https://github.com/derrickbaruga7/python-data-analysis

This project analyzes ORU’s off-season sewer usage using Python, with `pandas` for data handling, histograms and line plots for exploration, and a `scipy`-based model for prediction. Pearson’s correlation and visualizations help reveal key trends and relationships.
https://github.com/derrickbaruga7/python-data-analysis

analytics data data-science visualization

Last synced: 11 months ago
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This project analyzes ORU’s off-season sewer usage using Python, with `pandas` for data handling, histograms and line plots for exploration, and a `scipy`-based model for prediction. Pearson’s correlation and visualizations help reveal key trends and relationships.

• Host: GitHub
• URL: https://github.com/derrickbaruga7/python-data-analysis
• Owner: derrickbaruga7
• License: mit
• Created: 2024-07-24T17:42:32.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-26T14:01:14.000Z (almost 2 years ago)
• Last Synced: 2025-04-02T19:47:06.722Z (about 1 year ago)
• Topics: analytics, data, data-science, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 1.5 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Python-Data-Analysis

This project analyzes ORU’s off-season sewer usage using Python, with `pandas` for data handling, histograms and line plots for exploration, and a `scipy`-based model for prediction. Pearson’s correlation and visualizations help reveal key trends and relationships.

Executive Summary

The project aims to understand and reduce sewer usage during the off-season (November to April) at ORU. It explores factors influencing sewer usage, including cooling degrees and water usage, and builds a predictive model to minimize off-season usage.

Goals of the Project

Understand Off-Season Sewer Usage: Analyze factors affecting sewer usage during off-season months and hypothesize methods to reduce it.

Analyze Relationships: Examine how cooling degrees, water usage, and sewer charges relate to sewer usage.

Predictive Modeling: Develop a model to predict and reduce off-season sewer usage.

What We Did

Exploratory Data Analysis: Generated histograms and line plots to capture usage patterns and seasonality.

Usage Comparison: Compared off-season and on-season sewer usage to identify high-contributing meters.

Predictive Modeling: Created a model with a low Mean Squared Error to estimate future sewer usage.

Results

Usage Patterns: Histograms revealed significant variations in water and sewer usage, highlighting seasonal trends and anomalies.

Seasonal Trends: The summary line graph showed a decrease in sewer usage from 2012 to present, with a spike in off-season usage from 2014 to 2017.

Trouble Meters: Identified meters with higher off-season usage, such as Quad.Maintenance and Hamill_Timko_Dorms, which are key targets for intervention.

Usage Comparisons: Significant decreases in water usage during the off-season were noted, with the NEC, Welcome.Center, and Chapel being less impactful compared to the major users.

Yearly Trends: Off-season sewer usage generally exceeds on-season usage from 2013 onwards, with notable peaks and fluctuations.

Discussion

The analysis provides valuable insights into sewer and water usage patterns, identifying key meters contributing to off-season usage. The predictive model shows promise, but further validation is needed.