Investigation of Violent Crime Rates per Capita

Overview

This repository contains two major investigations related to violent crime rates per capita:

1. Dimension Reduction and Handwriting Analysis
2. Linear Model Investigation and Derivation

Each subfolder contains its own code, documentation, and results, with a dedicated README for more details.

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📁 Project Structure

Investigation_of_Violent_Crime_Rates_per_Capita/
├── Dimension_Reduction_Investigation_of_Violent_Crime_Rates_per_Capita_And_Handwriting_Analysis/
│   ├── README.md
│   ├── code.ipynb
│   ├── Brief_Introduction.pdf
│   ├── Code_Part.pdf
│   ├── target.pdf
│   └── ...
├── Linear_Model_Investigation_of_Violent_Crime_Rates_per_Capita_And_Derivation/
│   ├── README.md
│   ├── code.pdf
│   ├── code_colab.pdf
│   ├── Brief_Introduction_And_Equation_Derivation.pdf
│   ├── target.pdf
│   ├── coefficient trajectory/
│   │   ...
│   └── Other PICS and Model Performance/
│       ...
└── ...

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1️⃣ Dimension Reduction & Handwriting Analysis

• Location: Dimension_Reduction_Investigation_of_Violent_Crime_Rates_per_Capita_And_Handwriting_Analysis/
• Focus:
  • Application of dimension reduction techniques (e.g., PCA, LDA)
  • Analysis of violent crime rates and handwriting data
  • Jupyter notebook implementation and PDF documentation
• Key Files:
  • code.ipynb: Main analysis notebook
  • Brief_Introduction.pdf: Project background
  • Code_Part.pdf: Full code in PDF format
  • target.pdf: Project requirements/objectives

See the subfolder's README for details.

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2️⃣ Linear Model Investigation & Derivation

• Location: Linear_Model_Investigation_of_Violent_Crime_Rates_per_Capita_And_Derivation/
• Focus:
  • Linear modeling of violent crime rates
  • Theoretical derivations and statistical analysis
  • Visualizations of model performance and coefficient trajectories
• Key Files:
  • code.pdf, code_colab.pdf: Implementation in PDF/Colab format
  • Brief_Introduction_And_Equation_Derivation.pdf: Theory and derivations
  • target.pdf: Project requirements/objectives
  • coefficient trajectory/: Coefficient analysis images
  • Other PICS and Model Performance/: Model performance images

See the subfolder's README for details.

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📑 Notes

• Each subproject is self-contained and can be explored independently.
• For setup, requirements, and detailed methodology, refer to the README in each subfolder.

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Course: STAT 5241 - Statistical Machine Learning
Institution: Columbia University
Semester: Second Semester
Date: February 2025