A growing collection of end-to-end analytics projects that demonstrate data cleaning, exploratory analysis, modeling, and business-facing insights. This repository currently includes a Telco churn analysis notebook and will expand with additional notebooks over time.
I’m an aspiring data analyst focused on developing strong, practical analytics skills that translate directly to business impact. I’m currently transitioning from operational roles while pursuing a college education and building a portfolio of real-world data projects.
My work centers on data cleaning, exploratory analysis, SQL querying, and visual storytelling using Python and BI tools. I aim to answer clear questions, surface meaningful patterns, and communicate insights in a way that supports informed decision-making.
This portfolio reflects my commitment to mastering the fundamentals and building the kind of analytical judgment required in an entry-level data analyst role.
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├── README.md
└── Telco_Customer_Churn_Project.ipynb
As new projects are added, each notebook will live at the repo root with a clear, descriptive filename. Supporting assets (data extracts, images, and helper scripts) will be added in future project-specific folders when needed.
File: Telco_Customer_Churn_Project.ipynb
Highlights:
- Loads the Telco churn dataset from Kaggle.
- Cleans and prepares the data (including total charges and churn encoding).
- Builds feature engineering risk flags.
- Explores churn drivers with visualizations and cohort analysis.
- Builds a logistic regression model for churn prediction.
- Estimates ARPU and LTV by contract type, then summarizes recommendations.
- Set up a Python environment (Python 3.9+ recommended).
- Install dependencies (example):
pip install pandas numpy matplotlib seaborn scikit-learn kagglehub
- Open the notebook in Jupyter or VS Code.
- Run all cells from top to bottom.
Note: Kaggle data downloads require Kaggle credentials configured on your machine. See the Kaggle API documentation for setup instructions.
- Python: data wrangling, modeling, and visualization
- Pandas / NumPy: data preparation
- Matplotlib / Seaborn: visualization
- Scikit-learn: modeling and evaluation
- KaggleHub: dataset download
Planned additions include:
- Additional datasets and notebook-based case studies
- Cleaned datasets and reusable helper scripts
- Consistent project templates for faster navigation
If you'd like to collaborate or provide feedback, feel free to reach out via GitHub.