Project Title: Electrifying Analytics: Web Scraping for AI-Powered Electricity Rate Predictions
Overview
This project aimed to implement web scraping techniques to extract electricity rates by state in the US from EnergyBot. The goal was to use this data to build an AI application capable of analyzing and predicting electricity rates, providing valuable insights for consumers and businesses alike.
Tools and Technologies
Programming Languages: Python
Libraries: BeautifulSoup for web scraping, Pandas for data manipulation, Requests for HTTP requests
Technologies: Jupyter Notebook for code development and documentation
Approach and Methodology
The project commenced with extracting data using the Python Requests library to access the website and BeautifulSoup to parse the HTML content. We specifically targeted tables listing electricity rates by state. Data cleaning involved removing unwanted characters and converting string values to numerical types for analysis. The cleaned dataset was then used to train a predictive model aimed at forecasting electricity rates.
Results and Impact
The project successfully extracted and cleaned data on electricity rates from all 50 states. Preliminary analysis offered insights into regional price trends and historical rate changes. The predictive model, still in its development phase, aims to forecast future rate changes, potentially helping users to anticipate costs and optimize energy usage.
Visuals
web page
extracted dataframe
Code and Repository Link
For a detailed look at the project code and methodology, visit the GitHub repository: Electrifying Analytics Project Repo
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