Feature Engineering
If you like applying Feature Engineering, every challenge here gives you a chance to practice it on a real industry brief.
- AnalysisIntermediateNew
Customer Churn Prediction for 40-Person SaaS Scale-Up
You receive a dataset with 500 customers and 10 features (e.g., monthly logins, number of support tickets, contract length, industry). Your task is to perform exploratory analys…
- Logistic Regression
- Classification
- Feature Engineering
Econometrics - AnalysisIntermediateNew
Spectral-Analyze Wearable Sleep Data for a Healthtech Pilot
You receive 30 nights of wearable data per 25 volunteers, with polysomnography-derived ground-truth stages (Wake / NREM / REM). Engineer spectral features (delta, theta, alpha, …
- Spectral Analysis
- Feature Engineering
- Wavelet Analysis
Time Series Analysis and Forecasting - AnalysisIntermediateNew
Cluster a Telco's Subscriber Base for a Pricing Refresh
You receive 12 months of anonymized subscriber-level data: monthly minutes, SMS, mobile data, top-up frequency, top-up amount, churn flag, and tenure. Clean and feature-engineer…
- Clustering
- Feature Engineering
- Exploratory Data Analysis
Data Mining and Knowledge Discovery - CodeIntermediateNew
Predict Subscription Churn for an EdTech Platform
You receive a CSV with about 18,000 student-month rows: features include login frequency, session length, quiz scores, parent app opens, and plan tier. The target is whether the…
- Supervised Learning
- Logistic Regression
- Gradient Boosting
Machine Learning (Undergraduate) Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeIntermediateNew
Ship a Lightweight ML Microservice for an EdTech Reading App
You receive 3 months of session telemetry (around 50M reading events, child-anonymized). Engineer features per session window, train a small classifier (logistic regression base…
- Feature Engineering
- Model Serving
- Containerization
Applied Machine Learning - AnalysisIntermediateNew
Diagnose Churn Drivers for a B2B SaaS Workflow Tool
You receive three CSV exports: 18 months of weekly product-usage events for about 1,800 accounts, the full support-ticket history, and account firmographics (industry, size, pla…
- Exploratory Data Analysis
- Data Wrangling
- Feature Engineering
Applied Data Analysis and Practical Data Science - CodeIntermediateNew
Tune a Recommender for an EU Streaming Music App
Use the public Last.fm-360k or similar dataset (anonymized listening histories) as a stand-in. Implement a baseline matrix-factorization recommender, then a hybrid that adds tra…
- Recommender Systems
- Feature Engineering
- Model Evaluation
Applied Machine Learning - AnalysisIntermediateNew
Predict Equipment Failure for a Wind-Farm Operator
You receive 18 months of SCADA (Supervisory Control and Data Acquisition — the standard turbine telemetry feed) data sampled every 10 minutes from all 240 turbines, with labeled…
- Classification
- Regularized Regression
- Gradient Boosting
Statistical Machine Learning - Browse challenges
Explore role
Product Manager
Ship product that solves real user problems. Combine user research, prototyping, and stakeholder alignment to turn ambiguous briefs into measurable wins — the role at the centre of modern software teams.
- CodeIntermediateNew
Churn-Prediction Model for a B2B Vertical SaaS
Use 18 months of anonymized data (provided) covering: usage events, login frequency, support tickets, NPS responses, billing health, feature adoption, practice firmographics. De…
- Supervised Learning
- Python Programming
- Ml Applications
Machine Learning (CS Elective) - AnalysisIntermediateNew
Customer-Segmentation Study for a DTC Subscription Box
Use 18 months of anonymized data: order history, churn events, NPS responses, box-rating data, referral activity, marketing-channel attribution. Engineer features (RFM-style + b…
- Unsupervised Learning
- Python Programming
- Ml Applications
Machine Learning (CS Elective) - AnalysisIntermediateNew
Churn Prediction for a Stockholm D2C Cosmetics Brand
You are a data science consultant hired by NordicGlow. Using the provided dataset (synthetic but realistic), you must preprocess the data, engineer features from transaction, cl…
- Data Preprocessing
- Feature Engineering
- Classification
Data Science for Business - CodeIntermediateNew
Predictive Churn Model for Bangalore D2C Cosmetics
You will analyze a provided dataset of 10k customers with features like purchase frequency, average order value, time since last purchase, pages visited, support tickets, and su…
- Python
- Scikit Learn
- Logistic Regression
Machine Learning and AI for Business Build a verifiable portfolio.
Submissions become evidence. Reviewers with shipping experience score against a rubric; the result becomes a credential anyone can verify.
Why Ewance
- CodeIntermediateNew
Reduce Dimensionality on Sensor Streams for a Mid-Cap Robotics OEM
You receive 120 robot-hours of windowed sensor data (5s windows, 240 channels) with labels for normal vs. one of four fault classes. Implement (1) PCA, (2) kernel PCA with an RB…
- Dimensionality Reduction
- Kernel Methods
- Autoencoders
Machine Learning - CodeIntermediateNew
Predict Catalyst Properties for a Green-Hydrogen Pharma Spinout
Use an open catalyst dataset (e.g., Open Catalyst Project subset, or a Materials Project pull) where each candidate has descriptors and a target activity property. Train a tabul…
- Tabular Modeling
- Uncertainty Quantification
- Feature Engineering
AI for Science and Engineering - CodeIntermediateNew
Build a Credit-Card Fraud Detector for a Singapore Neobank
You receive 9 months of anonymized authorization data (around 8 million transactions, around 0.4 percent fraud) plus current rule outcomes. Split temporally and train at least t…
- Classification Modeling
- Class Imbalance
- Model Calibration
AI and Quantitative Finance - AnalysisIntermediateNew
Analyze a Learning-Analytics Dataset for At-Risk Detection
You receive an anonymized dataset of LMS engagement features (logins, assignment submissions, forum posts, video-watch time), grade history, and a binary label for end-of-semest…
- Learning Analytics
- Classification
- Fairness Metrics
AI in Education and Learning Analytics - CodeIntermediateNew
Stack Five Models for a Kaggle-Style Forecasting Bake-Off
You receive a pseudonymized dataset of 24 months of daily shipment volumes across about 200 origin-destination lanes plus weather and holiday features. Train 5 base models, use …
- Ensemble Methods
- Time Series Forecasting
- Feature Engineering
Advanced Machine Learning - AnalysisIntermediateNew
Detect Fraudulent Refund Requests for a Mid-Market Marketplace
You receive a labeled dataset with buyer history, seller history, shipping carrier, refund reason text, and outcome label (legit / fraud). Train and evaluate at least two classi…
- Classification
- Model Calibration
- Imbalanced Classification
Machine Learning (Undergraduate) - CodeIntermediateNew
Ship a Churn-Prediction Mini-Project End to End
You receive a 12-month anonymized dataset of subscriber events (logins, lesson completions, payment history, support tickets) for around 200,000 users. Define churn precisely (n…
- Feature Engineering
- Model Evaluation
- Gradient Boosting
AI/ML Practicum and Hands-on Lab - AnalysisIntermediateNew
Cluster a Mid-Market SaaS Customer Base for Account-Tier Re-segmentation
Pull 12 months of usage signals from the warehouse: feature adoption depth, session frequency, integration counts, ticket volume, NPS (Net Promoter Score), seat utilization. Sta…
- Clustering
- K Means
- Hdbscan
Data Mining and Information Retrieval
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
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Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.