Hey everyone, I’m back again with another little project I’ve been tinkering with. This time, I wanted to play around with something a bit different, so I decided to try my hand at making a program that predicts crossword puzzle answers. Sounds kind of cool, right? Well, let me tell you, it was a bit more involved than I initially thought!
Getting Started
First things first, I had to find a way to get some data. I mean, how can you predict crossword answers without knowing what questions are usually asked? So, I spent a good chunk of time just scraping the internet. I wrote a little Python script to crawl through a bunch of different crossword puzzle websites and grab as many clues and answers as I could. It took a while, and my script wasn’t exactly perfect, but it got the job done. I ended up with this huge text file just filled with clue-answer pairs. Think of stuff like “Capital of France” and “Paris”. Lots and lots of that.
Cleaning Up the Mess
Of course, the data I got was a total mess. There was a ton of random junk in there, extra spaces, weird characters, you name it. I had to roll up my sleeves and do some serious data cleaning. This part was pretty tedious, I won’t lie. I used regular expressions and some more Python code, to remove all the garbage from the file, and get it into a format that made sense, mainly just lowercasing, removing whitespace.
Building the Predictor
Next up, the actual prediction part. I decided to keep things simple and use a basic approach. I thought, “Okay, I’ll just count how often each answer appears for each clue.” So, if the clue “Capital of France” showed up 100 times, and the answer “Paris” was there 90 times, then “Paris” would be a pretty good guess, right? I stored all these counts in a big dictionary and it worked ok, but it felt a little too basic, you know? So many clues can have the same answer, and just going by frequency didn’t seem like it would cut it.
Adding Length into the Mix
Then it hit me – crosswords also give you the length of the answer! So I figured, why not use that too? I modified my program to also take the length of the answer as input. Now, when you give it a clue and a length, it first filters out all the answers that don’t match the length, and then it looks at the frequencies of the remaining answers. This made a huge difference! Suddenly, my predictions were way more accurate. It was like, “Oh yeah, this actually kind of works!”
Testing It Out
I spent a bunch of time just playing around with it, plugging in different clues and lengths. It was pretty satisfying to see it come up with the right answers most of the time. Of course, it’s not perfect. There are still some tricky clues that it struggles with, especially those really clever ones that rely on wordplay or puns. But overall, I was pretty happy with how it turned out.
Final Thoughts
This whole crossword prediction thing was a fun little project. It was a good way to practice my coding skills and learn a bit more about data processing and basic prediction techniques. It definitely reminded me that even seemingly simple tasks can have a lot of hidden complexity. And hey, maybe one day I’ll expand on this and build a full-blown crossword-solving AI. Who knows?
Key takeaways from this little experiment:
- Data scraping can be messy but necessary.
- Data cleaning is super important. Don’t underestimate it!
- Simple approaches can work surprisingly well.
- Adding more relevant information (like answer length) can make a big difference.
Alright, that’s it for this one. Hope you found it somewhat interesting! Until next time, stay curious!
