Corporate Experience
We started Izziesnow in 2021 with a simple goal: help businesses make sense of their data and leverage on the data to enhance business success. Since then, we have worked hard to help our clients, both big companies and small startups, use their data to make better decisions. We’ve worked on lots of projects in different areas like finance, health, and online shopping. For example, we helped a big shop chain cut their costs by 20% and an online store keep 15% more of their customers, all by using data smartly.
We are proud to have won some awards, like the “Data Innovator of the Year, in 2022” and even got featured in “Data Science Weekly, (February, 2023), but what we care about most is our team. We make sure everyone keeps learning and getting better at what they do, so our clients always get the best possible solutions. The leadership and management of Izziesnow have lots of experience and studied at top schools, and they make sure the work that is done at PSP Analytics meets industry standards and best practices.
We also believe in using data to do good things for society and the planet. So, we take part in projects that use analytics to help people and the environment. Looking ahead, we want to keep being leaders in our field. But more than that, we want to make sure our work is always top-notch, honest, and helpful for our clients and the community.
"Data Innovator of the Year", 2022
Awards and Recognitions
Nigerian Information Technology Development Agency (NITDA)
Technical training Partner, 3 Million Technical Talent Program (3MTT)
Partnerships and Associations
Microsoft Corporation
Microsoft Startup business hub Incubation progam
PECB, Montreal, Canada
Liscencing Partner for the region of Africa and the Middle East, ISO Traing, Evaluation and certification
Recent Projects
Projects
Image Forgery Detection: In the digital age, the authenticity of images plays a crucial role across various industries, from journalism to identity verification. However, the rise of advanced image manipulation tools has made it challenging to verify the genuineness of these images. This project focuses on detecting tampered images using machine learning and image processing techniques. The project consists of the following four sub-projects:
Document Forgery Detection With SIFT : Passive image forensics using the Scale-Invariant Feature Transform (SIFT) algorithm and homography estimation. Learn more
Image Forensics with TensorFlow : In this project, we used googles deep learning algorithm (TensorFlow) to test if images have been digitally manipulated or not. Learn more
Image Forensics with Deep Learning (Pytorch): Utilizes Convolutional Neural Networks (CNN) for image forgery detection. Learn more
Decoding Encoded Census Data Using Pyspark: Analysis of data from the United States Census Bureau with the aim of decoding the data that was encoded for security and privacy compliance. Learn more
Completed Projects
Here are a few of the projects that we have completed.
Energy Generation, Supply and Consumption Visualization: We used PowerBI to connect to data sources related to Eskom’s power generation and created visuals to derive the insight required to appropriately answer several critical questions about the SA Energy crisis. Learn More Note that you must have a valid microsoft account to view this project
Scrapping S&P Historic Stock Price Data from the Internet: We used the yfinance library and python to scrape stock data from the S&P 500 (Standard & Poor's 500). Learn More.
Mortgage Amortization Project: We compared three different mortgage loans, each with a different interest rate, term, and amortization schedule. Learn more
Load shortfall Prediction: Predictive modelling of load shortfall between energy generated by means of fossil fuels and various renewable sources was built. Learn more
Stock Inventory prediction: Prediction of the right quantity of the products to be stocked for sale by a store that serves several countries from a common stock inventory to prevent overstocking or understocking. Learn more.
Asset trading Model: This trading model uses the 200day moving average to determine when to buy or sell an asset. this example trades crypto but the model can be adapted for stocks and other assets. Learn more
Basic Crypto Trading Model: Enforcing transaction protocols to buy or sell crypto assets when certain market conditions are met. Learn more
Identifying and Applying Risk Metrics Associated With Financial markets: Modelling returns of different asset classes. We observe custom volatility metrics, along with how to model them in Python. Learn more
Using Statistical Distributions to Model Asset Returns: Evaluation of distribution of asset returns for normality or otherwise in order to correctly model risk and returns on assets. Learn More
Other Projects
Here is a list of even more projects that we have completed for our clients some of whose identity we are not allowed to disclose due to NDAs to which we are signed.
Anomaly detection for a financial institution: This project involves the detection of anomalous transactions. It is useful in the early detection of frauds and abuse of bank processes, allowing the institution to take action to prevent loss of funds or other security breaches.
Cloud Migration: We migrated the entire operation of our client from a completely onsite operation to a cloud based operation with full security implementation of the least privilege protocol.
Churn Prediction: We generated a model that predicts churn by analyzing customer data to enable the classification of customers into groups that are likely to churn based on known features. The company can take pre-emptive steps based on these Predictions to prevent churn by targeting remedial efforts to these customers.
Customer classification: Classification of customers into categories based of behaviors, demographics etc. to allow for targeted marketing. This particular project resulted in a 32% improvement in the rate of conversion for our client.
Anomaly detection for electricity consumers: In this project, we detected anomalous patterns in the consumption of electricity. This allowed us to be able to detect electricity theft and by pass of meters in real time. Our client previously relied on manual detection by field staff which was both labor and capital intensive. Our solution resulted in an 87% detection of electricity theft wit only a 2% case of false positives, greatly improving our clients bottom-line.
Project Portfolio Management Project for a construction company: The Project Portfolio Management" database project is aimed to streamline and enhance the operations of PSP Alpha, a construction and property development company. The comprehensive system we developed was an efficient project tracking, resource management, quality assurance, financial oversight, and client/stakeholder engagement system. It centralized document management, timeline tracking, risk assessment, and reporting.
Youth engage Plus: The YouthEngage Plus project is a multifaceted database management system tailored to the needs of an organization dedicated to youth development and community engagement in Malawi. This all-in-one solution seamlessly integrates features for member and volunteer management, donor and fundraising tracking, event and activity registration, program evaluation and impact assessment, resource and inventory management, communication and outreach, and document / content management. By providing a centralized platform, YouthEngage Plus empowers our client to efficiently manage its operations, measure program effectiveness, strengthen stakeholder relationships, and streamline organizational processes, all while ensuring data security and privacy.
Digitization of Legacy project and thesis documents and creation of project repository and Database for a Tertiary Institution: We took thousands of old students' projects that were only available in hard copies, created digital copies of these projects and integrated them into their existing students repository. Because of our work, these materials which were hitherto unavailable to students as there were limited copies of them are now digitally available to potentially thousands of students at once. We also implemented access restrictions to the repository in line with the instruction of the client and provided an option for the monetization of the digital asset that we created.