Feature engineering
If you like applying Feature engineering, every challenge here gives you a chance to practice it on a real industry brief.
- CodeIntermediateNew
Forecast Energy Demand for a Nordic Renewable Utility
You receive 5 years of hourly residential-segment demand, hourly weather data (temperature, wind, irradiance), and a calendar of public holidays. Build a probabilistic forecaste…
- Time Series Forecasting
- Probabilistic Modeling
- Feature Engineering
Applied Machine Learning - AnalysisBeginnerNew
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 - AnalysisBeginnerNew
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 Or Javascript
- Ml Applications
Machine Learning (CS Elective) - CodeBeginnerNew
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 Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- AnalysisIntermediateNew
Structured Prediction for Insurance Claim Triage
You receive 18,000 historical claims with text, attachments-count, claim amount, customer tenure, and the ground-truth final routing bucket. Train a structured classifier (e.g.,…
- Structured Prediction
- Multi Class Classification
- Model Evaluation
Advanced Machine Learning - CodeBeginnerNew
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) - CodeIntermediateNew
Build a Real-Time Fraud-Detection Stream for a Card Issuer
Design the stream topology: authorization events in, customer-feature state (30-day rolling) maintained in state store, scoring function applied per event, fraud-score emitted t…
- Apache Flink
- Kafka Streams
- Stream Processing
Event-Driven Architecture - CodeIntermediateNew
Forecast Intraday FX Volatility for a London Liquidity Desk
You receive 18 months of tick-level mid-quote data for six FX pairs plus a calendar of scheduled macro events. Resample to 1-minute bars, engineer realized-volatility features, …
- Time Series Forecasting
- Feature Engineering
- Model Validation
AI and Quantitative Finance - 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.
- AnalysisIntermediateNew
Capstone Lab: Diagnose Why a Production Model Quietly Stopped Working
You receive 6 months of production logs (model inputs, predictions, ground truth from chargebacks) plus the original training data and model card. Reproduce the recall drop in a…
- Data Drift Detection
- Model Monitoring
- 5 Whys & Fishbone Root Cause Analysis
AI/ML Practicum and Hands-on Lab - AnalysisBeginnerNew
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
Build a Feature Store Backbone for a Healthtech ML Team
You receive synthetic wearable telemetry (heart rate, accelerometer, sleep stages) for around 5,000 patients across 90 days, plus the existing scattered feature scripts from the…
- Feature Engineering
- Data Modeling
- Python Or Javascript
Data Engineering and Big Data Systems - AnalysisBeginnerNew
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 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
- CodeBeginnerNew
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 - AnalysisBeginnerNew
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 - CodeBeginnerNew
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 - DesignIntermediateNew
Stand Up a Feature Store for a Series-B Fintech
Pick one priority feature group (recommend the 25 transaction-history features used by the fraud model). Define the offline source-of-truth (likely Snowflake or BigQuery), the o…
- Feature Store
- Feature Engineering
- Airflow Dags
ML Engineering and Production ML - CodeIntermediateNew
Build a Streaming Pipeline for Real-Time Fraud Detection
Receive 30 days of anonymized card-transaction events (around 240M events total), the team's existing batch features (cardholder behavior summaries), and a pre-trained fraud-sco…
- Stream Processing
- Kafka Event Streaming
- Flink
Big Data and Data-Intensive Systems - CodeBeginnerNew
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 - CodeBeginnerNew
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 Or Javascript
- Ml Applications
Machine Learning (CS Elective) - AnalysisBeginnerNew
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
Predict Loan Default Risk for a Cross-Border Fintech
You receive 18 months of transactions (around 12M rows) and seller-firmographic data. Define a defensible proxy label for default (e.g., a 60-day chargeback-or-dispute spike com…
- Feature Engineering
- Model Selection
- Model Evaluation
Applied Machine Learning - CodeIntermediateNew
Build an End-to-End ML Pipeline for Loan-Default Prediction
You receive 24 months of historical application + outcome data (about 380,000 rows). Build a pipeline using a workflow orchestrator (Prefect, Kedro, or a simple Makefile chain) …
- Ml Pipelines
- Feature Engineering
- Pipeline Testing
Machine Learning in Practice - AnalysisBeginnerNew
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
Forecasting Churn for a SaaS Scale-Up
You are a data scientist intern at TaskFlow. Using the provided dataset, perform feature engineering and build a logistic regression or decision tree model to predict churn. Ide…
- Data Analysis
- Regression
- Classification
Data Analytics for Business
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
Hiring from this pool?
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.