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Windmills Capstone Project

A comprehensive data science and machine learning capstone analyzing the impact of wind farms on housing markets.

Table of Contents

Project Overview

This repository contains a step-by-step capstone project that explores the relationship between wind farm locations and housing market trends. It includes idea generation, a formal proposal, data cleaning, exploratory and inferential analysis, storytelling, machine learning modeling, and final deliverables.

Directory Structure

.
├── .gitattributes
├── README.md                     # Project overview and instructions (this file)
├── 01 Ideas/                     # Initial idea documentation (Word & PDF)
├── 02 Proposal/                  # Project proposal (Word & PDF)
├── 03 Data Wrangling/            # Jupyter notebooks and docs for data cleaning and merging
├── 04 Storytelling/              # Narrative analysis and storytelling notebooks
├── 05 Inferential Statistics/    # Inferential statistics analysis notebooks and docs
├── 06 Milestone Report/          # Interim milestone reports and slides
├── 07 Machine Learning/          # Model training, evaluation, and comparison notebooks
├── 08 Final Report/              # Final report deck, slides, and document
└── 09 CapstoneProject1Data/      # Raw and processed datasets, organized by subfolder

Data

All datasets are stored under 09 CapstoneProject1Data/, organized into:

  • InferentialStatsData/: ZIP code-level data for statistical tests
  • MachineLearningData/: Prepared feature sets for model training
  • MergedData/: Combined Zillow and wind farm data for analysis
  • WindfarmData/: Original wind farm source files and supporting codebooks
  • ZillowData/: Housing market data files

Note: Some data files are large; ensure you have sufficient disk space.

Getting Started

Prerequisites

  • Python 3.7+
  • Jupyter Notebook or JupyterLab
  • Common data science libraries: pandas, numpy, matplotlib, seaborn, scikit-learn, statsmodels

Installation

  1. Clone the repository

    git clone https://github.com/krpopkin/Windmills.git
cd Windmills

  2. Create and activate a virtual environment (optional but recommended)

    python3 -m venv venv
source venv/bin/activate    # macOS/Linux
venv\Scripts\activate     # Windows

  3. Install required packages

    pip install pandas numpy matplotlib seaborn scikit-learn statsmodels jupyterlab

Launching Notebooks

Start Jupyter Lab or Notebook in the project root:

jupyter lab

Open and run the notebooks in numerical order within each folder.

Notebook Workflow

  1. 01 Ideas: Brainstorm and capture project ideas.
  2. 02 Proposal: Solidify objectives, methodology, and expected deliverables.
  3. 03 Data Wrangling: Clean, merge, and prepare data from raw sources.
  4. 04 Storytelling: Exploratory data analysis and narrative insights.
  5. 05 Inferential Statistics: Hypothesis testing and statistical validation.
  6. 06 Milestone Report: Progress report with interim results.
  7. 07 Machine Learning: Train and compare multiple predictive models.
  8. 08 Final Report: Compile final findings and visualizations into a presentation.

Contributing

This capstone project is provided “as-is” for educational purposes. Contributions or suggestions are welcome via Issues or Pull Requests.

License

This project is currently unlicensed. Feel free to use or adapt it for non-commercial or educational purposes.

Author

Ken Popkin
GitHub: krpopkin
Email: krpopkin@gmail.com