Skip to content

heathbrew/Sales-Prediction-Analysis-with-PySpark

View on GitHub ↗Related repositories →

This project demonstrates an end-to-end analysis of synthetic sales data, covering exploratory data analysis, linear regression, random forest regression, and KNN for prediction. Each step involves plotting and regression fitting to answer specific research questions.

active 2024-10-25 → 2024-10-25 (UTC)

Complete coverage28,189 / 28,189 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-11-01 (UTC)
Events
4
Pushes
2
Pull requests
0
Issues
0
Stars
0
Forks
0

Activity over time

Daily event counts in the loaded window

Line chart, 1 days from 2024-10-25 to 2024-10-25. Pushes: 2 total, peak 2 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.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Top contributors

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

ContributorContributionsPushesPRsComments
heathbrew2200

Recent activity

Latest issues, pull requests and releases

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.