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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...
Optimize Pandas data processing pipeline. Techniques: 1. Vectorize operations (avoid loops). 2. Use appropriate data types (int8, category). 3. Process large datasets with chunking. 4. Parallelize processing with Dask or Swifter. 5. Efficient file formats (Parquet/Feather). 6. Memory usage profiling...
Build an interactive real-time analytics dashboard. Tech stack: 1. Use Plotly Dash for the web framework. 2. Implement WebSocket connections for live data streaming. 3. Create responsive charts (time series, heatmaps, scatter plots). 4. Add filtering and date range selectors. 5. Implement data aggre...
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...
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...
