StockBot 3000: Automating Your Financial Panic Since 2020

My project namely “StockBot 3000: Automating Your Financial Panic Since 2020” is a Python-based automation tool that fetches real-time financial data from the Yahoo Finance API. It retrieves stock market information for a given company (e.g., Apple, Google, Tesla), processes the data using Pandas, and saves it in a structured format for further analysis.

This project enables the user to monitor stock performance without manual checks, analyze historical trends for data-driven decisions, integrate real-time data into dashboards, reports, or AI models, and reduce manual effort through automated data collection. I mainly use it to monitor and adjust my personal investment portfolio.

You can check out the full code on my GitHub repo here!

EgoBot1200: Your Online Reputation Damage Control Dashboard

EgoBot1200 is a Python-based automation tool designed to help businesses and individuals monitor and manage their online reputation in real-time. By aggregating data from social media, review platforms, and news sources, this project provides actionable insights into brand sentiment, customer feedback, and potential PR crises. It’s a powerful tool for marketers and businesses looking to stay ahead of their reputation game.

You can check out the full code on my GitHub repo here!

What does it do?

Social Media Monitoring – Tracks brand mentions on platforms like Twitter and Reddit, performing sentiment analysis to gauge public perception.

Review Aggregation – Collects and analyzes customer reviews from platforms like Google and Yelp, identifying trends and common themes.

Automated Reporting – Generates visual dashboards and CSV reports for easy analysis and sharing with stakeholders.

Crisis Detection – Sends real-time alerts via Slack or email when sudden spikes in negative sentiment are detected, enabling quick response to potential PR issues.

How It Works

Data Collection – Uses APIs (e.g., Twitter, Google Maps, NewsAPI) and web scraping to gather data from social media, review sites, and news articles.

Sentiment Analysis – Leverages Natural Language Processing (NLP) libraries like TextBlob and VADER to classify sentiment as positive, negative, or neutral.

Dashboard – Built with Dash or Streamlit, the dashboard visualizes key metrics like daily sentiment trends, review ratings, and brand mentions.

Alerts – Monitors sentiment trends and sends automated alerts when predefined thresholds are breached.

Technical Stack

Languages: Python

Libraries: pandas, numpy, TextBlob, VADER, Tweepy, Dash, BeautifulSoup, Slack SDK

APIs: Twitter API, Google Maps API, NewsAPI

Deployment: Hosted on Heroku/AWS/Google Cloud

Why It Matters

In today’s digital age, a brand’s reputation can make or break its success. This project demonstrates how automation and data-driven insights can help businesses:

– Proactively manage their online presence.

– Identify and address customer concerns before they escalate.

– Make informed decisions based on real-time data.

Sample Output

Dashboard – Interactive visualizations of sentiment trends, review ratings, and brand mentions.

Reports – CSV/Excel files with detailed sentiment analysis and review data.

Alerts – Real-time notifications for negative sentiment spikes.