> For the complete documentation index, see [llms.txt](https://techacademy.gitbook.io/data-science-wintersemester-24-25/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://techacademy.gitbook.io/data-science-wintersemester-24-25/introduction-to-your-project/about-the-project-guide.md).

# About the Project Guide

## 💡 Hint boxes

This document will guide you through the different steps of your project for the submisson and will provide you with valuable hints along the way:

* Hint for `R`-Track will be in 🏴‍☠️ boxes (think "arrrr!")
* Hint for `Python`-Track will be in 🐍 boxes

## 🤔 Ask for help!

If you’re unsure how to tackle a task, don’t worry—that’s all part of coding! Here’s what you can do:

* **Search online** for the error or question (StackOverflow is a great resource!)
* **Ask a teammate** for help 🤝
* **Reach out to TechAcademy mentors** on Slack, WhatsApp, or during the coding meetups
* **Consult ChatGPT**— <mark style="color:orange;">not for the exact solution</mark>, but to <mark style="color:green;">help troubleshoot errors</mark>

## 📊 Here’s a quick look at the technical parts we’ll cover:

### 👉 Beginner

#### **Exploratory Data Analysis (EDA)**

The first step in many data science projects, including this one, is to explore and understand the data before diving deeper into analysis. You’ll investigate:

* What is the data about?
* Is it clean? Does everything make sense?
* Are there any missing values, patterns, or areas that need special attention?\
  We’ll answer these questions as part of the EDA.

#### Linear Regression

Linear regression helps us understand relationships between variables and make predictions based on data trends. For example:

* Is there a correlation between temperature and violent tendency?
* Does crime target a certain race or gender? (correlation not causation!)

By the end, you’ll have a model that can make basic predictions and help uncover patterns in the data!

### 👉 Advanced

#### **K-Means Clustering**

In this part, you will take a sample of the crime data and identify hotspots around the city by applying clustering methods on the latitudes and longitudes. In the end, you will visualize the locations of the hotspots on the map of LA.