Diving Into My Weekly Horoscope Project: From Idea to Insight
You know me, always tinkering with something. Lately, I’ve been hooked on the whole “horoscope as data” idea. Not because I’m a massive believer in predicting the lottery numbers, but because synthesizing weekly forecasts from a source like Michele Knight into something digestible for folks tracking their life patterns—especially around money and career—sounded like a killer challenge.
The Initial Hustle: Sourcing the “Magic”
First step, obviously, was getting the actual content. Michele Knight does her thing, posting these weekly rundowns. I didn’t want to just copy/paste; I wanted a repeatable process. I started out manually checking the Virgo forecast every Monday morning, copying the relevant sections on “Money” and “Career.” Yeah, I used a standard spreadsheet, Google Sheets actually, just jotting down the dates and the key sentences. It was slow, tedious, and prone to human error—I missed a week in May because I was swamped with a client project.
- Identify the source URL for the Virgo weekly forecast.
- Manually scrape (read and copy) the Money and Career paragraphs.
- Input data into a spreadsheet with columns for Date, Money Focus, and Career Outlook.
- Realize this is a dumb way to spend my Monday.
Automating the Tedium: Getting Code to Do the Dirty Work
I quickly pivoted. No way was I spending an hour every Monday morning reading and transcribing. I needed a simple scraper. I threw together a quick Python script using requests and BeautifulSoup. The structure of her site is pretty consistent, which was a huge win. I targeted the specific HTML tags that consistently wrapped the ‘Money’ and ‘Career’ sections for Virgo.
The first attempts were messy. The site sometimes uses slightly different div classes for mobile vs. desktop view. My scraper kept grabbing the entire page header sometimes. I spent an afternoon tightening up the CSS selector until it was reliable. I needed it robust enough to handle minor layout changes without breaking my whole system.
The Data Refinement: What Does “Good Fortune” Mean?
Once I had the raw text dumping into a local file—which I then synced to my Google Sheet automatically using the Google Sheets API—the real work began: turning descriptive language into patterns. Horoscopes are notoriously vague, right? “Expect movement in your financial sector,” “a conversation opens doors.” I decided to apply a simple sentiment analysis, not super complicated, just classifying the tone:
- Positive Signal: Words like ‘Gain’, ‘Advantage’, ‘Opportunity’, ‘Increase’.
- Negative Signal: Words like ‘Caution’, ‘Delay’, ‘Review’, ‘Challenge’.
- Neutral/Action Signal: Words like ‘Communicate’, ‘Reflect’, ‘Plan’, ‘Wait’.
I manually labeled the first 10 weeks of data based on these signals, building a small dictionary. Then, I wrote a basic parser that would scan the new weekly text and assign a score (e.g., +2 for Strong Positive, -1 for Mild Negative). This score allowed me to generate a simple weekly trend line.
Sharing the Insight: Making the Data Useful
Having the data in a spreadsheet is one thing; making it shareable is another. I wasn’t going to make people look at my messy Python scripts. I used a simple visualization tool—nothing fancy, just embedding a dynamic chart from Google Data Studio onto a simple static page.
This visualization focused on showing the trend: how many ‘positive signals’ were there this week compared to last week in the money sector? How did the ‘action signals’ for career compare over the past month?
I’ve been running this process now for close to three months. Every Sunday evening, the script fires up, grabs the new forecast, processes the sentiment, updates the Google Sheet, and the Data Studio dashboard automatically refreshes. It’s pretty satisfying seeing that weekly line move. I’ve noticed some interesting correlations—when ‘Review’ pops up in the career section, my own projects tend to hit administrative delays a few days later. Coincidence? Maybe, but tracking it makes it fun. It turned a nebulous weekly reading into a predictable, data-driven check-in.
