预测分析 Jobs

Our company has amassed a rich set of customer data from CRM, support tickets, and usage logs, and I need a full prescriptive analysis that pinpoints actions we can take to boost retention. The work goes beyond describing what has happened; I need to understand why customers leave, simulate what-if scenarios, and receive clear, prioritized recommendations I can implement right away. You will have direct access to anonymized customer records, churn labels, engagement metrics, and marketing touchpoints. I expect you to apply statistical modeling or machine-learning techniques of your choice, validate findings rigorously, and convert them into concrete retention strategies—loyalty offers, upsell timing, personalized messaging, or workflow changes—complete with expected impact. D...

Our company has amassed a rich set of customer data from CRM, support tickets, and usage logs, and I need a full prescriptive analysis that pinpoints actions we can take to boost retention. The work goes beyond describing what has happened; I need to understand why customers leave, simulate what-if scenarios, and receive clear, prioritized recommendations I can implement right away. You will have direct access to anonymized customer records, churn labels, engagement metrics, and marketing touchpoints. I expect you to apply statistical modeling or machine-learning techniques of your choice, validate findings rigorously, and convert them into concrete retention strategies—loyalty offers, upsell timing, personalized messaging, or workflow changes—complete with expected impact. D...

I have a collection of patient medical records already cleaned and stored as CSV files, and I’m ready to turn them into an accurate diabetes-risk prediction tool. My preference is to work with a Random Forest model because of its balance between interpretability and performance on tabular health data. Your task is to take these CSV datasets, craft the full machine-learning pipeline, and return a model that can reliably identify individuals at elevated risk for diabetes. Along the way, please document the preprocessing steps, feature engineering choices, and the metrics you use so I can reproduce and fine-tune the work later if needed. Deliverables I expect: • Complete, well-commented Python code (preferably in a Jupyter notebook) that ingests the CSV files, performs prep...

I need a digital twin that zeroes-in on my building’s energy performance. Right now I only have partial data—BMS exports, sub-metering on a handful of panels, and monthly utility bills—so the model must be able to start with what’s available, validate any gaps, then scale as more sensors come online. Primary improvement targets: • Heating and cooling systems • Overall energy consumption The twin should connect to the existing BIM (Revit) files, ingest the live and historical data, and run calibrated simulations—EnergyPlus or an equivalent physics-based engine is fine—so I can visualise real-time KPIs and generate “what-if” scenarios for retrofit ideas. I’m interested in clear, actionable outputs such as predicted savings...