How fahadpashaa/comp2040-assignment3 works
This repository is a notebook-based Los Angeles crime analysis project. Its documented workflow reads the crime CSV, cleans and engineers features, explores patterns, trains a baseline Logistic Regression model, and interprets the results. The README and helper source were inspected; notebook source was not sampled, so the stage sequence reflects the documented workflow rather than verified cell-level calls.
Main entry points: analysis.ipynb Clean Data · Crime_Data_from_2020_to_2024.csv Crime CSV · helpers.py Analysis Helpers
Components 9
Data Input
- Crime CSV
data/Crime_Data_from_2020_to_2024.csv
Analysis Workflow
- Clean Data
notebooks/analysis.ipynb - Engineer Features
notebooks/analysis.ipynb - Explore Patterns
notebooks/analysis.ipynb - Logistic Regression
notebooks/analysis.ipynb - Interpret Results
notebooks/analysis.ipynb - Crime Storytelling
notebooks/LA_Crime_Storytelling.ipynb
Reusable Helpers
- Analysis Helpers
src/helpers.py
Other components
- Analyst
Connections 7
- Analyst → Clean Data: runs notebook
- Crime CSV → Clean Data: provides records
- Clean Data → Engineer Features: prepares data
- Engineer Features → Explore Patterns: supports exploration
- Engineer Features → Logistic Regression: supplies features
- Logistic Regression → Interpret Results: produces scores
- Clean Data → Analysis Helpers: calls helpers