Transform empty repository into entry-level data science portfolio

[Copilot]

Portfolio showcasing data science fundamentals for entry-level positions: EDA, visualization, and ML.

Structure

• Main README: Skills overview, project summaries, setup instructions
• Three projects with dedicated READMEs and Jupyter notebooks:
  • Exploratory Data Analysis (sales dataset, Pandas/NumPy)
  • Data Visualization (10+ chart types, Matplotlib/Seaborn)
  • Machine Learning (customer churn classification, Scikit-learn)

Implementation

Each notebook is self-contained with synthetic data generation:

• Complete workflows from data loading to insights
• Business-focused recommendations
• Professional visualizations and documentation
• 469-729 lines per notebook

Supporting Files

• requirements.txt: Core data science stack (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Jupyter)
• .gitignore: Python artifacts excluded
• LICENSE: MIT
• data/: Placeholder for datasets

Notebooks use standard APIs and best practices (train/test splits, cross-validation, feature scaling, multiple evaluation metrics).

Original prompt

I'm going to use this repo as a showcase to show hiring managers that I possess data science skills worthy of an entry level position.

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