Population Growth vs GDP Trends Across African Sub-Regions
Tools: Python, Excel, World Bank Data, IMF Data
- Analyzed population growth and GDP trends across 24 African countries grouped into East Africa, Francophone West Africa, and Southern Africa.
- Applied time-series analysis to evaluate development patterns relative to Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs).
- Generated insights on economic sustainability and regional development policies.
- Visualized macroeconomic trends for actionable decision-making.
ECG Signal Processing and Heartbeat Detection (Pan–Tompkins Algorithm)
Tools: MATLAB, Signal Processing, Time-Series Analytics
- Implemented the Pan–Tompkins algorithm on multiple ECG signals to detect QRS complexes and heartbeats.
- Extracted and analyzed key cardiovascular metrics including heart rate, RR intervals, and QRS width.
- Applied filtering and noise reduction techniques to improve signal clarity.
- Performed biomedical time-series feature extraction and analysis.
Impact of Foreign Direct Investment on GDP in V4 Countries (1993–2021)
Tools: Python, Excel, Regression Analysis, Econometrics
- Analyzed FDI impact on GDP per capita and national income in Czech Republic, Hungary, Poland, and Slovakia.
- Applied regression modeling and correlation analysis to quantify economic relationships.
- Produced macroeconomic insights to support policy interpretation and forecasting.
- Built long-term economic trend visualizations using historical data.
Machine Learning Models for Biomedical Risk Prediction
Tools: Python, MATLAB, Neural Networks, SVM, Logistic Regression
- Built ML models including Linear Regression, Logistic Regression, SVM, and Feedforward Neural Networks.
- Predicted tumor characteristics and blood pressure risk levels from biomedical data.
- Performed feature engineering, bias-variance analysis, and model optimization.
- Designed end-to-end ML pipelines from preprocessing to evaluation.
In this report we implemented the following machine learning algorithm on biomedical data sets: feed-forward neural network using backpropagation on the fisher iris data set, regularized linear regression and bias variance and SVM in determining the normal, elevated and stage1 high blood pressure of a patient give the systolic and diastolic blood pressures.
Image Enhancement and Filtering for Biomedical Imaging
Tools: Python, MATLAB, Image Processing
- Implemented contrast enhancement, noise filtering, and image transformation techniques on biomedical images.
- Applied edge detection, intensity slicing, and kernel filtering to improve image clarity.
- Enhanced diagnostic interpretability of biomedical image datasets.
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