Figure 1.
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Welcome to my data analysis journey exploring the Chicago Crime Dataset! This project is just the beginning, and I invite you to join me in uncovering insights and trends within the data. Your feedback is invaluable as we embark on this exploration together! 😀
You can reach me for further discussions and inquiries at my email address or LinkedIn: 👇🏽
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Introduction: In this project, I conducted a comprehensive data analysis of the Chicago Crime Dataset using Google BigQuery to uncover insights and patterns regarding reported incidents of crime in the city. The dataset spans from 2001 to 2023, capturing a wide range of crime types, locations, and arrest outcomes.
<aside> <img src="/icons/timeline_blue.svg" alt="/icons/timeline_blue.svg" width="40px" /> Google's BigQuery is a managed data warehouse service that operates without the need for servers, enabling scalable analysis of large data sets. This Platform as a Service (PaaS) supports querying using SQL and includes native machine learning features.
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Objective: The primary objective of this project was to analyze the Chicago Crime Dataset and extract actionable insights that could inform decision-making for law enforcement agencies, policymakers, and community stakeholders. By leveraging BigQuery and SQL for data analysis, I aimed to identify trends, patterns, and correlations within the dataset.
Technologies Used:
<aside> <img src="/icons/light-bulb_yellow.svg" alt="/icons/light-bulb_yellow.svg" width="40px" /> Prerequisite:
Before deploying this project for learning purposes, please ensure you have or create a Google Cloud Platform (GCP) account. Google BigQuery is used for analyzing the Chicago Crime Dataset, and having a GCP account is necessary to access and run SQL queries in BigQuery. If you don't have a GCP account, you can sign up for free here.
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Methodology: