LA_Crime

A data analysis & machine learning project focused on crime trends in Los Angeles. This repository includes data preprocessing, exploratory data analysis (EDA), visualizations, and predictive modeling efforts.

Project Overview

The goal of this project is to explore and analyze crime data from Los Angeles to uncover patterns and, where possible, build a predictive model to forecast crime occurrences. The project covers:

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA) to understand crime trends by category, location, and time
• Visualization of temporal and spatial patterns
• Regression / predictive modeling to estimate crime occurrence or trends

---

Dataset

• LA_Crime_dataset.csv — the primary dataset used for analysis
• regression dataset/ — contains derived/processed data used for modeling

The data includes (but may not be limited to) crime incident types, locations (e.g. neighborhoods or coordinates), timestamps, and related features.

---

Getting Started

Prerequisites

• Python 3.10.18
• Environment management tool (conda)
• Packages as specified in environment.yml

Installation

1. Clone the repository:

   git clone https://github.com/smusab9152/LA_Crime.git
   cd LA_Crime

2. Clone the enviroment in conda:

   conda env create -f enviroment.yml