NumpyJobs

...alternative lines, and relevant metrics in a simple user interface. • Include testing, performance optimization, and documentation for long-term maintainability. Recommended Technologies • Python for rapid prototyping and backend development • OpenCV for image and video processing • PyTorch for model training and inference • YOLO or a similar object detection model for card and chip recognition • NumPy / Pandas for data processing • PySide6 or Tkinter for the desktop interface • Optional solver integration if precomputed strategy outputs are available Development Plan 1. Define requirements and architecture Before implementation, define the exact project goals, constraints, and module boundaries. The developer should specify: &b...

...execute the full experimental evaluation pipeline. This project focuses on rigorous experimentation and evaluation, not developing new ML models. Required Skills Strong candidates should have experience in: Machine Learning / Deep Learning Computer Vision for remote sensing Geospatial data processing Experimental design for research papers Technical stack: Python PyTorch or TensorFlow OpenCV NumPy / Pandas Scikit-learn Matplotlib / Seaborn AWS cloud workflows GPU computing Experience with the following is highly desirable: Satellite imagery processing GeoJSON / GDAL STAC catalogs LLM integration Retrieval-Augmented Generation Nice-to-Have Experience Experience with: Academic ML research Remote sensing journals Geospatial AI Multimodal AI pipelines Experiment reproducibility f...

...publication-quality visualisations of the results The current code is organised in a single file. I want it refactored into well-named functions or classes, documented with concise docstrings, and wrapped in a short Jupyter notebook that demonstrates the new workflow from import to final figure. Please keep any existing logic that is still valid but feel free to streamline it with modern libraries—pandas, NumPy, SciPy, matplotlib or seaborn are all fine. Deliverables 1. Updated .py module(s) with preprocessing, stats, and plotting functions 2. Example Jupyter notebook that runs end-to-end on sample data I will supply 3. Brief README outlining dependencies and execution steps 4. Comments or inline notes highlighting any assumptions or edge-case handling you added...

...execute the full experimental evaluation pipeline. This project focuses on rigorous experimentation and evaluation, not developing new ML models. Required Skills Strong candidates should have experience in: Machine Learning / Deep Learning Computer Vision for remote sensing Geospatial data processing Experimental design for research papers Technical stack: Python PyTorch or TensorFlow OpenCV NumPy / Pandas Scikit-learn Matplotlib / Seaborn AWS cloud workflows GPU computing Experience with the following is highly desirable: Satellite imagery processing GeoJSON / GDAL STAC catalogs LLM integration Retrieval-Augmented Generation Nice-to-Have Experience Experience with: Academic ML research Remote sensing journals Geospatial AI Multimodal AI pipelines Experiment reproducibility f...

I’m expanding our team with an AI engineer who can take the lead on end-to-end Machine Learning work. The immediate focus is on time-series data: everything from cleaning raw feeds through to building and shipping a production-ready predictive model. You’ll be working in Python (think pandas, NumPy, scikit-learn, TensorFlow or PyTorch) and will have the freedom to introduce the tools you’re most comfortable with, as long as the final stack is reproducible and easy to maintain. Here’s what I need from you: • Prepare and engineer the time-series dataset so that it’s model-ready, documenting every transformation. • Design, train, and iterate on forecasting or anomaly-detection models that outperform a naive baseline. • Hand over clean, wel...

I need a clean, well-documented NumPy implementation of backpropagation for a Convolutional Neural Network that will train on colour image data. No high-level deep-learning libraries—just NumPy (plus optional helpers such as tqdm or Matplotlib for progress and visual checks). The forward pass is already covered on my side; your job is to write the full backward pass so that every layer (convolution, ReLU, pooling, fully-connected, soft-max with cross-entropy) contributes correct gradients all the way to the parameters. Efficiency matters but readability comes first, as the code will be used for teaching. Acceptance criteria • Functions for each layer that return both output and cache, plus a matching backward function that consumes the cache and returns gradien...

...Key Requirements: - Engaging and interactive presentation of AI capabilities - High-quality visuals and graphics - Clear and concise messaging tailored to potential clients - Experience in marketing materials and AI technologies is a plus Scope • Build the complete workflow in Python, using current mainstream libraries (scikit-learn, TensorFlow or PyTorch, plus supporting tools such as pandas, NumPy, and Streamlit/Plotly for visual insights). • Provide clean, reproducible code, modular enough to be adapted later. • Document the entire pipeline with architecture and data-flow diagrams, plus a concise narrative that explains each stage to a non-technical audience. • Incorporate rigorous evaluation: baseline, key metrics, and a brief ablation or feature-importan...

Python Data Analysis ...or regression). Evaluate the model performance using metrics like accuracy, precision, recall. Documentation & Report: Provide a brief report of the analysis, model results, and recommendations. Include the final cleaned dataset and the trained model files. Requirements for Freelancer: Strong experience in Python, data analysis, and machine learning. Familiarity with Scikit-learn, Pandas, NumPy, and data visualization tools. Ability to deliver clean, well-documented code and reports. Deliverables: Cleaned and preprocessed dataset. Python scripts or Jupyter Notebooks with analysis and model. Visualizations and charts. Short report summarizing results and insights. Timeline: 5–7 days from project start. Budget: $50–$100 depending ...

... well-commented Python code (Jupyter notebook or .py scripts) plus a brief README describing setup, retraining, and inference steps. • Optional but appreciated: a lightweight way to serve the model (e.g., FastAPI endpoint or batch script) so it can slot straight into production. I’ll provide the dataset and any domain context you need right after kickoff. If you have experience with pandas, NumPy, scikit-learn, statsmodels, TensorFlow/PyTorch, or Prophet, you’ll be right at home. Accuracy, clarity, and reproducibility are more important to me than flashy visuals, but a concise plot or dashboard that helps explain the results would be a bonus. Let me know what modeling approach you’d start with, how long you’ll need to deliver the first working pro...

...Backtesting System Test strategies using historical data. Show results such as: Win rate Profit/loss Drawdown Number of trades Live Analysis Run the strategy on live data. Generate signals or alerts when conditions are met. Dashboard A simple web dashboard where I can: View charts Monitor signals See performance statistics Control strategy settings Suggested frameworks: Python Pandas / NumPy Streamlit or Flask dashboard Strategy Customization The tool should allow modification of: Indicators Entry rules Exit rules Risk management settings Documentation Clear installation instructions Code comments Basic user guide The goal is to create a system that can analyze historical data, run backtests, and display results in a simple dashboard. Core Requirement...

...techniques like edge detection, contour detection, and gradient analysis to determine the steepest safe path for climbing. Applied the Hill Climbing optimization algorithm to continuously choose the best next movement direction based on terrain analysis. Integrated real-time decision making so the robot adjusts movement dynamically while climbing uneven surfaces. Technologies Used Python OpenCV NumPy Computer Vision Hill Climbing Algorithm Real-time Image Processing Core Features Real-time terrain detection using camera feed Edge detection for slope identification Path optimization using Hill Climbing algorithm Dynamic movement adjustment while climbing Obstacle detection and avoidance Advanced Features (for CV – very important) Add these to make the projec...

...learning models for performance and accuracy. Mentor junior engineers and assist with code reviews and best practices. Skills & Qualifications: Proven expertise in machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn. Deep learning experience with neural networks (CNNs, RNNs, transformers, etc.) and advanced AI models. Strong background in Python programming, data manipulation (NumPy, pandas), and algorithms. Experience in deploying models at scale using tools like Docker, Kubernetes, AWS, or GCP. Solid understanding of statistics, linear algebra, and calculus. Experience in big data tools (Spark, Hadoop) and cloud platforms (AWS, GCP, Azure) is a plus. Ability to communicate complex technical concepts effectively to non-technical stakeholders. Strong pr...

...The program should combine theory with plenty of hands-on practice so I can start applying what I learn right away. What I expect • A clear learning path that begins with fundamental concepts (data types, basic statistics, exploratory analysis) and steadily introduces more advanced topics. • Practical exercises and mini-projects using widely adopted tools such as Excel, SQL and Python (Pandas, NumPy, Matplotlib or similar), so I can build a small portfolio along the way. • Short quizzes or checkpoints after each module to confirm I have grasped the material. • Real-world datasets for practice plus guidance on where to find additional open data. • Live or recorded walkthroughs that explain your workflow step-by-step—screen-sharing or annotat...

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I'm seeking a Python expert to develop a credit risk management model, focusing specifically on market data. Key Requirements: - Credit Risk Expertise: In-depth understanding of credit risk, particul...market data. - Python Proficiency: Advanced skills in Python for data analysis and modeling. - Data Handling: Experience in working with large datasets, especially market data. - Financial Acumen: Strong background in finance and risk management principles. Ideal Skills and Experience: - Proven experience in developing credit risk models. - Familiarity with relevant Python libraries (e.g., Pandas, NumPy, Scikit-learn). - Ability to provide clear documentation and insights on the model developed. Please share your relevant experience and approach to this project. I look forwar...

...mini-projects. Rather than general tutoring or technical platform troubleshooting, I specifically need someone who can step in, finish the required tasks, and submit clean, well-commented solutions on my behalf. This will be an ongoing project and will span an year. Typical exercises range from data preprocessing and feature engineering to training and evaluating models with libraries such as Python, NumPy, pandas, scikit-learn, TensorFlow or PyTorch. Code must run flawlessly in the course’s Jupyter-based environment and meet the rubric laid out in each brief (accuracy thresholds, narrative explanations, and any visualisations the instructors request). What I’d like to see in your offer is a short note about your relevant experience—previous AI/ML coursework...

I have already trained and deployed a Logistic Regression model in Streamlit that classifies breast-tumour samples as malignant or benign. What I need now is a polished data-visualization layer so users can quickly grasp how eac...Python module (or Streamlit component) that produces the requested bar charts • Seamless integration with the existing Streamlit interface—no regressions to current functionality • Clean, readable code using Matplotlib or Seaborn, with comments and docstrings • Brief README explaining how to invoke the charts and adjust feature lists You will be working in a familiar stack—Python, Pandas, NumPy, Scikit-learn, Seaborn/Matplotlib, Streamlit—so please highlight previous projects where you delivered similar visual insi...

...Develop a lightweight dashboard (using **Streamlit** or **Dash**) to visualize "Best Case," "Base Case," and "Worst Case" profit scenarios. * **Documentation:** Ensure the code is modular and well-documented so it can be updated as our strategy evolves. ### **Required Skills** * **Core Python:** Expert-level proficiency in Python 3.x. * **Data Libraries:** Strong experience with **Pandas** and **NumPy** for numerical modeling. * **Visualization:** Experience with **Matplotlib**, **Plotly**, or **Seaborn**. * **Financial Literacy:** Basic understanding of business metrics (EBITDA, ROI, Valuation Multiples) is a huge plus. * **Reliability:** Ability to work **10–15 hours per week** with consistent communication. * **Duration:** Ongoing, part-t...

...precision, recall, F1, G-mean, ensemble weighting) set with the template’s equation environment. • A few well-labelled diagrams: system architecture, data-flow, and comparative ROC/PR curves. • All in-text citations and reference list strictly in APA style. • Implementation notes reference scikit-learn (RandomForestClassifier, IsolationForest) and any supporting Python tools such as pandas, NumPy, and Matplotlib. Deliverables 1. Editable source files (Word / LaTeX plus figures). 2. A compiled PDF ready for direct submission. 3. A short README outlining dataset links, Python version, and command line to reproduce results. Acceptance criteria • Conforms to Springer or Scopus template without formatting warnings. • Plagiarism-free...

I need a purpose-built bot that can execute fully automated buying and selling of stocks throughout the t...compiles and runs on Windows or Linux with clear setup instructions. – Simulated back-tests on one month of intraday data match expected win/loss ratios. – Live paper-trading session completes a full market day with zero manual fixes required. – Source code is clean, modular, and documented well enough for me to tweak indicators later. Preferred tech: Python with libraries such as pandas, NumPy, ta-lib, and websocket support, but I’m open to C# or Node.js if you already have a proven framework. If you’ve built stock trading automation before—especially day-trading systems—let’s talk. I’m ready to start as soon as yo...

I’m building a research-grade quantum simulator in Python and need a robust codebase that can accurately model multi-qubit circuits, apply standard gate operations, and return state-vector or density-matrix outputs. Whether you prefer to work directly with NumPy/SciPy or leverage existing open-source frameworks such as Qiskit, Cirq, or QuTiP is completely up to you; the key requirement is clean, well-documented code that runs reliably under Python 3.11. Please provide: • A modular simulation engine capable of handling at least 10-15 qubits, with optional noise or decoherence modelling • A clear, Pythonic API for defining circuits, executing simulations, and extracting results (probabilities, expectation values, etc.) • Unit tests plus a concise README tha...

...scaling and the usual EDA-driven feature engineering. What I want now is a measurable lift in overall model performance, with the F1-score as the guiding metric. Feel free to explore more advanced algorithms (e.g., Gradient Boosting, XGBoost, LightGBM, calibrated ensembles, or even a tuned version of my existing classifiers) as long as they integrate cleanly with the existing Python | Pandas | NumPy | Scikit-learn stack and can be surfaced through the current Streamlit front-end. Key points you should address • Re-examine preprocessing and feature selection only if it directly supports a higher F1-score; the interface and general UX can remain untouched. • Provide well-commented, reproducible code and a concise notebook or markdown explaining your methodology, h...

...score each candidate’s depth of knowledge across Python, Scala and SQL. Our stack centres on Azure and Databricks, so practical insight into large-scale Spark/PySpark jobs, data-model design, ETL orchestration and cloud performance tuning is essential. Candidates frequently discuss streaming, optimisation strategies and modern AI/ML add-ons, so any hands-on exposure to libraries such as PyTorch, NumPy, SciPy or TensorFlow will help you challenge them at the right level, though it is not mandatory. Availability is limited to two focused hours per weekday; I will share the interview schedule at least 24 hours in advance. After each session you will file a concise written assessment noting technical strengths, gaps and a simple hire/no-hire recommendation. Consistency an...

...years. Primary questions on the table: • How have sales trends evolved month-to-month and season-to-season? • Which customer segments are driving (or dragging) revenue, and how has their purchasing behaviour shifted? • Which products or product groups are over- or under-performing once promotions, returns and stock-outs are factored in? A clean, reproducible workflow in SQL, Python (Pandas, NumPy, Sci-Py) or R is essential so the team can rerun the analysis after future data drops. Visual explanations—preferably in Tableau, Power BI or Matplotlib/Seaborn—should accompany every finding, making it easy for non-technical stakeholders to see root causes at a glance. Deliverables 1. Processed dataset with all transformation scripts documented. 2...

...another and visualising how they behave on real data. The focus is evenly split between implementing the code, walking through every line of mathematical reasoning, and then training and comparing the resulting classifiers. Concretely, I must deliver: • Logistic regression – full derivation of the log-likelihood, gradient, and Hessian, followed by a working optimiser that reproduces those steps in NumPy/SciPy. • EM for a constrained Gaussian Mixture Model – step-by-step derivation of the E and M updates with the specified covariance constraint, plus a clean implementation that converges on synthetic and real data. • Naive Bayes spam classifier – closed-form derivations for the parameter estimates and a vectorised implementation that processe...

...anchor the conversation; from there, the real focus is prescriptive analysis that translates those patterns into actionable recommendations. You’ll clean and explore the data, highlight the key historical insights, then build an optimization or scenario-based model that prescribes concrete actions (e.g., portfolio rebalancing, cost-cutting levers, revenue-boost initiatives). Python with pandas, NumPy, scikit-learn—or R with tidyverse—is fine as long as the workflow is fully reproducible and the logic is transparent. Deliverables • Cleaned dataset with documented preprocessing script/notebook • Visual and written summary of the historical performance • Prescriptive model code plus a brief, plain-English explanation of how it works • ...

Senior Data Scientist / Lead Search Engineer for Local Algorithmic Optimization (Ukraine) Senior Data Scientist / Lead Search Engineer for Local Algorithmic Optimization (Ukra...regions. Automation Development: Build and maintain Python-based scripts for data extraction and competitor tracking. Technical Audit: Optimize site architecture and schema for local search performance. Required Professional Background Advanced Technical Degree: A background in Computer Science, Artificial Intelligence, or Mathematics is required. Data Proficiency: Mastery of Python (Pandas, NumPy) and the Google Maps API. Local Expertise: Deep understanding of the search landscape and language nuances in Ukraine. Full-Stack Capability: Knowledge of TYPO3 or similar enterprise CMS for technical imple...

I’m assembling a small pool of Python specialists who can jump in as on-demand interviewers for my hiring pipeline. Every candidate you meet will be applying for a data-science-focused role, so I need someone whose own background is genuinely senior-level in that space—think daily work with pandas, NumPy, scikit-learn, Jupyter notebooks, model deployment, and the usual mix of statistics and SQL that glues real projects together. Your task is to run one-hour coding-test sessions over Zoom or Google Meet. I supply the shortlist of applicants and the calendar invites; you supply the technical depth and the objective scoring. During each session you will: • present or share a prepared data-centric coding challenge (I’m happy to reuse a problem you already tr...

...cleansed at a basic level; now I want to dig deeper—descriptive statistics, correlations, trend discovery, and any notable anomalies that jump out once the numbers are explored properly. The workflow I imagine is straightforward: connect directly to the database (credentials will be supplied), pull the relevant tables, and run the analysis in Python or R using standard libraries such as pandas/NumPy or dplyr/tidyverse. If you prefer SQL-heavy exploration first, that’s fine too; the goal is to surface findings in a format that’s easy to digest. Deliverables • A concise written report (PDF or Markdown) summarising key insights, supported by clear visuals (plots, charts or dashboards). • The reproducible code/notebook used for the analysis, with com...

... engine that trades the major forex pairs—think EUR/USD, GBP/USD and the rest of the high-liquidity set. The core objective is to exploit short-term mean-reversion and cointegration opportunities, all fully automated from signal generation through execution. Here is what I need from you: • Strategy logic coded in a language suited for low-latency connections to my broker’s API (Python with NumPy/Pandas is fine, but I’m open to C++ or a mixed approach if latency becomes critical). • Robust data-handling: live tick or one-second data ingestion, plus a pipeline for historical price pulls so we can back-test properly. • Back-testing framework that reports Sharpe, max drawdown, win rate and trade distribution, with parameter optimisation built ...

...Statistics (Foundation) Linear Algebra Calculus Probability Statistics Why first: Math is the backbone of algorithms, ML, and deep learning. 2. Programming & Core Tools Python R Git Data Structures & Algorithms SQL Why: Programming skills and understanding data structures are essential to implement data solutions and work with databases. 3. Data Preparation & Visualization Pandas NumPy Matplotlib Seaborn Why: Before modeling, you need to clean, manipulate, and visualize data effectively. 4. Business Intelligence Tools Tableau Power BI Why: Understanding dashboards and reporting helps communicate insights to stakeholders. 5. Machine Learning Scikit-learn TensorFlow PyTorch Why: Learn supervised, unsupervised, and reinforcement learning for pr...

Required skills Strong mathematics / statistics (regression, distributions, normalization, variance, shrinkage, model validation) Solid Python engineering (pandas/numpy, clean architecture, testing, reproducibility) Experience with sports/racing analytics or time-series/competition modelling Ability to explain assumptions and tradeoffs clearly Nice to have Prior work with horse racing specifically (pace, sectionals, speed maps, track bias, class ratings) Bayesian modelling, Elo-style ratings, survival/hazard models Experience scraping/cleaning messy sports data

...system and need a Python developer who can take my raw ideas and translate them into production-ready algorithms. The core of the job is end-to-end algorithm development: ingesting historical market data, crafting and refining signal logic, running robust backtests, and preparing the strategy for live execution through a broker API. You should be comfortable working with common quant tools—pandas, NumPy, SciPy, ta-lib, or similar—as well as a backtesting framework such as Zipline, Backtrader, or your preferred equivalent. Clean, modular code with thorough docstrings and unit tests is essential because I plan to iterate quickly once the initial version proves itself. I will supply any proprietary data or specific parameter ideas; you’ll advise on data cleaning,...

I want to put numbers behind an intuition: many NSE/BSE mid-cap counters seem to jump on roughly the same calendar dates each year. Your task is t...well-commented Python/R script you used so I can rerun or extend the study. Acceptance criteria: • Data span ≥10 full years per stock. • Results reproducible with the supplied code and publicly available data (NSE Bhavcopy, BSE archives, yfinance, Quandl, etc.). • Spreadsheet opens cleanly with no macros and figures reconcile with the code output. Use whichever toolchain you prefer—Pandas, NumPy, R tidyverse, matplotlib/seaborn for quick sanity plots—so long as the final answer is a clear, filter-friendly spreadsheet that tells me the probability of a meaningful up-move on any given day of the year fo...

.../ No Cancer) OR Multi-class classification (Cancer types) OR Tumor segmentation (locating tumor regions) Treatment Prediction Goal Predict response to therapy (Responder / Non-responder) Predict survival category Predict recurrence risk For MVP: Start with diagnosis, then add treatment prediction. STEP 2: Setup Development Environment Install Dependencies Python 3.9+ PyTorch MONAI pydicom numpy scikit-learn FastAPI or Flask Example: pip install monai torch torchvision pydicom fastapi uvicorn scikit-learn Setup GPU Local CUDA GPU OR Cloud (AWS/GCP/Azure) STEP 3: PET Scan Dataset Preparation Collect Dataset Public PET database (e.g., TCIA) Research partnership dataset Must include: PET images Diagnosis labels (Optional) treatment outcome labels Organize Data Structu...

...statistical summary report (PDF or Word) that explains key measures, visualises results where useful, and highlights notable findings • Optional but appreciated: the reproducible script or Excel formulas you used so I can rerun the analysis later Accuracy and attention to detail are more important to me than speed; everything starts and ends in Excel, but you are free to use R, Python (pandas, NumPy), or similar tools while working—as long as the final files open flawlessly in Excel 365....

...a brief executive summary with top 5 insights and strategic recommendations backed by data. Deliverables: Data Quality Log (Excel/PDF) documenting issues and fixes. SQL DDL Scripts & Data Ingestion queries. Python Jupyter Notebooks (Cleaning, EDA, Statistics). Power BI .pbix file with data model and reports. 2-Page Executive Summary (Insights & Recommendations). Required Skills: Python: pandas, numpy, matplotlib, seaborn, scipy/statsmodels. SQL: MySQL or PostgreSQL (DDL, Constraints, Joins). Visualization: Power BI (DAX, Data Modeling). Statistics: Hypothesis testing, A/B testing concepts. Documentation: Ability to clearly explain data cleaning decisions. To Apply: Please share examples of previous dashboards or analytics projects. In your proposal, briefly mention how yo...

I need a Python-based routine that will pull several energy-market data sources into pandas DataFrames, t...can see what’s coming Acceptance criteria • Reproducible Jupyter notebook or .py script with clear function blocks • Sample charts for at least one complete delivery month and one spread pair • All raw and processed data saved to DataFrames and pickled/CSV’d • README explaining set-up, dependencies, and how to point the code at new data paths Libraries I normally work with are pandas, numpy, matplotlib/plotly, seaborn, mplfinance, and yfinance/requests for scraping, but I’m open to your preferred stack as long as it’s Python. Please build the solution so I can run it locally on Windows; any external APIs or credentials sho...

I have a cleaned-but-raw dataset plus a small MNIST subset waiting for a full exploratory and modelling pass in Python. My goal is to understand its underlying structure through several classic unsupervised techniques—density estimation, Gaussian Mixture Models trained with the Expectation–Maximisation algorithm, PCA for dimensionality reduct...a single command or notebook execution. – All figures render without manual tweaks and are saved to disk. – Explanations are written in plain English, no unexplained jargon. – Delivery is within the next few days (ASAP), including one quick iteration if minor tweaks are needed. I will provide the datasets the moment we start; you handle the rest using Python and common libraries such as NumPy, Pandas, scik...

I have a sizeable set of consumer-level data that needs to be examined with a clear, methodical approach. My priority is rigorous data analysis that turns raw records into well-supported insights I can act on right away. What I will provide • A cleaned CSV expo...quick visual snapshots (charts or dashboards) that make the results easy to present internally. Acceptance criteria 1. All code is fully commented and runs end-to-end on the supplied dataset. 2. Insights are backed by numbers and clearly referenced to the source fields. 3. Final deliverables arrive in an agreed-upon shared folder and open without errors. Tools you are comfortable with—Pandas, NumPy, R tidyverse, Looker Studio, Tableau—let me know; I’m flexible as long as the outcome is solid, t...

...instantâneo, quero relatórios detalhados que mostrem o histórico de performance, taxa de acerto por período e gráficos de lucro potencial, tudo exportável em CSV ou PDF. Notificações push devem funcionar tanto em Android quanto em iOS, respeitando a formatação nativa do Telegram (botões inline, markdown básico). Tecnologias sugeridas: Python ou Node.js para o backend, bibliotecas como Pandas, NumPy e um modelo de machine learning (XGBoost, LightGBM ou rede neural) treinado com os dados históricos que eu disponibilizo. A infraestrutura pode rodar em servidor VPS ou Heroku; aceito recomendações de hospedagem se isso facilitar a latência dos alertas. Entrego a você o...

...Ingredient min/max limits Regularization to prevent micro-dosing overfitting Performs residual minimization after re-analysis. Supports iterative refinement (Trial-1 → Trial-2 → Trial-3 loop). This is an inverse analytical modeling engine, not a surface-level similarity system. Mandatory Requirements (No Exceptions) You must have: Strong Python experience (minimum 4–5 years) Advanced Pandas and NumPy knowledge Experience with scientific or analytical datasets Experience implementing regression models Experience with constrained optimization (linear or nonlinear) Understanding of L1/L2 regularization Experience with numerical modeling (SciPy optimize or similar) Ability to clearly explain statistical calibration logic Clean modular code architecture ...

...Identify marker compounds and decision rules Create probabilistic ingredient combination models Generate optimized recipe outputs (percentage-based, normalized to 100%) Structure the system in a modular and scalable Python architecture Build clean, documented code suitable for long-term expansion Required Skills (Mandatory) Strong Python programming experience (3+ years) Advanced Pandas & NumPy knowledge Scientific data processing experience Experience working with structured chemical datasets Algorithm development experience Data normalization & similarity scoring logic CSV / Excel data parsing Clean code & modular architecture mindset Strong Plus Experience with chromatography or GC-MS data Background in analytical chemistry Experience in scientif...

...86-million-dollar portfolio and need them transformed into clear, defensible investment insights. The core purpose is to evaluate current and prospective opportunities, not merely spot trends or forecast performance. Expect to dig into raw transactional data, balance sheets, and market feeds, then surface the strengths, weaknesses, and hidden potential of each holding. You are free to work in Python (Pandas, NumPy, SciPy), R, SQL, Excel Power Query, or any other toolkit you trust, so long as the outcome is reproducible. I will provide the data in CSV and relational-database dumps; secure transfer protocols are already in place. Deliverables • Cleaned and well-documented dataset (with transformation scripts) • Analytical report highlighting valuation metrics, risk ...

...countries**, compares **time-wise interest**, and explores **related keywords** to uncover search behavior patterns. ## Objectives * Analyze time-wise search interest of a keyword * Compare keyword popularity across 15 countries * Identify and analyze related search keywords * Visualize trends for better insights ## Tools & Technologies * Python * Pytrends (Google Trends API Wrapper) * Pandas * NumPy * Matplotlib * Seaborn * Jupyter Notebook ## Project Structure ``` ├── google data analysis # Main notebook ├── # Project documentation ``` ## Key Analysis * Interest over time (trend analysis) * Country-wise keyword comparison * Related queries and keywords analysis * Data visualization using line charts and bar plots ## How to Run

...the text and image sets. • Concise visual summaries—charts for the text, heat-maps or feature plots for the images—exported in high-resolution formats I can drop straight into presentations. • A short written report (PDF or Markdown) highlighting key findings, unusual correlations and any recommendations that emerge from the analysis. Feel free to lean on standard libraries such as pandas, NumPy, NLTK/spaCy, OpenCV or PyTorch-based utilities—whatever helps you surface meaningful insights quickly and reproducibly. If you have other preferred tools that suit mixed unstructured data, I am open to them so long as the final deliverables remain easily reusable. I’ll supply a structured folder containing sub-directories for /text and /images along...

I have a set of finance-related CSV files that need to be explored, cleaned, and summarised. The goal is strictly descriptive analysis—think clear statistics, trends, and visual snapshots—without venturing into predictive modelling or prescriptive optimisation. All raw data will arrive as comma-separated files. You are free to use Python (pandas, NumPy, Matplotlib, Seaborn), R, Excel Power Query, or a comparable toolkit, as long as the workflow is reproducible and well-documented. Deliverables: • A concise cleaning script or notebook that imports each CSV, handles missing or inconsistent entries, and outputs a tidy dataset • A written summary (PDF or Markdown) of key descriptive metrics—averages, distributions, correlations, outliers—tailored to...

...performance (PnL, Sharpe, drawdown) Requirements Proven experience in algorithmic trading / quant development Strong Python programming Experience with QuantConnect or LEAN Engine Experience with IBKR API integration Understanding of US equities / ETFs markets Experience with backtesting frameworks Knowledge of trading risk management Nice to have Intraday or HFT strategies pandas / numpy / scipy Walk-forward optimization Experience in prop trading / hedge fund C# (LEAN) Project details Market: US stocks & ETFs Broker: Interactive Brokers Platform: QuantConnect / LEAN Strategy type: systematic / algorithmic Engagement: long-term collaboration possible To apply Please include: Relevant algo trading projects QuantConnect / LEAN experience IBKR integrati...

I’ll hand over three raw datasets—sales transactions, customer demographics, and product inventory—spanning several stores. Your task is to stitch them together, clean inconsistencies, and delve into them with Python. Using Pandas, NumPy, and Matplotlib (feel free to add Seaborn or Plotly if that speeds insight), uncover how buying behaviour shifts: • weekday versus weekend • month by month I’m interested in concrete, data-backed stories: which products spike on Saturdays, whether certain customer segments shop more mid-week, seasonal category swings, ticket size trends, and anything else you spot that helps me fine-tune promotions and staffing. Deliverables • A merged, tidy dataset ready for future modelling • A well-commented Ju...

I am working on a graduate-level project that involves mixed data types and I need one-on-one guidance to reinforce my skills—especially in data cleaning and preprocessing with Python. The focus will be on the practical, step-by-step application of Pandas, NumPy and Matplotlib while staying fully compliant with academic integrity guidelines; no AI-generated work is permitted. My most urgent challenges include: • Handling missing values • Removing duplicates • Dealing with outliers Beyond cleaning, I will also ask for advice on choosing suitable descriptive statistics, selecting the right statistical tests, and presenting results clearly. Expect questions on relationship analysis and time-series concepts as the project evolves. What I’m hoping ...