Data-analytics

Python codes for machine learning, statistics, bioinformatics, and epidemiology

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Overview

This repository contains Python scripts and notebooks for analyzing large-scale datasets, with a focus on applications in:

• Machine learning
• Statistics
• Data cleaning and structuring
• Visualization
• Bioinformatics
• Infectious diseases and epidemiology

The goal is to provide a resource for researchers, data scientists, and students working in computational biology, genomics, molecular diagnostics, and related fields.

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Features

• Machine Learning & Modeling: Scripts for supervised and unsupervised learning, feature engineering, model evaluation, and explainability (including SHAP, feature importance, and more).

• Bioinformatics: Tools for processing sequencing data, variant analysis, resistance gene identification, and genomics analytics.

• Statistics & Data Cleaning: Utilities for data wrangling, normalization, deduplication, and advanced statistical analysis.

• Epidemiological Analysis: Code for cohort analysis, prevalence and incidence calculations, interaction and association testing, and public health surveillance analytics.

• Visualization: Publication-ready plots and multi-panel figures using Matplotlib, Seaborn, and other libraries.

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Use Cases

• Infectious disease diagnostics and surveillance
• Antimicrobial resistance analytics
• Genomic and molecular biology data analysis
• Epidemiological modeling
• General-purpose data analytics in biomedical research

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Repository Structure

• /scripts/ – Core Python scripts and utility functions
• /notebooks/ – Example Jupyter notebooks and analyses
• /data/ – Sample or test datasets (de-identified or simulated)
• /figures/ – Example output plots and visualization templates

Folders will be updated as the repository grows.

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Getting Started

1. Clone this repository:

   git clone https://github.com/joseiky/Data-analytics.git
   cd Data-analytics

2. Install required dependencies: Most scripts require pandas, numpy, scipy, scikit-learn, matplotlib, seaborn, and jupyter. You can install them using:

   pip install -r requirements.txt

   (A sample requirements.txt will be provided soon.)

3. Run scripts or notebooks:

   • Navigate to the relevant folder
   • Open Jupyter notebooks or run .py files as needed

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License

This repository is licensed under the MIT License. See LICENSE for details.

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About

Created and maintained by Dr. John Osei, Extraordinary Professor, Medical Microbiology & Bioinformatics Contact: jod14139@yahoo.com

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Keywords

machine-learning • bioinformatics • genomics • statistics • epidemiology • infectious-diseases • antibiotic-resistance • visualization • data-cleaning

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Feel free to contribute, open issues, or fork the repository!

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