Analitică predictivă Jobs

I will supply our historical sales data in CSV format, already cleaned of obvious entry errors but otherwise untouched. Your task is to run a full predictive analysis aimed at understanding and anticipating customer behavior—specifically what, when, and how likely each shopper is to purchase again. Here is what I expect: • A reproducible model built in Python (pandas / scikit-learn) or R that predicts the next purchase probability and likely product category for each customer. • Clear feature importance or explanatory output so I know which factors are driving the predictions. • A concise slide deck or report that translates the results into plain-language insights and recommended actions for marketing and inventory teams. • All code, notebooks, and any i...

I have a structured customer-level dataset and the central aim is to turn it into a reliable predictor of future purchase behavior. The work revolves entirely around outcome prediction, not trend-spotting or process optimization, so every step—from cleaning the raw customer records to validating the finished model—should directly support that goal. Here is the flow I envision: • Data preparation: handle missing values, engineer features that truly influence buying decisions, and document every transformation in clear, reproducible code (Python, R, or SQL—whichever you prefer). • Model development: test several supervised learning techniques, benchmark them with appropriate metrics (AUC, precision-recall, or another mutually agreed KPI), and converge on the be...

$5-$10. Max for this expert information: EXPERT REQUIRED: POLYMARKET HISTORICAL DATA ARCHITECTURE FOR QUANTCONNECT/LEAN I need an expert to solve one specific problem in my existing Polymarket quantitative trading system. With traditional futures trading, I can obtain large amounts of continuous historical data for the same underlying asset and use QuantConnect/LEAN to perform discovery, backtesting, walk-forward testing, out-of-sample validation, overfitting analysis, parameter optimization, and strategy validation. Polymarket is fundamentally different because it consists of thousands of individual prediction markets that open, evolve, and resolve at different times. My concern is that my current LEAN bridge does not have enough historical Polymarket data in a format that allows it ...

I’m building a technology-driven initiative and need a seasoned data analyst to turn raw information into clear, actionable insight. The scope centers on three core services: • Data visualization – translate complex datasets into intuitive dashboards or charts so trends are instantly understandable. • Statistical analysis – run rigorous tests and descriptive stats to validate findings and quantify relationships. • Predictive modeling – create models that forecast key outcomes and recommend next steps. You’ll receive access to the datasets, along with context on our business objectives and any relevant KPIs. I’m open to your choice of platforms or languages—whether that’s Python (Pandas, Sci-Kit-Learn), R, Tableau, or anot...

I’m ready to move from concept to production with an AI-driven system that combines natural language processing, predictive analytics and a set of custom-built models. The high-level goal is to ingest varied data sources, understand text at scale, uncover actionable patterns and expose the results through clean, well-documented APIs that my in-house developers can extend. Here’s what I need from you: • Model design and training – choose the right architecture (transformer, LSTM, hybrid, etc.) and justify it in a brief technical note. • End-to-end NLP pipeline – data cleaning, tokenisation, entity extraction and sentiment or intent classification. • Predictive layer – supervised or unsupervised techniques that surface trends and generate fo...

I need an experienced analyst to turn my historical sales data into a forward-looking forecast entirely in Excel. Your task is to clean the raw figures, build an accurate predictive model, and present the results in a format that a non-technical stakeholder can understand at a glance. The dataset arrives as monthly CSV exports. After cleaning, I expect you to apply appropriate time-series or regression techniques available within Excel—think Data Analysis ToolPak, Power Query, Solver, or even VBA if it streamlines the workflow. Please document every transformation step so the model can be refreshed with new data later. Deliverables • A well-structured Excel workbook containing: – The cleaned data sheet – The predictive model with formulas fully visible &n...