https://github.com/jgohel9902/property-analytics-u.s.-owned-and-leased-properties

This project focuses on analyzing the U.S. Inventory of Owned and Leased Properties using datasets from Data.gov. It includes SQL queries for data cleaning and trend analysis, Excel for manipulation and reporting, Python for automated workflows and exploratory data analysis, and Power BI for creating interactive dashboards to visualize key insights
https://github.com/jgohel9902/property-analytics-u.s.-owned-and-leased-properties

data-visualization dataanalysis excel jupyter-notebook pandas powerbi python sql

Last synced: 6 months ago
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This project focuses on analyzing the U.S. Inventory of Owned and Leased Properties using datasets from Data.gov. It includes SQL queries for data cleaning and trend analysis, Excel for manipulation and reporting, Python for automated workflows and exploratory data analysis, and Power BI for creating interactive dashboards to visualize key insights

• Host: GitHub
• URL: https://github.com/jgohel9902/property-analytics-u.s.-owned-and-leased-properties
• Owner: jgohel9902
• Created: 2025-01-28T14:07:27.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2025-01-28T14:10:52.000Z (almost 2 years ago)
• Last Synced: 2025-03-24T05:31:36.819Z (over 1 year ago)
• Topics: data-visualization, dataanalysis, excel, jupyter-notebook, pandas, powerbi, python, sql
• Language: Jupyter Notebook
• Homepage:
• Size: 18.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
Property Analytics: U.S. Owned and Leased Properties

Project Overview

This project analyzes the U.S. inventory of owned and leased properties using data from Data.gov. The analysis involves data cleaning, querying, visualization, and reporting to extract actionable insights into property trends and patterns.

Features

SQL Queries:

Data cleaning and duplicate removal.

Optimized queries for trend analysis across the "Leases" and "Buildings" datasets.

Excel:

Advanced data manipulation with pivot tables and charts.

Summary reports showcasing key insights.

Python (Jupyter Notebook):

Exploratory Data Analysis (EDA) using Pandas and Matplotlib.

Automated workflows to prepare data for visualization.

Power BI:

Interactive dashboards highlighting property types, lease patterns, and trends.

Visualizations for effective data storytelling.

Tools Used

SQL

Microsoft Excel

Python (Pandas, NumPy, Matplotlib)

Power BI

Visual Studio Code

Dataset

The project uses datasets titled Leases and Buildings sourced from Data.gov.

How to Use

Explore the SQL scripts, Python notebooks, and Excel files in their respective folders.

Open the Power BI file to view interactive dashboards.

Key Files

SQL/: Contains SQL scripts for data cleaning and analysis.

Python/: Jupyter Notebook for automated EDA.

Excel/: Cleaned datasets, pivot tables, and charts.

Power BI/: Interactive dashboards.

Insights Gained

Patterns and trends in owned and leased property usage.

Key metrics for property analysis, including lease distribution and property types.

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