Feature engineering
If you like applying Feature engineering, every challenge here gives you a chance to practice it on a real industry brief.
- 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 - 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 - 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 - 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 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
- 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
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
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 - CodeBeginnerNew
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 Or Javascript
- Scikit Learn
- Logistic Regression
Machine Learning and AI for Business - Browse challenges
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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
Detect Coordinated Fraud Rings via Link Analysis at a Neobank
You receive 90 days of account, login, and transaction data (around 1.2 million accounts, around 30 million events) plus a labeled set of 80 known fraud rings. Build a multi-rel…
- Graph Analysis
- Community Detection
- Link Analysis
Data Mining and Knowledge Discovery - 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 - AnalysisBeginnerNew
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 - 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) 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
- 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 - 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 - 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 - 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 - 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 - CodeBeginnerNew
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 - 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 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 - CodeBeginnerNew
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 - 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 - 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
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
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.
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