Data-Analytics

Welcome to my Data Analytics Portfolio, which includes structured projects in both Data Science and Data Analysis, implemented in R and Python.

This repository is organized to reflect both the type of analytics and the programming language used, demonstrating a range of skills from exploratory data analysis to machine learning modeling.

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Folder Structure

Data Analytics/ ├── Data Science Project in Python/ ├── Data Science Project in R/ ├── Data Analysis Project in Python/ └── Data Analysis Project in R/

Descriptions:

Folder	Description
Data Science Project in Python	Predictive modeling, machine learning, or automation using Python libraries like scikit-learn, pandas, matplotlib, etc.
Data Science Project in R	Machine learning or statistical modeling using R packages such as caret, tidymodels, ggplot2.
Data Analysis Project in Python	Exploratory and descriptive analysis using Python for insights, visualizations and business reporting.
Data Analysis Project in R	Data wrangling, visualization and reporting using R’s powerful data analysis tools.

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Tools & Technologies

• Languages: Python, R
• Libraries/Frameworks:
  • Python: Pandas, NumPy, Matplotlib, Seaborn, scikit-learn
  • R: tidyverse, ggplot2, dplyr, caret
• Others: Jupyter Notebook, RMarkdown, Git

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Purpose

This portfolio is intended to:

• Showcase a range of data analytics skills
• Provide examples of real-world project structure
• Demonstrate proficiency in both R and Python
• Serve as a learning and reference resource

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How to Use

Each subfolder is a standalone project. To explore:

1. Navigate into any project folder
2. Follow the individual README.md for setup and details (if available)
3. Open notebooks or R scripts for code and results

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Contact

For questions, feedback or collaboration:

• 📧 Email: gmail
• 🌐 LinkedIn: linkedin-profile

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Note: This portfolio is a work in progress. More projects and documentation will be added regularly.