The frustrating part? After all that logical feature engineering, my XGBoost ROC-AUC score is still stuck at 0.65. π Iβm heading back to the EDA and visualization phase to hunt for deeper patterns.
Data Scientists: where do you look when feature engineering doesn't work? ππ
Back on my ML project, LendIQ, I caught a major real-world flaw: absolute currency values. The dataset is in USD, but my local currency is Naira. A 500k loan means wildly different things. To make the model currency-agnostic, I engineered Loan-to-Income ratios instead. ππ΅
Took on LeetCode 2415 (Reverse Odd Levels of Binary Tree) using BFS. Worried my nested loops were O(n^2), I put on my CS student hat and calculated the exact running time function. Fun realization: Python's deque is a doubly linked list, so index lookups aren't true O(1)! π³β±οΈ
Now for the final boss: the ML tier. My XGBoost model is currently performing slightly better than a coin toss πͺ. The culprit? A highly imbalanced dataset tanking the ROC-AUC score.
ML Engineers (and @AnthropicAI folks!)βwhatβs your go-to strategy for imbalanced data? ππ
Been a heavy coding week! π
First up: DSA. Tackled "Kth Smallest Element in a BST". The concept was easy, but translating it to code and managing the call stack took serious troubleshooting. Finally seeing that green "Accepted" was worth the grind. π³π»
Next, full-stack work on LendIQ (my loan prediction app). Built the UI and wired it to the backend. Because I planned the architecture earlier, tweaking the backend on the fly was stress-free. There is nothing like seeing a full system click together! βοΈπ§
But the data is tough. I'm dealing with a highly imbalanced dataset and my ROC-AUC is currently sitting at 0.654. I want to push it to at least 0.75 before deployment.
Data scientists & ML engineers: What are your go-to strategies for improving ROC-AUC on stubborn models?
Jumped back into Lendiq, my 3-tier loan prediction app. Since it's my first time using Prisma & MongoDB, I felt completely lost opening the codebase. What saved me? A step-by-step "Design Game Plan" I documented earlier.
Lesson: Document your architecture. Itβs a lifesaver! π
Shifted to the ML tier to optimize my XGBoost model. To understand its decisions, I dove into SHAP. It took some workarounds, bypassing pipeline limits for scaled data & converting log odds to probabilities but finally seeing the feature impacts was worth it.
A model needs a home! Putting my full-stack skills to work by spinning up MongoDB, navigating the Prisma ORM, and learning FastAPI to serve the model.
Also teaming up with @OritsesangbemiM and @davidchair3025 competition project. More updates coming, good to be back!
Back from a posting hiatus! π Academics were heavy this semester, but the learning didn't stop, it just went under the radar. Letβs do a quick sprint through what I've been studying and building behind the scenes.
Deep into Python ML. I'm building a Loan Default Prediction app and hit a wall: heavily imbalanced data. A model could approve everyone and hit 90% accuracy! It forced me to ditch basic accuracy and rely entirely on Precision, Recall, and ROC-AUC scores.
The academic grind was real. Algorithms was a favorite, Assembly tested my patience, but Software Engineering completely rewired how I view the dev lifecycle. Also, connecting classroom Bayes' Theorem directly to the Naive Bayes algorithm in ML was a massive "aha" moment.
I just shipped the tournament and in-game chatting feature. π₯Ήπ₯Ή
What do you think about this?
What feature would you like to see?
Here's the link - https://t.co/6jQ56DarPH
Didn't get to code as much as I wanted, but still squeezed in some solid learning:
β Express.js: Writing to MongoDB
β ML: XGBoost pipelines & hyperparameter tuning β Java: String methods (still fighting local vs instance variables tbh π )
WE MADE IT! 90 unbroken days of code.
Started this challenge over the holiday to keep my motivation up (huge shoutout to my coursemate @DionDevs for the idea). Here is a look back at everything I built, broke, and learned over the last 3 months! π§΅π
πΌ I am currently open to remote internships where I can bring my Full-Stack/ML skills to a team while balancing my degree.
Thank you to everyone who followed the journey! ππ₯