Explainable AI for Chronic Kidney Disease Detection

Explainable AI system for Chronic Kidney Disease prediction using XGBoost, SHAP, MLflow, and Databricks to analyze 52 healthcare indicators and deliver interpretable risk predictions.

Insurance Risk Intelligence System: Machine Learning for Exposure Prediction & Underwriting Analytics

Developed a machine learning-driven insurance risk intelligence system using Python and Databricks to predict weighted exposure, identify portfolio risk drivers, and support smarter underwriting and pricing decisions through predictive analytics.

Fiverr Data Scientists Market Analysis (SQL)

A SQL-based analysis of Fiverr data scientist profiles to uncover how skills, experience level, ratings, and platform reputation affect pricing and earning potential.

Online learning Completion Vs Dropout Analysis

This project uses SQL (MySQL) to analyze course completion and dropout patterns across online learning platforms. Results show a low 31.2% completion rate, driven mainly by time constraints and course difficulty, with higher completion among working professionals and Non-Tech courses.

Freelancer Earnings vs Skill Stack Analysis (SQL & POWER BI)

This project analyzes freelancer earnings across the tech industry to understand how income is influenced by skill specialization, experience level, education, geographic location, and platform choice

Advanced Data Cleaning Using SQL

Cleaned and transformed Ebuka Logistics’ customer order data using SQL. Fixed duplicates, inconsistent names, messy dates, invalid prices/quantities, and missing values. Standardized formats, created derived metrics, and delivered a reliable dataset ready for reporting, dashboards, and analysis.

LinkedIn Data Job Trends (Apr–May 2025) – Power BI Analysis

Power BI | DAX | Data Visualization | Job Market Analysis In this project, I analyzed LinkedIn job postings for data roles from April to May 2025. Using Power BI, I visualized key insights on the most in-demand roles, top hiring companies (like Meta and Netflix), and posting trends by week and day.

Lung Cancer Risk Pattern Analysis (2014–2024)

This project focuses on analyzing clinical, demographic, & lifestyle data to uncover patterns and risk indicators associated with lung cancer. Using Microsoft Excel, I performed a detailed exploratory data analysis (EDA) to help inform early detection strategies and public health awareness campaigns

Predicting Hypertension Risk With Machine Learning

This project focuses on building a predictive model to determine whether an individual is at risk of **hypertension** based on personal and lifestyle attributes. Early detection of hypertension is critical in preventing serious health complications such as heart disease, kidney failure, and stroke.

Vehicle Theft Analysis in New Zealand (2021-2022)

This project analyzes vehicle theft patterns in New Zealand for the years 2021 and 2022. By leveraging a relational dataset that includes vehicle theft incidents, vehicle make details, and geographical location data, the goal is to uncover key insights and trends that can help in preventing vehicle

Naija Tastes Sales Analysis Report

This project explores sales performance, customer behavior, and business trends using SQL-based analysis. Key insights reveal top-selling products, revenue patterns, and customer preferences, providing actionable recommendations to optimize sales and enhance customer experience

Diabetes Prediction Using Machine Learning

This healthcare project uses Logistic Regression, Random Forest, and XGBoost to predict outcomes based on health indicators like smoking history, BMI, age, hypertension, heart disease, and glucose levels. It aims to enhance accuracy through feature engineering and hyperparameter tuning

Credit Card Users Segmentation

This project analyzes credit card usage to segment users based on spending and payment behaviors. It helps financial institutions offer personalized services, improve marketing and manage risks effectively. The insights enable businesses to optimize financial products and enhance customer engagement

Cookie Company Sales Profit Analysis

This project analyzes profit distribution across countries and products (2019-2020) using an interactive dashboard with Pivot Tables and Charts. Data was cleaned for SQL storage, ensuring accuracy. Key insights include profit trends, seasonal sales patterns, and business recommendations.

JAMB 2024 Performance Prediction

Predicting JAMB 2024 exam performance using machine learning. Includes data analysis, visualization, and models to explore factors affecting student outcomes.

Unveiling The Upwork Data Science Job Market: Insight on Demand and Supply

This project analyzes 9500 Upwork job postings to uncover trends in data science roles, earnings, payment preferences, and geographical demand, offering insights and recommendations for freelancers and clients to optimize their engagement and success on the platform.

Brain Tumor Analysis: Can Data Illuminate Path To Better Treatment?

This project analyzes brain tumor data to identify patterns in demographics and treatment outcomes. Key insights include gender-specific tumor prevalence and the effectiveness of various therapies. These findings support the development of more personalized treatment strategies.

UK Energy Consumption Forecast

This project forecasts UK energy consumption using historical data from smart meters, weather, and demographics. Utilizing machine learning models, it aims to provide accurate predictions for resource planning and efficient energy use. The dataset, sourced from Our World in Data, includes key metric

Coronary Heart Disease Early Detection

This project aims to develop a machine learning model for early detection of coronary heart disease (CHD). Leveraging patient demographic and clinical data, the model predicts CHD presence with high accuracy, offering a cost-effective and accessible alternative to traditional diagnostic methods.

Bank Users Churn Project

The Bank Customers Churn Project analyzes a dataset to understand why customers leave banks. By examining demographics, financial status, and activity, the project identifies factors driving churn. Its goal is to offer actionable strategies for improving customer retention based on these insights.

Sales Analysis And Forecast

In this project, I developed a sales forecasting model using time series analysis to predict future sales from historical data. Through data cleaning, exploratory data analysis, feature engineering, and model building, I made accurate predictions for the next two years.

Thyroid Cancer Prediction Model

Showcased my machine learning expertise. Using a 13-feature dataset, I built a Random Forest Classifier with an impressive F1 score of 0.8985 for predicting thyroid cancer recurrence, this project exemplifies my ability to deliver effective predictive model in healthcare

New York Housing Analysis

Uncovered the nuances of the New York Housing Market through a regression model. Based on a Kaggle dataset, illuminates key factors influencing property prices. With an impressive R-squared of 0.986, the model reveals insights into bedrooms, bathrooms and square footage

British Airways Feedback Exploration

Executed a comprehensive analysis of British Airways customer feedback, utilizing advanced skills in machine learning and text mining for sentiment analysis. This project showcased my adeptness in leveraging cutting-edge techniques, providing a nuanced understanding of customer experiences.

Cannabis Sales Insight

Embark on a data-driven journey into the dynamic world of cannabis sales. Leveraging machine learning on data.gov's weekly retail sales data. With a perfect-fit model and actionable recommendations for businesses and medical stakeholders, explore the intricacies of cannabis sales in this industry.

Chicago Bikeshare Analysis

This project, centered around the bustling city of Chicago, delves into data-driven insights to uncover user behavior, popular routes, and trends in bikesharing usage. Explore the dynamics of Chicago's bikeshare system while prioritising user privacy

CreditOptimaPro: Advanced Loan Eligibility Prediction with Machine Learning

This project, named CreditOptimaPro, uses a RandomForestClassifier to predict loan eligibility. The model is trained, tuned, and evaluated, achieving a high F1 Score. Predictions on a test dataset were made, and the CreditOptimaPro algorithm was saved for future use.

Breast Cancer Detection Algorithm

Explore groundbreaking breast cancer detection using machine learning. Our model, trained on the "Breast Cancer Study: Clinical Insights" dataset, achieves 73.5% accuracy. Uncover intricate clinical patterns for early diagnosis, propelling advancements in healthcare.

Corn Yield Estimation Algorithm

This project builds a precise corn yield model using a dataset of 422 entries. With a 90.2% accuracy using Random Forest Regression, the model transforms agriculture by optimizing resources and informing financial decisions. It revolutionizes precision farming, enhances supply chains, and contribute

Diabetes Risk Prediction

Discover my Diabetes Risk Prediction project where I used advanced algorithms like RandomForest and GradientBoosting, the model achieves 95.96% accuracy in early diabetes identification. Unveil the future of medical diagnostics with cutting-edge data science.

Unraveling Laptop Market

Solo dive into the laptop market uncovered insights: preferences for 14.5-inch screens, lightweight 3.5 kg laptops, and dual-peaked pricing. XGBoost delivered optimal predictions. Explore on GitHub: https://github.com/lilemmy29/Unraveling-Lap.

Property Loan App

Swift property loan predictions with the Property Loan Approval App. Seamlessly blending machine learning and Streamlit for efficient and accessible approval. Future plans: feature additions, UI refinements, ensuring a dynamic financial tool.