Project Starts: August 29th, 2022

 Duration: 4 Weeks

UNILAG Lagos, Nigeria Chapter - Automatic River Encroachment Detection in Lagos with Machine Learning and Remote Sensing

By the Lead of UNILAG Lagos, Nigeria Chapter – Mustapha Moshood Olawale


The background:

This project is inspired by recent flooding impacts in the state, and an event that occurred in school which causes some parts of the school to be flooded because of its proximity to the Lagos Lagoon. Damages are made and it is disheartening that life was lost. This project will be an approach to solving the encroachment issues thereby reducing the flooding rate.


The problem:

River encroachment is a threatening problem that has been going on for a few years now. Its impact has badly affected the state leading to flooding and other environmental problems. More encroachment is still going on majorly on the island which puts them at greater risk of high flooding impact which causes loss of lives and properties in the state, it is therefore important for this problem to be addressed to reduce damages and impact.


The goals of the project are:

The goal of the project is to:

1. Collect data from open-source satellite images of specific rivers of Lagos, such as Yewa River, Lagos Lagoon, Lekki Lagoon, etc, and extract necessary information with remote sensing analysis.

2. Process the data following a systematic methodology, and do exploratory data analysis of illegally occupied river banks.

3. Develop an automatic encroachment detection for certain rivers with machine learning and deliver geolocations of them.

4. Outline an AI-driven solution to improve the cautioning system for Lagos.


The learning outcomes are as follows:

1. GIS processing

2. Remote Sense Analysis

3. Data Preprocessing

4. Data Visualizations

5. Machine Learning

6. Georeference API development.


The tasks & timeline:

Week 1

– Data Collection of certain rivers.

– Brainstorming

– GIS Processing

Week 2

– Remote sense analysis

– Exploratory Data Analysis(EDA)

– ML Preprocessing

Week 3

– Pre-processing Completion.

– Building Machine Learning models or computer vision models for illegal occupation of rivers.

– Evaluation of models in certain regions.

Week 4

– Creation of maps of particular encroachment of Lagos rivers

– Georeferencing the classified maps.

– Deployment of the model to classify encroachment with geo-referenced.


Read about Our Completed Project(s) Below!

Osun Chapter, Nigeria - Analyzing Public Dataset in Nigeria

 By the Lead of Osun, Nigeria Chapter – Kabirat Olayemi 

The Background 

Participation in data science in Nigeria has continually increased over the years. Students and graduates from various disciplines are fully captivated by data science potentials and are now gainfully engaged in the space. We have one major community in Nigeria who has taken over the task of tutoring undergraduate students in data science. This community naturally holds classes or bootcamps to introduce the basic data science tools such as python, R and databases. However, there is a big gap between learning and working on real-life datasets.

The Problem 


Participation in data science in Nigeria has continually increased over the years. Students and graduates from various disciplines are fully captivated by data science potentials and are now gainfully engaged in the space. We have one major community in Nigeria who has taken over the task of tutoring undergraduate students in data science. This community naturally holds classes or bootcamps to introduce the basic data science tools such as python, R and databases. However, there is a big gap between learning and working on real-life datasets. 

The Project Goals

  • In this four-weeks project, we seek to help upcoming data scientists learn and grow in the field by working on real life dataset. They will learn how to collect, organise, and analyse different datasets directly about or related to Egypt.
  • To serve as a data repository about Nigeria.
  • Provide visualization of multiple datasets in Nigeria.
  • To help tell a story about occurrences in Nigeria.
  • To serve as a tool for other researchers.
  • Make result openly available on a website

The Learning Outcomes

  1. – Learning how to search for, categorize and visualize datasets.
  2. – Learning how to use multiple datasets in one report to tell stories with data.
  3. – Learning how to create new datasets.
  4. – Learn how to present data for decision making.

The Tasks & Timeline 

Week 1

Week 2

Week 3

Week 4

– Determine key websites that list datasets about Nigeria

– Decide how to organize datasets, visualizations, and reports

– Decide on a new dataset to create

– Start creating an index of datasets and websites that host them

– Start creating visualizations and reports using the datasets in the index

-Start scraping data for the new dataset

– Continue improving the index and creating visualisations 

– Start looking into ways to host the results

– Organise new dataset and label it if necessary

– Finalize index and make sure it’s well-organized

– Host visualizations and reports on a website

– Finalize new dataset, host it, and test it

Osun Chapter, Nigeria - Improving Water Supply Sustainability in Nigeria Through Computer Vision

Tabbatar da Samar da Ruwa a Najeriya Ta hanyar Na’urar Kwamfuta
Idaniloju Ipese Omi ni Nigeria Nipasẹ Iwoye Kọmputa
Ịkwalite nkwado mmiri na Nigeria site na ọhụụ kọmputa


The Background 

Water is an essential need for all. Regardless of your area, income, status, educational level, ethnic or culture, access to safe water is required by all. According to national standard, the minimum acceptable water needed by each person is between the range of 12 and 16 liters per day. However, the average amount each person receives in Nigeria is 9 liters per day (Owoicho, UNICEF; 2020). This serves as an indicator that there is need to work more in order to ensure that all Nigerians have access to quality and adequate water supply. 

This project is aimed at identifying locations with functional water service (boreholes) and makes suggestions to the government of locations where there is need for water services.

The Problem 

There has been an alarming state of water deprivation in Nigeria. According to a report released by the Federal Ministry of Water Resources and UNICEF in 2019, approximately 60 million Nigerians were living without access to basic drinking water service. Reports have also shown that people in the rural areas are mostly affected with non-access to water supply as 39% households lack access to at least basic water supply. Usually, their main source of water supply is through streams or rivers as shown in figure 1. In contrast, those in the city survived mainly by buying from water hackers or well digging or from a community borehole within a few miles walk. 

The effect of this problem rests majorly on women and children as they are the ones saddled with the responsibility of the house keep. They spend hours and walk over long distances as shown in figure 2 to provide water for the family which has been associated with negative effects on well-being, school attendance and higher risk of gender- based violence to the girls.

The Project Goals

  • To help government and non-governmental organizations to be able to identify areas lacking clean water supply.
  • To provide easy access to water supply services.
  • To be able to detect non-functional water supply services.
  • To serve as a means to better the lives of Nigerian citizens.

The Learning Outcomes

  1. Captured the locations of water points and their distance to the people
  2. Obtained the average population in each locality to the number of water points available.
  3. Make recommendations to the government or NGOs for the construction of water points.

The Tasks & Timeline 

Week 1 Week 2 Week 3 Week 4
-Understanding the problem
-Data Gathering

-Data cleaning

-Data pre-processing and data labelling

– Model building 
– Deployment 


Abuja Chapter : Automatic Number Plate Detection for security of lives and properties using Computer Vision

Model building – The Background 

Presently in Nigeria, there’ no standard automated system that can detect and extract Nigerian number plates. We believe this would help in the security of lives and properties that would benefit government agencies, parastatals and privately owned establishments. This would help in keeping track and records of vehicles that come in and out of their premises and monitor traffic offences (offenders)

The Problem 

There are a lot of vehicles in Nigeria. It is estimated that the number of vehicles in Nigeria roads is about 11.8 million. It is common to have traffic related offences, crimes around car theft, robbery and use of cars for kidnapping. We are proposing the use of an Automatic number plate detection system using image processing and computer vision to help keep track, match and identify vehicles for the purpose of security. This system can be used or deployed in schools, estate, government agencies and private companies.

The Project Goals

1. To create model that will identify plate numbers and extract the plate numbers
2. To create a character recognition model that will identify characters and extract the characters

3. To create a database that will be used to store the number plate characters that will be used for querying

To create Automatic number plate application

The Learning Outcomes

  1. 1. Data collection
  2. 2. Data Processing
  3. 3. Labelling of Data
  4. 4. ML Model for extraction of plate number
  5. 5. ML Model for classification and extraction of characters for plate numbers
  6. 6. Database system for storing of extracted characters
  7. 7. Deployment of the whole system

The Tasks & Timeline 

Week 1 Week 2 Week 3 Week 4
-Understanding the problem
-Data Gathering

-Data cleaning

-Data pre-processing and data labelling

– Model building  -Application Building
-Deployment of Application




Lagos Chapter: Solving unemployment in Nigeria using A.I
The Background 

Among the thousands of Nigerians graduating every year, only few get their dream jobs or graduate training jobs, leaving others with no choice but to start building up a career through certification and professional courses. In the end, many Nigerians are underemployed or unemployed.

The Problem

The Unemployment rate in Nigeria increased to 33.30 percent in the fourth quarter of 2020 from 27.10 percent in the second quarter of 2020 while the underemployed rate is 28.7% giving a total percentage rate of 61.9% of  Nigerians that are unemployed or underemployed

The Project Goals

To create a machine learning model that will merge NYSC graduates with possible job opportunities based on their career path

To provide employable job skills recommendation to NYSC graduates based on their their career path 

The Learning Outcomes

1. Data collection

2. Web scraping

3. Machine learning model

4. Natural Language Processing

5. Recommender system

The Tasks & Timeline


Week 1

Week 2

Week 3

Week 4            

Week 5

Understanding the problem

Breaking down the task into units

Choosing task leaders for each units

Fixing workshop training days and time

Text Analysis using NLP

Web scraping 

Getting graduate data from Government and NGOs

Data Wrangling

web scraping

Getting the data of registered companies and international companies from Government

Data Wrangling

Exploratory Data Analysis (EDA)

Data visualization

Feature engineering


Machine learning model

Recommender System

Port Harcourt Chapter : Leveraging Data Science Techniques to Raise Awareness of Air Pollution in Nigeria

The Background 

“Pollution should never be the price of prosperity.” (Al Gore, 45th Vice President of the U.S.) Studies have shown that dirty air has caused more premature deaths, than other risk factors such as unsafe water, unsafe sanitation, and malnutrition in Africa in 2013. 

Periodically in Port Harcourt, a thick black haze rising to the skies is visible. Black soot settles on everything and is inhaled by everyone, as evidenced by black particles in nostrils, and throat soreness. This results in a double air pollution burden as the unresolved prevailing widespread pollution already exists and then the added emergence of particle pollution (said to be resulting from incomplete combustion of hydrocarbons). This poses a health risk for all individuals inhabiting the state, especially sensitive groups. 

For relevant authorities to pay attention, more awareness should be raised on the issue, and using insights generated from available data, solutions can be proffered. 

The Problem 

Raising awareness on this existing air pollution will call relevant authorities to act on the issue and will help individuals especially sensitive groups carry out safer practices.

Air quality data isn’t readily available or simply doesn’t exist in some cases. Port Harcourt-based climate-tech startup, Pyloop has offered access to data sourced from one of their sensors. Using this data, engaging visualizations can be created and insights generated from the data provided can be used to assess the quality of air in Port Harcourt.

The Project Goals

1. Applying exploratory data analysis to air quality sensor data to generate useful insights

2. Using data visualization to create engaging dashboards to raise awareness about air pollution

3. Perform time series analysis on this data to generate future predictions using machine learning techniques

The Learning Outcomes

1. Obtain sensor data provided by Pyloop

2. Carry out exploratory data analysis on the data provided 

3. Visualize this data using Google Data Studio 

4. Perform time series analysis on the data provided 

5. Project documentation

The Tasks & Timeline 

Week 1 Week 2 Week 3 Week 4

– Data





– Exploratory





plots with near

real-time data

– Data


– Time Series


-Time Series

Analysis contd.

-Deploy the

model in Cloud





Lagos, Nigeria Chapter : A.I for Energy: Transitioning Toward a Sustainable Energy System in Nigeria

The Background

Despite the fact that Nigeria is the largest Africa oil producer and one of the best producing oil countries in the world. Currently, only 45% of Nigeria’s population is connected to the energy grid whilst power supply difficulties are experienced around 85% of the time and almost nonexistent in certain regions.

The Problem

Governments can dramatically reduce their carbon footprint by purchasing or directly generating electricity from clean, renewable sources and installing them to regions without power supply or not connected to the national electricity grid .The most commonly used renewable energy source is solar which will be our focus for this project. The project results will be made open source and can help governments identify how and when to use renewable energy.

The Project Goals

1. Getting the lists of the top Nigerian regions with a high demand for electricity

2. Finding the best solar energy spot

3. Identifying sites that are most suitable for solar panel Installation

The Learning Outcomes 

1. Processing Satellite Imagery

2. Image segmentation

3. Machine-learning-driven heat maps


Source Code:

Original Project:

Osun Chapter: Improving Food Security and Crop Yield in Nigeria through Machine Learning

The Background

According to the Food and Agriculture Organisation of the United Nations, 2018, approximately 88 % of the farmers in Nigeria engage in agricultural production at a subsistence level, and they lack sustainable farming knowledge and practices. Also, Nigeria is endowed with different climatic conditions and soil quality which leads to lackluster crop production. This project is aimed at helping farmers to boost their farm produce and plan their farming system.


The Problem

Nigeria is among those hardest hit by climate change. According to research, the largest population of Nigeria depends largely on agriculture with climatic change, floods, and droughts being the most serious environmental threat causing hunger, disease, malnutrition, and poverty. At a time in Nigeria where the prices and availability of food commodities are scarce and highly costly, we need to need to produce more with the little available resources; AI could help to transform agriculture in Nigeria hence, the world at large. Hence, in this challenge, we will work on building agricultural models to help actors better think, predict, and advise farmers via a variety of AI applications that presents Nigeria with the potential to achieve food security in the country.  

With the help of technology, farmers should be able to predict when, where, and what is the climatic condition will be in Nigeria for the next farming season while promoting a data-driven agricultural system. Data such as soil PH, temperature, and moisture levels, combined with other data sources from World Bank’s data portal and Nigerian Metrological Agency could be processed to show exactly when and where farmers should add water or fertilizer, and to know the best crop type to be planted on the soil. The data will also help to decide where to invest, and help strengthen the understanding of crop losses while maximizing revenues and minimizing losses.

The Project Goals

1. To help farmers to know the best crop to be planted based on the soil type.

2. To help farmers to know where and when to add water or fertilizer based on soil type.

3. It also helps farmers to know the best crop mixing system to adopt.

4. To help minimize losses while maximizing crop yields and profit.

The Learning Outcomes 

By the end of this challenge, we should have achieved the following:

1. Extract data and perform some EDA on it.

2. Predict and detect drought.

3. Predict the occurrence of flood.

4. Predict crop yield.

5. Predict the best crop to plant

The Tasks & Timeline

Week 1 Week 2 Week 3 Week 4

– Data collection

Data Analysis (EDA)

– Data Analysis(EDA)

– Model building

– Application Development( Deployment)


– Model Deployment

– Report Planning

– Documenting Final Analysis Report 


Link to the original project: 

Improving Food Security and Crop Yield in Senegal Through Machine Learning

Uyo, Nigeria Chapter : Machine Learning for Credit Scoring: Banking the Unbankable in Nigeria

The Background

A report by Nigeria Inter-Bank Settlement System (NIBSS) shows that as of 2020, 56 million out 99.6 million adults in Nigeria  are either unbanked, underbanked or financially excluded. This shows that many working adults will not have access to loans, grants and government subsidies. This could be one of the reasons for the high level of poverty in the country.

The Problem

We will build a model that house the details of people that are unbanked in Nigeria.

The aim is to build tools that are accessible to every adult. They can subsequently register their details. Their details will be collected and stored on the system. Any financial institution, government and NGO can log onto the system to access the credit worthiness of an individual. This will help them in decision making about whether to avail the loan to the user or not. 

The project results will be made open source. The solution will help the country alleviate poverty by making credit facilities available to everyone, even the unbanked. It will also help the government in development planning and allocation of subsidies to the people. The project can be further developed to be integrated into the systems of financial institutions and governments.

The Project Goals

  1. Determine the credit worthiness of an unbanked customer with alternate and traditional credit scoring data and methods.
  2. Build the model in such a way that it is accessible by anyone from an app or a website.
  3. Serve as a database for the unbanked in Nigeria.

The Learning Outcomes 

  1. Web Scraping
  2. Machine Learning
  3. Data Analysis
  4. Data Preparation

The Tasks & Timeline


Link to the original project: 

Machine Learning for Ethical Credit Scoring

Nigeria Chapter Leads

Kabirat Olayemi

“Kabirat is a Post Graduate Researcher (PhD.) at Federal University Oye and an Academic with over 8 years of experience working alongside other professionals and organizations. Kabirat is an enthusiast of biometrics, Pattern recognition, Information retrieval, Internet security, Recommender system, and Machine learning. She enjoys using her skill and knowledge to guide other students and to contribute to the exciting technological advances that happen every day. She is inspired every day with the saying “Our little effort can make the world a better place.”

Gigi Kenneth

Gigi is a machine learning engineer with a background in biochemistry. She has gained experience from building projects, startups, working on research and contributing to open source. She is passionate about technology especially artificial intelligence, its applications in a variety of domains and how it will impact the future of work.

Christian Chukwuma Ozoemene

A Data Science educator and instructor leveraging ML & AI to drive business strategy and solutions for social good

Utpal Mishra

AI Engineer at Deciphex
Omdena Researcher and Chapter Lead
IBM Quantum Project Collaborator
NLP Intern at Orcawise
AI Researcher in Healthcare and Agriculture

Abdulazeez Jimoh

Abdulazeez Jimoh is an AI / Software Developer who believes in the power of Artificial Intelligence and Software Development and he is focused on AI solutions development. He began his journey in AI and Software Development through Havard CS50 online program and now dreams of teaching AI and solving real-world problems with AI.

Abdulazeez collaborated with all kinds of people in building AI solutions that can solve real-world problems. Data Scientist Network (DSN) and Omdena also awarded him AI Community Lead based on his AI knowledge and mentorship skill.

Abdulazeez currently lives in Zaria, Nigeria. He is still pursuing his dream of dreams of teaching AI and solving real-world problems with AI. To get in touch with Abdulazeez Jimoh, call/email/message him on;


Eliel is a data scientist eclectically curious about the data and driving fast innovations and passionate about connecting with people and solving everyday problems. With 4 years of experience in Python, Machine learning, and data science, he has mentored 200+ people, created 50+ insightful visualizations, and contributed to the growth of 5 global brands.

Oluseye Jeremiah

Oluseye Jeremiah is a Data Scientist with over a year of experience who also studies computer science. Jeremiah specializes in constructing machine learning models and is in charge of educating others on various cloud services, predictive modeling, and general data knowledge. Jeremiah is a significant motivator in the business, inspiring others to work hard and succeed with his good attitude and boundless energy. Jeremiah is always inspired by his family and friends. Jeremiah enjoys reading and watching movies in his spare time.


I am an individual with a unique blend of skills in Data Science, technical writing, Agile product development, Health project management, and business development. With a background in health and my passion for innovation in the healthcare industry, My trajectory and upskilling journey over the years have further equipped me with valuable skills needed for building and deploying more innovative solutions and products. Visit:

Mustapha Moshood Olawale

Olawale is a software and machine learning engineer with a background in geomatics engineering. He is currently undergoing a degree in Surveying and Geoinformatics at the University of Lagos, Nigeria.

He is passionate about the application of artificial intelligence and machine learning to geospatial development and analysis in building real-life projects that provides solutions to global warming, disaster management, predictions, optimizing agricultural processes, and many others.

He is also a core member of Global AI Hub where he educates people on artificial intelligence and its application in all fields of study. As part of his contribution to solving the global warming issues in the world, He participated in the 2021 DrawDown project where he worked on the backend application that was used by the researchers in loading the output of their various research on how global warming affects different materials in the world.

He currently works as a solutions developer at AirSmat, an AI agro-based tech company where he built automation applications and worked on advanced drone mapping software for aerial imagery and photogrammetry. He also worked on a notification service for the IoT device and many more.

Olamide Olabode Goriola

Olamide Olabode Goriola is a solution-oriented, junior machine learning engineer/data scientist/data analyst who is creative, dependable, energetic, motivated, innovative, hardworking, tech-savvy, and can handle multiple tasks daily. He has a Higher National Diploma in Computer Sciences and Bachelor’s degree in Management Sciences. He is a determined, committed, enthusiastic person who loves working with others to complete tasks and make a difference to drive real-world social change. He seeks more leadership opportunities where he can make substantial impacts not just on his immediate environment but in his country, continent, and the world at large.