Okay, so I’ve been messing around with this “Li Tu Prediction” thing, and let me tell you, it’s been a bit of a rollercoaster. I wanted to predict something, and I thought, why not try this method I heard about? Let’s get this done!
First, I gathered all the stuff I needed. This was mostly data, and like, A LOT of it. Think spreadsheets, notes, the whole nine yards. I spent a good chunk of time just making sure everything was in order, you know, no messy data sneaking in.
Setting Up My Workspace
Next, I fired up my trusty computer. I’m using Python because, well, it’s pretty versatile. And I like how it looks. Then, I installed some libraries. I am not very sure about these things, but I followed some guys on the Internet and did as they told.
- Pandas: for handling the data.
- Scikit-learn: for the prediction part.
- Matplotlib: because I love me some pretty graphs.
With all that set up, I loaded my cleaned-up data into a Pandas DataFrame. Sounds fancy, but it’s basically just a table. Then, I split the data into two parts: one for training my model and one for testing it. It’s like teaching a dog a trick – you show it examples (training), then you see if it learned (testing).
The Prediction Part
This is where the “Li Tu Prediction” comes in. I’m not gonna lie, I had to look up a bunch of stuff to really understand how it works. But basically, I created a model, fed it the training data, and then let it do its thing. It’s like magic, but with math and code.
I trained it with one method first and test, and then trained and test it again and again with different parrameters set up. Because I think I need to find a best practice.
Once the model was trained, I used it to make predictions on the testing data. It’s like asking the dog to do the trick it just learned. And guess what? It kinda worked! It was right about 78% of the time. Which I found very exiting.
Finally I draw all these data to some beautiful graphs to demostrate the differences.
Final Thoughts
So, that’s my journey with Li Tu Prediction. It wasn’t perfect, and I definitely hit some bumps along the way, mainly data cleaning and finding a best fit. But it was a fun experiment, and I learned a lot. I’m not gonna claim I’m an expert now, but I definitely feel more confident messing around with this kind of stuff. Next, maybe I will be better.
