Colorado Lobbyist Data Analysis

Tools Used: Python, Pandas, NumPy, Matplotlib, Excel
Skills Showcased: Data Extraction, Cleaning, EDA, Visualization, Automation

Web Scraping to PowerBI

This section showcases my portfolio and project explanations.

people sitting down near table with assorted laptop computers

Project Overview

This project focuses on identifying irregular patterns in public lobbying expenditure data from the State of Colorado. Using a government API, I built a complete data pipeline using Python—from extraction and cleaning to exploratory analysis and visualization.

Project Details

Extracted the data from the Colorado government open data website using Python (1_download_data.py)
Cleaned and processed the dataset to normalize formats and remove incomplete records (3_clean_data.py)
Performed basic analysis and created visualizations using Matplotlib (4_analysis_and_graphs.py)
Results include: total expenditure trends, average employment trends, and firm participation patterns
Summary of the results from output file.
- Highest Spending Year: 2023 with $22,622,885.23
- Lowest Spending Year: 2022 with $12,482,512.40
- Highest Average Employment: 2019 with 559.15 employees
- Lowest Average Employment: 2014 with 485.63 employees
- Most Firms Reported: 2012 with 18,154 firms
- Fewest Firms Reported: 2022 with 10,983 firm
Once done I wrote python code to upload to MongoDb
This pipleine is not end to en dbuilt due to available resource but could be automated to reflect updated data
Following are the Images generated from Matplotlib