Analítica Predictiva Jobs

I need to embed two core AI capabilities into my current workflow: a predictive-analytics engine driven by machine-learning techniques and an image-recognition module powered by computer vision. Both tools should be production-ready, well-documented, and callable through REST or Python APIs so they slot cleanly into our existing stack. The predictive component will ingest historical data that I will provide (CSV and SQL sources) and return forward-looking metrics such as demand forecasts and risk scores. Accuracy must be benchmarked against a held-out test set, with clear reporting on feature importance and model performance (precision, recall, F1). The image-recognition piece will classify and tag uploaded photos, surfacing confidence values for each label. A lightweight front-end demo ...

The use of data and machine learning has become increasingly common in professional sports, especially in the NBA. Teams now collect and analyse large amounts of historical data related to games, players, and overall performance in order to gain a competitive advantage. As the data is well structured and widely available, the NBA has become a popular area for sports analytics and predictive modelling. Most existing basketball analysis focuses on team statistics and individual player performance, such as scoring, shooting efficiency, and defensive contributions. While these metrics are useful, basketball is a team sport, and success often depends on how well players perform together on the court. This is team chemistry, but it is difficult to measure using traditional statistics.

Project Brief – Customer Churn Prediction for Telecom Project Goal: The project aims to predict customer churn in the telecommunications industry. By identifying customers likely to leave, telecom companies can take proactive retention actions, reduce revenue loss, and improve customer satisfaction. Data Used: The dataset includes customer demographics, service subscriptions, contract details, billing information, and payment methods. Project Steps: Data Cleaning: Handling missing values and correcting inconsistencies. Exploratory Data Analysis (EDA): Understanding patterns and key factors affecting churn. Feature Engineering: Creating new variables to improve model performance. Model Building: Developing a machine learning classification model to predict churn. Technologies & To...

Project Brief – Customer Churn Prediction for Telecom Project Goal: The project aims to predict customer churn in the telecommunications industry. By identifying customers likely to leave, telecom companies can take proactive retention actions, reduce revenue loss, and improve customer satisfaction. Data Used: The dataset includes customer demographics, service subscriptions, contract details, billing information, and payment methods. Project Steps: Data Cleaning: Handling missing values and correcting inconsistencies. Exploratory Data Analysis (EDA): Understanding patterns and key factors affecting churn. Feature Engineering: Creating new variables to improve model performance. Model Building: Developing a machine learning classification model to predict churn. Technologies & To...

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I already have an HRIS web application live, but several core pieces still need to be taken from “mostly there” to production-ready. I’m looking for someone who can dive straight into the existing codebase, clean up what’s built, and finish the remaining functionality fast. Here are the areas that must be completed: • Employee onboarding – end-to-end workflow, document upload, e-signature integration • Payroll management – calculations, statutory deductions, payslip generation and export • Performance evaluation – goal-setting, 360° feedback, appraisal reporting • Absence management & time-off tracking – requests, approvals, accrual rules • Training & development – course catalogue, enrolme...

I need an expert to analyze daily rainfall data for my farm and build a predictive model. The analysis should cover: - Trend analysis - Anomaly detection - Seasonal patterns assessment The goal is to develop a robust rainfall prediction model. Ideal skills and experience include: - Proficiency in data analysis and statistics - Experience with predictive modeling - Knowledge of agricultural impacts of rainfall patterns - Familiarity with relevant software and tools (e.g., Python, R, etc.) The data is very erratic so I dont need a detailed monthly prediction model although it would be nice if possible, otherwise a yearly prediction model or atleast something that says a dry season is coming or a wet season is coming. The model must include macro environmental factors such as El Nino (ONI...

I am working on a concept of mainly "Preventive Maintenance using Sensors" For this I will Import sensors from China; Install at factories and Charge monitoring subscription in Maharashtra. I want guidance on below -- Which sensors to buy from these ones (dont want hypothetical answers like alibaba and some listed companies over it - that ChatGPT also gives: vibration sensors; temperature sensors; motor monitoring devices. Need exact compaby and specification and to which cloud platform as well as dashboard it would be fit for AI monitoring remotely. No ChatGPT answer please - I want someone who have actually worked on it or knows end-to-end. Next: Sensors + Edge device + AI software - which one to use and how? Wireless Vibration Sensors; Motor Condition Monitoring Advanced Se...

I have a spreadsheet that tracks container-throughput from the Port to the ICD over time. I need a clean, reader-friendly report that tells the whole story hidden in those numbers. Scope of work First, lay out a solid descriptive analysis that covers total throughput and the day-to-day movement patterns. Next, build a predictive model so I can see how volumes may evolve in the near future. Finally, compare key periods and parameters to uncover how performance differs across time frames or operational variables. The descriptive section must highlight: • Total throughput • Daily trends • Trends specifically linked to Containers Received and any relationships you uncover between variables Deliverables • A written report (Word or PDF) summarising findings from the ...

I’m building a complete AI-driven platform that trades listed options for me end-to-end. The engine has to recognise and act on the full range of strategies I use—straight buying or selling of calls and puts, multi-leg spreads, straddles, strangles, and any automatic strategy the model itself flags as favourable. Core capabilities I need • Real-time market analysis that fuses live quotes with historical time-series data, news feeds, social-media sentiment and technical indicators such as Wave Trend, VWAP and similar momentum tools. • Automated trade execution through a broker API, with order staging, smart routing and position monitoring. • Built-in risk management covering position sizing, max-loss cut-offs, volatility shocks and margin checks before any or...