Mohammed Zniber
Chemical Data Scientist with 4+ years of experience in Chemical Sensors, Metabolomics and Machine Learning
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Exploring the Potential of Chemometrics, Data Science, and Machine Learning
Protomix : A Python Package for 1H-NMR Metabolomics Data Preprocessing
This project focused on the development of a preprocessing pipeline for NMR data in Python. For more details, check out the scientific publication describing the package. You can also access the full documentation on ReadTheDocs and the notebook below illustrates a glimpse of the capabilities that this library has to offer.
PyShift: NMR Peak Alignment using Icoshift Algorithm in Python
This project aims to implement the Icoshift algorithm in Python to achieve accurate alignment of NMR spectra. The implementation’s performance will be assessed using the Wine NMR dataset, showcasing its ability to effectively handle variations in peak positions.
A Potentiometric Multisensor Array for Prostate Cancer Diagnosis
A potentiometric electronic tongue combined with machine learning was used to distinguish between prostate cancer patients and those with benign prostate hyperplasia by analyzing urine samples.
A Custom Data Collection Pipeline Using BeautifulSoup
The objective of this project is to utilize BeautifulSoup for gathering pertinent data and valuable insights from a rental website. This will enable users to access a comprehensive list of apartments along with their specific details. Additionally, the project includes the creation of an interactive Power BI dashboard, facilitating data visualization for a more engaging user experience.
An Interactive Power BI Dashboard for Apartments’ data
The project includes the creation of an interactive Power BI dashboard, facilitating data visualization for a more engaging user experience. The data was retrieved using BeautifulSoup in the aforementioned project.
Important note: Power BI must be installed on your computer in order to interact with the dashboard.
A Comparative Analysis of Rice Varieties Osmanjik and Cameo using CatBoost Classifier and SHAPley values
This project conducts a comparative analysis of rice varieties Osmanjik and Cameo using the CatBoost classifier for accurate classification. SHAPley values are employed to interpret the model’s decisions and identify discriminative features between the two varieties.
