Can Python Really Predict the Next Viral TikTok Trend?

TikTok feels like chaos. One day it’s a random sound effect, the next it’s a dance challenge, and before you know it, the whole internet is in on the joke. From the outside, virality looks like magic.

But I had a different suspicion: maybe it’s not magic — maybe it’s math.

So I built a Python pipeline to test it. Not a toy script, but a step-by-step, end-to-end workflow:

• cleaning messy TikTok data,
• engineering momentum features,
• training models that respect time,
• tuning thresholds for reality,
• and exporting CSVs with predictions you can actually use.

And yes — if you follow along, you’ll have the entire pipeline ready to run by the end of this article.

Step 1 — Wrestling the Chaos Into Clean Data

TikTok data isn’t neat. Dates don’t parse. Views come as strings. Likes and shares sometimes vanish. If I hadn’t fixed that first, the whole experiment would’ve collapsed.

Here’s how I cleaned it:

import pandas as pd
import numpy as np
# Load dataset
df = pd.read_csv("tiktok_trends.csv")
# Convert date
df["date"] = pd.to_datetime(df["date"]…