Public service announcement 📢
My NHANES ML project is finally complete! 😌
I built a Random Forest model to predict multiple cardiometabolic outcomes:
F1 score: 0.76–0.91
PR AUC: 0.75–0.93
Not perfect, but it works 💅🏽
📂 Full project: [https://t.co/sxEEoNzWpS]
I’m starting a new ML project! 🧵
I’ll be building a system that predicts obesity-related conditions in children and adults using the NHANES dataset.
Conditions like; Obesity, Pre-diabetes, Metabolic syndrome and Cardiovascular disease..and possibly more as the project evolves.
Update: My cardiometabolic risk ML project is now LIVE 🚀
A web app predicting 5 risks: Diabetes, Prediabetes, Dyslipidemia, Metabolic Syndrome & CVD.
Try it 👇
https://t.co/6bDwkV7ArE
(First load ~20–60s)
#HealthTech#AI#DataScience
Trust it's not too late to share my 2025 review.
2025 was a lot!
That's the easiest way to put it, but one thing I'm grateful for is that I ended the year spotting the mindset shifts I needed
Full review: https://t.co/vLXNPTjWCS
Public service announcement 📢
I’ve finished the most part of my project 🤭
My model is ready 😌
Using the F1 and PR score, the performance ranges between 0.75 - 0.93
It’s not a perfect score, but it’ll do for now 💅🏽
By the way, the selected model is RF2 - Random Forest
After trying out different models, the best performing model so far is RandomForestClassifier but I’m still not satisfied with its overall performance so I still have to improve the performance.
@Ola_yinka224@Fimiii06 I told my friend this last week.
The air has gotten dry, but because of how hot the weather is, it’s very subtle
I’ve been drinking water like a fish for over a week now😭😭😭
I wanna deploy it to an app, but I haven’t learnt model deployment yet, so when I do that, I’ll let y’all know 🤭😌
Oh, I’ll share my GitHub link when I publish it on my GitHub.
Thank you guys 😊
Working with @UNICEF is actually my dream job 🙂↔️
Those of you who know me, knows how much I love children and how it’s my life’s mission to give them better chances in life 🫠
God willing, I will live my life to make an impact on the lives of millions of children 🤲🏽
After trying out different models, the best performing model so far is RandomForestClassifier but I’m still not satisfied with its overall performance so I still have to improve the performance.
Been a minute guys
A lot has been going on.
But the new update is that after creating the model, it had 100% accuracy and I realized it’s because I used the same features in creating the target variables to train the model.
So I had to drop them and retrain the model without them
Been a minute guys
A lot has been going on.
But the new update is that after creating the model, it had 100% accuracy and I realized it’s because I used the same features in creating the target variables to train the model.
So I had to drop them and retrain the model without them
Update 🙂↔️
I’m done with EDA🥲
Did a few digging around to understand the trend and distribution in the data.
The features are skewed, there are outliers and class imbalance.
But, I’m going to be using tree models for it, and they’re not as sensitive to skew mess & outliers
Update 🙂↔️
I’m done with EDA🥲
Did a few digging around to understand the trend and distribution in the data.
The features are skewed, there are outliers and class imbalance.
But, I’m going to be using tree models for it, and they’re not as sensitive to skew mess & outliers
Update guys.
I’m done with the data gathering, cleaning and preparation.
I think I ended up downloading about 8 datasets per cycle
Insulin, plasma glucose and HbA1c has also added.
I need the model to be generalized, even in extreme medical conditions so I decided to leave them as they are.
The data is already encoded, so there’s no need for that.
Up next, Model Development 🙂↔️