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FutureGoose/analyzing_crime_data

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In this Machine Learning Engineering lab, we traverse from meticulous data cleaning to deep exploratory analysis, extracting nuanced insights into Chicago crime data. The capstone is a polished XGBoost model, fine-tuned via step-wise Hyperopt and honed through Recursive Feature Elimination, achieving an commendable 89% precision rate.

active 2023-12-152024-04-07 (UTC)

Complete coverage26,429 / 26,429 hourly files (100%) · 2 absent upstream2023-08-152026-08-20 (UTC)
Events
47
Pushes
45
Pull requests
0
Issues
0
Stars
0
Forks
0

Activity over time

Daily event counts in the loaded window

Line chart, 115 days from 2023-12-15 to 2024-04-07. Pushes: 45 total, peak 24 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.

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Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

ContributorContributionsPushesPRsComments
FutureGoose454500

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No issue or PR events — this repo's activity is pushes only.

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.