Can data science write the perfect Christmas song? In this video, we use real Spotify data, machine learning, and music analysis to uncover what actually makes a festive hit. From Lyrics and sentiment arcs to keys, BPM, and danceability, we build a model using TF-IDF and Elastic Net regression to find out what works and what definitely doesn’t.

Along the way, we battle the Spotify, API, analyse classics from Mariah Carey to Slade, and turn messy real world data into a statistically informed Christmas song… which we then actually write.

You’ve heard this argument a million times. Some people are like, “It’s set at Christmas, there are carols, of course it’s a Christmas movie.” Other people: “It’s just an action film with tinsel, calm down.” So instead of screaming on the internet for the rest of my life, I did the only sensible thing: I built a model.

In this video I’m going to show you how I trained a simple Christmas-movie classifier using soundtrack data and basic movie metadata, and then used it to settle the age-old internet debate so we never have to talk about it again. We’re literally going to turn the Die Hard argument into machine learning.

So, what was the actual goal here? I wasn’t trying to build the biggest, fanciest AI in the world. The question was much pettier than that: can I take the stuff people always bring up in the Die Hard argument and turn it into numbers, train a model on a bunch of movies, and ask it, “Okay, based on everything you’ve seen… what do you think about Die Hard?”

Santa is out of time. He's got 32 houses to hit in Springfield, a tight schedule as it is, and he's spent so long optimising his naughty list, he hasn't got a single minute left to calculate the best route. But never fear, not even we’re Evil enough to not help Santa. 

We’re going to use the Traveling Salesman problem to save the Simpsons’ Christmas before it becomes MATHEMATICALLY IMPOSSIBLE. 

Today we’re taking a look at three algorithms to save the magic of Christmas. Can any of them do it? Let’s find out. 

So there comes a time in every adult’s life where an annoying child asks how Santa could possibly visit every house out there. I’ve no idea why we don’t get given response scripts on our eighteenth birthday, but today I’m going to give you what you need to baffle the child so much that they never ask the question again. Bells ready to jingle? Here we go

Black Friday is back. But are we still that into it or is that just leftover lockdown energy? Remember 2020, when Switches disappeared in minutes and we were flexing our Animal Crossing islands (and yes, the fruit absolutely mattered). With a new Switch on the horizon, is Black Friday still the best time to buy, or just a myth we love to repeat? I’m pulling some google trends data to see if 2025 can beat peak lockdown

As data scientists, people think we’re magicians. When they have a problem to solve, they’ll just bring it to us and we’ll wave our magic wand and suddenly they’ll have the perfect model for what they need. Oh, what’s that? You need data to be able to do that? Wave that magic wand again and I’m sure it’ll appear. 

Ever wondered why comparing Google Trends across countries feels impossible? I stumbled across this problem when I wanted to use google trends data to understand what it is that drives people. 

I thought it’d be an interesting video on multilinear regression, that I’d grab the google trends data for motivation and some other clever terms and off we go, but boy was I wrong.

What motivates people? Is it passion? Purpose? A good cup of tea?

Having something to queue for? The promise of a Sunday roast? The fear of disappointing your Nan?

I asked my colleague Caroline what motivates people in Brazil. Her answer? Football. Alright, but I'm English, I've been singing "It's Coming Home" since birth. She then suggested beaches and carnivals. That's the differentiating factor I'm looking for. We don't have the weather for those.

It's not all cultural stereotypes. I made a cup of tea for a prop for the video which I don't drink and SHE does. (Don't let the King find out, or we're both in trouble). So if what motivates people depends a lot on where you're from. There's something interesting there. Maybe this basket will help?

It's a spooky season. That gave me a terrible, wonderful idea: let’s make my colleagues watch those jump scares scenes while we track their heartbeats. For research. And maybe for laughs. We’ve got sensors, timestamps, and a playlist designed to ruin office friendships. Let’s find out who screams first and what the data says.

Artificial intelligence, it’s amongst us, whether we choose it to be or not, it’s in our homes, on our phones and in our daily lives. But in films Ai is less Siri? And more serial killer From murderous robots to big brother style overlords, Horror loves to imagine what could go wrong with this type of technology, sparking a new or potentially an existing fear into all of us. But how much of this is just‍ ‍popcorn fiction and how much could actually happen?

It's spoooooky season which means warm jumpers, pumpkin spice and the terrifying treachery of the Simpsons Treehouse of horror! And Bart's pretty good at coming up with spooky stories at this point, but is he any good at trick or treating? Let's use data science to find out.

What actually makes a horror movie good? Is it how many times you jump out of your chair or peek through your fingers? The plot? That creeping sound that grabs you from behind?
Is it what the Academy says… or what we feel in the dark? This way, you’ll know exactly why The Ring chills differently than Jaws, and what levers directors pull to make you want to look away.

Let’s test it with data science.

We’ll build a dataset: jump-scare counts, pacing, runtime, jump-scare intensity, critic and audience scores. Turn it into a clear, testable recipe for the perfect horror movie.

Less guessing. More evidence.
Let’s do some Evil Work.

Google Trends. What a gift to society this is. If not for google trends, how would we have ever known that more Disney movies released in the 2000s led to fewer divorces in the UK. Or that drinking Coca Cola is an unknown remedy to cat scratches. 

Wait, am I getting confused by correlation vs causation again?

Imagine a future where machines do everything for us. Where humans just…atrophy. Where we become so reliant on automation we literally can’t walk anymore. Where we can clap our hands and a robot will appear to solve our every whim, whether that’s something to drink or shade from the sun. Wait… Isn’t that just WALL-E?

Alright so it’s fair to say that the fact we haven’t invented hover-chairs yet means we’re a little way off Disney’s prediction but the future could, in some ways, be much closer than you think. It’s not curious and heart warming little robots like WALL-E and it’s not Terminators either. It’s something far more subtle, something that many of us interact with every single day. It’s AI. Specifically, ChatGPT. 

And it’s not just tasks we’re outsourcing either. We might be outsourcing our intelligence.

Is this the dawn of a new era of human ingenuity or are we on the fast track to intellectual laziness? The latest research is sounding some serious alarms.