Scikit-Learn Projects
End-to-end ML projects that take you from raw data to deployed models — covering classification, regression, clustering, and production pipelines.
Beginner Projects
1. Titanic Survival Predictor
Classic binary classification: preprocess mixed-type features (age imputation, one-hot encoding for embarked/sex), train multiple classifiers, compare with cross-validation, and analyze which passenger groups the model gets wrong.
What you’ll practice: ColumnTransformer, Pipeline, cross_val_score, confusion matrix
2. House Price Estimator
Predict house prices from the Ames Housing dataset. Handle missing values, encode categoricals, apply log-transform to the target, train ElasticNet and RandomForest, compare MAE/RMSE.
What you’ll practice: Imputation strategies, feature encoding, log transform, regression metrics
3. Iris Flower Classifier
Multi-class classification with decision boundary visualization. Train k-NN, SVM, and decision tree. Plot decision boundaries in 2D (PCA-reduced). Visualize misclassifications.
What you’ll practice: Multi-class evaluation, PCA visualization, model comparison
4. Customer Churn Predictor
Binary classification on telecom churn data. Address class imbalance with class_weight=‘balanced’. Select features with SelectKBest. Build a deployment-ready Pipeline.
What you’ll practice: Imbalanced classification, feature selection, pipeline serialization
5. Diabetes Risk Classifier
Classify diabetes risk from clinical features. Apply StandardScaler, compare Logistic Regression vs. Decision Tree vs. Random Forest. Plot feature importances and ROC curves.
What you’ll practice: Medical data handling, class-weight adjustment, ROC/AUC
6. Movie Rating Predictor
Predict movie ratings from features (genre, runtime, release year, director). Build a regression pipeline, compute residuals, and plot error distributions to find systematic biases.
What you’ll practice: Multi-type feature encoding, regression diagnostics, residual analysis
7. Text Document Classifier
Classify news articles into 4 categories (20 Newsgroups subset). Build a TF-IDF + LogisticRegression pipeline. Show the top features per class. Compute per-class F1 scores.
What you’ll practice: TfidfVectorizer, text classification pipeline, per-class evaluation
8. Credit Card Fraud Detector
Highly imbalanced binary classification (0.17% fraud). Compare threshold moving, class weights, and SMOTE oversampling. Evaluate with precision-recall curves, not accuracy.
What you’ll practice: Severe class imbalance, threshold tuning, PR-AUC
9. K-Means Customer Segmentation
Cluster e-commerce customers by RFM features. Determine optimal K using elbow method and silhouette scores. Profile each cluster and name the segments.
What you’ll practice: K-Means, cluster evaluation, business interpretation
10. Spam Email Classifier
Build a spam filter using CountVectorizer + Naive Bayes. Compare with TF-IDF + SVM. Analyze false positives (ham classified as spam) — understand the cost asymmetry.
What you’ll practice: Text preprocessing, Naive Bayes, cost-sensitive evaluation
Intermediate Projects
1. End-to-End Loan Default Predictor
Full production pipeline: impute mixed types, encode categoricals, engineer interaction features, tune with RandomizedSearchCV, calibrate probabilities, and export for deployment. Include SHAP feature importance.
What you’ll practice: Full pipeline, probability calibration, SHAP, model export
2. Real Estate Price Model with Feature Store
Build a feature engineering layer that computes 30+ features from raw property data, feeds them into a tuned GradientBoosting model, and logs everything to MLflow. Re-train pipeline on new data without code changes.
What you’ll practice: Feature engineering pipeline, MLflow tracking, retrainable design
3. Multi-Label Document Classifier
Classify research papers into multiple simultaneous categories. Compare one-vs-rest and label powerset strategies. Handle class imbalance per label. Report macro/micro F1.
What you’ll practice: MultiLabelBinarizer, OneVsRestClassifier, multi-label metrics
4. Time-Series Demand Forecaster
Convert time series to supervised learning: lag features, rolling stats, calendar features. Use TimeSeriesSplit for proper CV. Compare linear, tree, and boosting models.
What you’ll practice: Feature extraction from time series, temporal CV, regression evaluation
5. Anomaly Detection System
Build a multi-method anomaly detector (IsolationForest, LOF, OneClassSVM) on server log data. Compare detection rates, false positive rates, and explain which method to use when.
What you’ll practice: Unsupervised anomaly detection, evaluation without labels, threshold tuning
6. Customer Lifetime Value Predictor
Predict 12-month LTV from behavioral features. Use quantile regression (GradientBoostingRegressor with quantile loss) to output prediction intervals, not just point estimates.
What you’ll practice: Quantile regression, prediction intervals, business value framing
7. Recommendation Engine (Content-Based)
Build a content-based movie recommender using TF-IDF on plot descriptions + cosine similarity. Also implement collaborative filtering via matrix factorization with TruncatedSVD.
What you’ll practice: TfidfVectorizer, cosine_similarity, TruncatedSVD, user-item matrices
8. Hyperparameter Optimization Study
Compare GridSearchCV, RandomizedSearchCV, and Optuna on the same model+dataset. Track wall clock time, best score, and n_trials required. Visualize the search landscape.
What you’ll practice: Search strategies, Optuna integration, hyperparameter importance
9. Feature Selection Comparison
Compare 6 feature selection methods (variance threshold, chi2, mutual information, L1, RFE, RFECV) on a noisy dataset with 100 features, 20 informative. Measure which methods recover the true features.
What you’ll practice: All sklearn feature selection APIs, evaluation of selection quality
10. Semi-Supervised Learning
Use LabelSpreading or LabelPropagation on a dataset where only 5% of labels are available. Compare to supervised learning with the same 5% labeled set and to using all labels.
What you’ll practice: Semi-supervised learning, graph-based methods, label propagation
Advanced Projects
1. AutoML Pipeline Builder
Build a meta-learning pipeline that tries 10 model families + preprocessing combinations, selects the best via nested CV, and outputs a fitted, serialized pipeline with evaluation report — automating the typical model selection process.
What you’ll practice: Nested CV, pipeline composition, joblib serialization, reporting
2. Stacking Ensemble Framework
Build a general stacking framework: N base models generate OOF predictions for a meta-learner. Support both regression and classification. Compare stacking to the best individual model.
What you’ll practice: Cross_val_predict OOF, meta-learner design, stacking vs. bagging vs. boosting
3. Concept Drift Detector
Build a streaming learning system that detects distribution drift using the KS test and PSI. When drift is detected, retrain the model on recent data only. Simulate drift by splitting a dataset.
What you’ll practice: Drift detection, online/incremental learning, sklearn partial_fit
4. Production ML Audit Tool
Build a tool that takes any fitted sklearn Pipeline and a new dataset, then reports: data drift per feature, prediction distribution shift, calibration curve, and feature importance stability vs. training.
What you’ll practice: Pipeline introspection, statistical testing, calibration_curve, reporting
5. Custom Estimator Library
Build 3 custom sklearn-compatible estimators using BaseEstimator + TransformerMixin/RegressorMixin: a target encoder with smoothing and CV, a date feature extractor, and a quantile winsorizer. Write pytest tests for each.
What you’ll practice: Estimator protocol, fit/transform pattern, testing custom estimators
Portfolio Projects
1. End-to-End Insurance Claims Predictor
Full lifecycle project: business problem framing, EDA, feature engineering pipeline, XGBoost with Optuna tuning, SHAP explanation, probability calibration, FastAPI serving endpoint, and monitoring hooks. Documented for a non-technical stakeholder.
Tech stack: sklearn, XGBoost, Optuna, SHAP, FastAPI
Demonstrates: End-to-end ML, explainability, production readiness
2. Medical Diagnosis Assistant
Multi-class classification on clinical notes + structured lab data. Build a multimodal feature extractor (TF-IDF for text + StandardScaler for numeric), handle severe class imbalance, calibrate probabilities, and generate per-patient risk reports.
Tech stack: sklearn, imbalanced-learn, SHAP, matplotlib
Demonstrates: Healthcare domain, calibration, explainability
3. Real-Time Fraud Detection Service
Train an XGBoost fraud model with streaming feature engineering (velocity, location anomalies). Wrap in FastAPI with sub-50ms latency. Implement a shadow mode for safe model swaps. Track feature drift over time.
Tech stack: sklearn, XGBoost, FastAPI, Redis for velocity features
Demonstrates: Low-latency serving, streaming features, production operations
4. Benchmark Suite for Tabular ML
Systematically benchmark 15 algorithms (from Logistic Regression to TabNet) across 20 public datasets. Report Friedman rank, critical difference diagram, and conditions under which each algorithm excels.
Tech stack: sklearn, XGBoost, LightGBM, CatBoost, PyTorch (TabNet), scipy
Demonstrates: Rigorous experimental methodology, meta-learning knowledge
5. Automated Feature Engineering Platform
Build a system that generates 100+ candidate features from raw tabular data (polynomial, interactions, aggregations, target-encoded), selects the best subset using RFECV, and tracks feature lineage for auditability.
Tech stack: sklearn, featuretools or custom, MLflow
Demonstrates: Feature engineering depth, pipeline design, ML engineering maturity