#machine-learning

Build production churn prediction system. Pipeline: 1. Perform exploratory data analysis and visualization. 2. Engineer features (RFM, engagement scores, usage patterns). 3. Handle class imbalance with SMOTE or class weights. 4. Train multiple models (XGBoost, Random Forest, Neural Network). 5. Implement cross-validation and hyperparameter tuning. 6. Create SHAP values for model interpretability. 7. Build prediction API with FastAPI. 8. Set up monitoring for model drift. Include feature importance analysis and business impact metrics.

Build enterprise-grade LLM fine-tuning system. Pipeline: 1. Implement data preprocessing and quality validation. 2. Set up LoRA (Low-Rank Adaptation) for efficient training. 3. Configure distributed training across multiple GPUs. 4. Implement gradient checkpointing for memory optimization. 5. Add automated evaluation with ROUGE, BLEU, and custom metrics. 6. Create A/B testing framework for model comparison. 7. Set up MLflow for experiment tracking. 8. Implement model versioning and deployment pipeline. Include cost monitoring and training time optimization.

Create advanced features for a churn prediction model. Techniques: 1. Temporal features (days since last purchase, purchase frequency). 2. Aggregations (total spend, average order value). 3. Categorical encoding (one-hot, target encoding). 4. Interaction features (tenure × monthly charges). 5. Feature selection using mutual information and correlation analysis. Document feature importance and business rationale for each engineered feature.

Build a time series forecasting model using Facebook Prophet. Steps: 1. Prepare historical sales data with daily granularity. 2. Add custom seasonality for Black Friday and holiday peaks. 3. Include external regressors (marketing spend, weather). 4. Generate 90-day forecast with uncertainty intervals. 5. Validate model using cross-validation and MAPE metric. Visualize actual vs predicted with interactive Plotly charts.

Master systematic model selection and optimization for machine learning projects with performance evaluation frameworks. Model selection process: 1. Problem definition: classification vs. regression, supervised vs. unsupervised learning. 2. Data assessment: sample size (minimum 1000 for deep learning), feature count, missing values analysis. 3. Baseline models: linear regression, logistic regression, random forest for initial benchmarks. Algorithm comparison: 1. Tree-based: Random Forest (high interpretability), XGBoost (competition winner), LightGBM (fast training). 2. Linear models: Ridge/Lasso (regularization), ElasticNet (feature selection), SGD (large datasets). 3. Neural networks: MLPs (tabular data), CNNs (images), RNNs/Transformers (sequences). Hyperparameter optimization: 1. Grid search: exhaustive parameter combinations, computationally expensive but thorough. 2. Random search: efficient for high-dimensional spaces, 60% less computation time. 3. Bayesian optimization: intelligent search using Gaussian processes, tools like Optuna, Hyperopt. Cross-validation strategies: 1. K-fold CV: k=5 for small datasets, k=10 for larger datasets, stratified for imbalanced data. 2. Time series CV: walk-forward validation, expanding window, respect temporal order. Performance metrics: accuracy (>85% target), precision/recall (F1 >0.8), AUC-ROC (>0.9 excellent), confusion matrix analysis for class-specific performance.

Monitor fine-tuning of Low-Rank Adaptation models. UI elements: 1. Real-time loss graph. 2. Epoch/Step counters. 3. Predicted remaining time. 4. Samples generated mid-training (checkpoints). 5. Hardware metrics: VRAM usage, GPU Temp. Use a dark, developer-focused aesthetic with neon accents.

Leverage big data for research insights using appropriate methods. Data characteristics: 1. Volume: large datasets requiring distributed computing. 2. Velocity: real-time or near real-time data streams. 3. Variety: structured and unstructured data from multiple sources. 4. Veracity: data quality and reliability concerns. Analytics approaches: 1. Machine learning: supervised (prediction) vs. unsupervised (pattern discovery). 2. Natural language processing: sentiment analysis, topic modeling, named entity recognition. 3. Network analysis: social networks, collaboration patterns, information flow. 4. Time series analysis: trend detection, forecasting, anomaly detection. Tools and platforms: 1. R/Python for analysis, Spark for distributed computing. 2. Cloud platforms: AWS, Google Cloud, Azure for scalable processing. 3. Visualization: Tableau, D3.js for interactive dashboards. Validation: 1. Cross-validation for machine learning models. 2. Triangulation with traditional data sources. 3. Replication across independent datasets. Ethical considerations: consent for secondary use, privacy protection, algorithmic bias.

Build an anomaly detection system for transaction fraud. Approach: 1. Use Isolation Forest for unsupervised outlier detection. 2. Engineer features (transaction amount, time of day, location distance). 3. Set contamination parameter based on historical fraud rate. 4. Generate anomaly scores and flag top 1% as suspicious. 5. Create alerting system with precision/recall monitoring. Visualize anomalies on a scatter plot with decision boundary. Balance false positives vs fraud detection.