♦Image created on CanvaThey say love is a mystery. I believe love is just an algorithm in a dataset full of too many outliers.
Welcome to the corner of the internet where we don’t just “trust the process,” we work on it. I am Ms.DataByte, and I am on a mission to find Real Love (If there exists one).
Dating in 2025 isn’t about serendipity or meeting someone at a coffee shop (does anyone actually go to those anymore?). It is about optimization. It is about beating the algorithm before it beats you.
Dating apps have become new modes for socializing. Its all about how you present yourself and how the other person perceives you to be. Cause, lets be real… You can post your super exciting “lavish” life on social media, but in real life, you can hardly afford your college fees.
So, I got my hands on a dataset of 50,000 dating app users. Because I didn’t want “dating advice” from a lifestyle guru. I’m an impatient girl. I wanted hard evidence. I wanted to know exactly why people are getting ghosted, why things aren’t working out, and why dating is more difficult than coding.
So, I fired up my Python notebook, cleaned the messy CSVs, and ran the numbers.
⚠️ Quick Disclaimer: This analysis explores dating trends through real statistics. Any observations regarding gender, age, or behavior are based on dataset averages and are intended for educational (and entertainment) purposes only. No feelings were harmed in the making of this Python script.
Myth: “I need to move to the big city to find love.”So is it true? Urban for fun, Suburban for the long run. Being in the city, I have seen a number of relationships shatter because of the difference in location. So, I analyzed Location Type vs. Match Outcome. The bar chart reveals a fascinating split.
♦Geography is destiny. Urban Users have 40% more successful relationships, but their “Catfished” rate is double. The paradox of choice is real in the city. Suburban Users get fewer matches, but a significantly higher conversion rate to “Date Happened.” They are bored enough to actually show up and meet.
The “Dating Puddle” phenomenon is a condition in small towns and remote areas. Your match rate is low, but your “I Already Know Their Cousin” rate is 85%. This must be the reason why One-sided Like is also high. According to the data, relationships are mostly formed in the urban and metropolitan cities; this must be because of the hope of a safe future.
Ms. DataByte’s Advice:
- If you want a reliable partner, swipe in the city, but get ready to face the horrors of love.
- If you want to explore your chances at love, go to the suburbs, have fun with the flow.
- If you live in a small town, expand your radius to 50 miles or prepare to date your neighbor.
But whatever be the case, your location will determine only if you will meet a good candidate… consistent efforts depends on you. Just You.
Myth: “I swipe less, that’s why I get fewer chances.”I created a little metric I like to call the Swipe Score. It calculates a user’s Swipe Right Ratio against their Match Outcome. This will tell if the swiping culture really means if you’re carefully chosen or a random pick.
♦There is a direct, negative correlation between how much you swipe and how successful you are in securing your partner.
The “Optimistic” Swipers with Right-swiping >60% are statistically doomed. The algorithm smells your fear. You are using the app as a dopamine diet.
The “Picky” Swipers with Right-swiping <25% get more matches. Seems like you really invest in knowing and understanding the other person.
Ms. DataByte’s Advice: Stop treating the app like it’s a game of Fruit Ninja. The data says that desperate swiping is a cologne, and the algorithm can smell it. Dating apps are for you to meet new people and try out love life scenarios. You have to make this work for yourself, not doom the other person’s life.
Myth: If I spend more time on the app, it will give me more chances at love.I analyzed App Usage Time vs. Likes Received. This was my favorite chart because it hurts the most.
♦The graph is full of bubbles of tragedy (bursting with Extreme users).
This data demonstrates a “plateau effect,” where increased time spent on the app does not correlate with a higher volume of likes received. Statistically, this suggests that the algorithm prioritizes profile quality or “pickiness” over raw time investment.
Spending 300 minutes a day on the app yields zero statistically significant increase in matches compared to someone who spends 30 minutes.
Thus, swiping does not equal more love. It just equals more thumb cramps.
Myth: “Our timings are mismatched.”I understand this conflict, so I ran a heatmap of Swipe Time of Day vs Match Outcome.
♦The Heatmap may seem a bit confused, cause so am I. Most swipes that happen after midnight end up being “one-sided likes” and ghosted. On the other hand, swipes that happen around 8 am end up being blocked. Thus, The Golden Window is set from 6 PM to 9 PM. This seems just a bit weird, actually. I trust data, but don’t always agree with it.
Ms. DataByte’s Advice: Even I may get swayed a bit, but this is what the data suggests. Never Trust a Swipe Before Breakfast, people might just feel lonely. And “nothing good happens after 2 am” by Ted Mosby (HIMYM). Such wise words. Truly.
The Myth: “Most guys get ghosted.”Everyone is terrible, but in different ways. NO one should point fingers at each other. But just to resolve the mystery, I broke down the Chat Ignored statistic by Gender. And the truth is out.
♦Statistics hurt, don’t they? As a female, I know people always feel that we happen to get the perfect date, the perfect relationship, the perfect partner. But that’s not always true. Some of us are hiding the scars of so many failed dates. With more data exploration, I found out,
- Men are statistically more likely to ghost after the first date.
- Women are statistically more likely to ghost during the chatting phase.
Ms. DataByte’s Advice: Don’t take it personally. The data shows that ghosting is less about you and more about the other person’s inability to type “No thanks.” How you carry yourself out of it and give another chance at life, that’s the game. And I really hope you master it!
Myth: The app is the problem♦We often think the algorithm is working against us, but this chart shows it’s actually working for the community. The developers have a difficult job; they have to turn human chemistry into binary code.
This data shows the “punishment” for over-swiping is just their way of maintaining balance. It encourages users to be thoughtful rather than mechanical.
In a world of infinite choice, the algorithm is just trying to force us to pause and actually look at the person behind the pixel.
Is there a Love Algorithm?I didn’t just want to analyze the past, I wanted to predict the future. So, I built a Logistic Regression Machine Learning Model to calculate a “Compatibility Score” between any two users.
I didn’t use star signs or “eye color.” I used only the three features that scientifically determine if you will survive a dinner date (by dating apps data).
I boiled compatibility down to three non-negotiable variables:
- Swipe Behavior
- App Usage
- Interests.
The logic is simple; if you use the “shotgun approach” (swiping on everyone) while your match is a “sniper” (swiping on almost no one), or if you respond instantly while they check the app weekly, you are mathematically incompatible.
Finally, I used a Jaccard Similarity index to verify if you actually both like hiking, or if one of you is just lying for the aesthetic.
♦The Model’s Verdict: I now know how dating profiles find you the one. Dating apps use models with your data to help you find your most compatible partner. When I tried to do it, I got an accuracy of 99.15%. I imagine myself to be the Date-Lyfe Advisor now.
Why do these models work? Having “The Office” in common doesn’t help you when you have different personalities.
Behavior trumps interests.
The algorithm knows that shared habits create relationships; shared interests just create first dates.
Can We Actually Code Romance?We spent this entire post analyzing swipe ratios, response rates, and compatibility algorithms. But the most important data point I found wasn’t in the CSV files. It was the fact that despite the bugs, the ghosting, and the algorithmic chaos, we kept trying.
The data proves that Effort ≠ Results on dating apps. You cannot code your way into chemistry, and you cannot “optimize” a human connection. My logistic regression model can predict if you’re compatible on paper, but it can’t predict if you’ll laugh at the same jokes.
Love might be a mystery, but the data is crystal clear. So, here is your new strategy:
Ms. DataByte’s Golden Rules of Dating- Be Picky: The algorithm rewards standards. Swipe someone whom you actually would like to meet.
- Be Brief: Your bio is a movie trailer, not the full script. Keep it short. Keep it Original. Keep it all about you.
- Be Offline: The best matches happen when you are efficient enough to close the app and leave the house.
- Be Consistent: Try to engage with the other person. Don’t just text, meet, and have fun.
- Most Present: Things may go wrong, and you may get hurt. But try to be present with the process. Escaping may seem like the best way, but maybe we just need faith. “The universe has a plan, and the plan is always in motion”, Ted Mosby (HIMYM).
Stop trying to “hack” the system with pickup lines and fake personality. The only way to win the game is to play it efficiently enough that you can finally delete the app. Meet someone, go on the date, and let the chaos happen. Because the best parts of love are the outliers that no algorithm can predict.
Happy swiping (but mostly, happy living), Ms. DataByte
♦I analyzed 50,000 Dating Profiles to Decipher the Myths of Love in Algorithm was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.