#machine-learning Prompts

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. Impl...

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 au...

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. Featu...

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 interval...

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 learnin...

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...

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...