Numpy bytecode jobs

...FastAPI project. Problem: Project is breaking due to environment/dependency issues: ModuleNotFoundError: TensorFlow fails to load () NumPy 2.x incompatibility with TensorFlow _ARRAY_API not found error Uvicorn crashes while importing Project Details: Backend: FastAPI Model: TensorFlow (Keras) Files: (API) (model loading + prediction) dataset/model/ (, , ) dataset/PlantVillage/ (class folders) Requirements: Create a clean working Python environment Fix all dependency issues: TensorFlow compatibility NumPy version compatibility (<2) Keras / model loading issues Ensure model loads correctly using: Fix API endpoint: /disease-detection-file should accept image

I’m putting together a Python-based solution for an aviation use case and need an experienced developer who knows both the language and the industry. The exact feature set is still flexible—ranging from data-driven analytics and task automation to a lightweight application layer—but the end goal is the same: clean, reliable code that fits...refine the requirements • Unit tests and basic CI setup so the code can be trusted in production • Concise developer docs and a quick-start README to make future iterations painless If you’re comfortable brainstorming the final spec with me, then moving quickly into an MVP and iterating from there, I’d like to hear how you would approach the build, the libraries you’d lean on (pandas, FastAPI, NumPy...

I need an end-to-end Spotify analytics dashboard, ready for production use between March 2026 and April 2026. The data arrives as raw streaming logs; you will prepare it in Python (pandas, NumPy, Matplotlib/Seaborn where helpful) and feed a clean model into Power BI. The finished report must speak the language of Analysts, exposing deep drill-through while staying light and fast. Core visuals include KPI cards for Distinct Songs, Average Popularity, and Position-1 Hits, an area graph that tracks monthly growth, plus a mix of charts, tables, slicers and other interactive elements that let users pivot by artist, track, album type or time period. Consistent colour and typography should guide the eye and keep the music theme alive. Key objectives • Transform messy CSV/JSON export...

I already code in C++ and R, yet I have never had a structured journey through Python. I want to start with the absolute fundamentals—syntax, data types, functions—and, step by step, reach advanced topics such as object-oriented design, decorators, asynchronous programming and popular libraries like pandas, NumPy and Flask. Because I learn fastest by doing, I need live over recorded sessions paired with hands-on exercises and short projects that we review together. Each lesson should leave me with runnable code, clear explanations and a small assignment so I can practise before we meet again. By the end, I expect to feel confident building a complete, well-structured application on my own. If you are comfortable teaching from basics all the way to expert-level patter...

I have a set of customer survey responses and I need them transformed into clear, actionable product-feedback insights. The workflow I have in mind is straightforward: • Load and clean the raw survey data in python so it is well-structured for analysis. • Explore and model the data with Python (pandas, NumPy, matplotlib or seaborn are fine) to surface patterns in what shoppers like, dislike, and expect next from our product range. • Build an interactive Power BI dashboard that lets non-technical teammates drill down by questions, segments, or time periods and quickly spot the most common themes and emerging preferences. Please deliver the cleaned SQL tables, well-commented Python notebooks or scripts, a packaged .pbix file, and a concise slide or PDF summary o...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...guardrails) • Integrated backtesting module so I can validate ideas on historical data before going live • Clean, well-commented codebase that I can extend, plus simple configuration files for tweaking parameters without rewriting code I’m comfortable providing API keys and sample datasets; you just need to wire them into the solution. Preferred stack is Python 3.x with libraries such as pandas, NumPy, TA-Lib/ta, backtrader (or another reliable framework) and WebSocket handling for streaming quotes. Deliverables are the complete source code, setup instructions, and a short video or written walkthrough showing the algorithm running in both backtest and paper-trade modes. If anything is unclear, let’s clarify early so the first milestone already compiles...

...physical reference measurement) Stage 6-7: Manual and auto annotation tools (browser-based) Stage 8: Multiview triangulation of asset 3D positions Stage 9: Taper ordering and work zone measurements Stage 10: Ground truth validation Stage 11: Report generation (, , ) Tech Stack Python 3.10+ COLMAP (custom GPU build) RT-DETR object detection model (HuggingFace) NumPy, pandas, OpenCV, matplotlib ENU coordinate system, GPS/IMU sensor fusion What We Need Testing — run the full pipeline on multiple video segments and verify outputs are correct at each stage Validation — compare pipeline measurements (cone spacing, taper length) against known ground truth values, document accuracy Bug Fixes — identify and fix remaining issues including: Stage 9 taper/lane closure

I'm seeking an experienced Python developer to help with data analysis and automate data scraping tasks. Key Requirements: - Develop Python scripts for data analysis. - Automate data scraping from websites. - Deliver clean, structure...analysis and automate data scraping tasks. Key Requirements: - Develop Python scripts for data analysis. - Automate data scraping from websites. - Deliver clean, structured datasets for analysis. Ideal Skills: - Proficiency in Python, especially for data manipulation. - Experience with libraries like BeautifulSoup, Scrapy, or Selenium. - Strong background in data analysis, preferably with Pandas or NumPy. - Familiarity with data storage solutions (e.g., SQL, NoSQL). Looking for someone who can write efficient, reliable scripts and has a keen ...

...data-driven insight into which products are really driving revenue. The goal is simple: surface product-level sales trends so I can see which SKUs consistently outperform and which ones lag behind, all viewed through the lens of revenue generated rather than just unit counts. You’re free to structure the workflow as you see fit, but I expect the core analysis to happen in Python, leveraging pandas and NumPy for data wrangling and aggregation. I’ll supply the dataset along with a brief data dictionary; you return a well-commented Jupyter notebook (or .py script) that: • Cleans and normalises the raw data • Calculates revenue by product over selectable time windows • Highlights statistically significant up- or down-trends • Presents the findi...

I’m a complete beginner who wants to move well beyond theory and into practical, project-based learning. The journey should start with data-science fundamentals—Python, NumPy, pandas, basic statistics, data cleaning and visualization—then progress step-by-step to machine-learning models, large-language-model workflows, retrieval-augmented generation (RAG), agent frameworks, and automation best practices. Hands-on projects are essential; each topic needs to come with a small, self-contained exercise or micro-app that I can build, run, and extend. Think Jupyter notebooks, lightweight Flask or FastAPI endpoints, and scripts that hit real APIs (OpenAI, Hugging Face, LangChain, etc.). Code must be well-commented so I can retrace the logic afterward. Ideal flow &...

I need an experienced machine learning expert to develop a clustering model. The project involves working with numerical data. Key requirements: - Expertise in clustering algorithms (e.g., K-means, hierarchical, DBSCAN) - Proficiency in Python and relevant libraries (e.g., scikit-learn, NumPy, pandas) - Strong background in data preprocessing and feature engineering - Ability to interpret and visualize clustering results - Experience with model evaluation and validation techniques Please provide examples of similar projects you've completed.

I need a Python coder to help with basic data analysis tasks. The data is structured, so experience with CSV and SQL is essential. Key tasks include: - Data cleaning and preprocessing - Data visualization - Statistical analysis The ideal freelancer should have: - Proficiency in Python - Experience with data analysis libraries (e.g., Pandas, NumPy, Matplotlib) - Ability to work with structured data formats like CSV and SQL Please provide examples of similar work.

...Use suitable techniques for categorical exploration—cross-tabulations, chi-square tests, association rules, clustering on encoded variables, or any other method you feel is insightful. • Present the findings in clear, non-technical language supported by concise visuals (bar charts, heat-maps, mosaic plots or similar). • Provide a short, well-commented notebook or script (Python with Pandas, NumPy, SciPy, scikit-learn or R equivalents) along with an executive summary slide or PDF. Acceptance criteria The deliverable should let me: 1. See the key relationships between categories at a glance. 2. Understand any notable trends or unexpected concentrations. 3. Walk away with two or three actionable insights I can share with stakeholders. If you’re ...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

I’m looking for a seasoned Python professional to step in and keep my existing codebase running smoothly. The core of the project is a collection of data-processing scri...commit messages, and provide a concise change-log for every batch of fixes. If you spot opportunities for small enhancements, feel free to propose them; I’m open to sensible improvements as long as the primary focus stays on maintaining the current scripts. Stack highlights you should already know: • Python 3.x • Flask for the few API wrappers around the scripts • Typical data tooling (Pandas, NumPy, SQLAlchemy) Deliverables will be merged, tested, and accepted once existing functionality passes and any new tests you add succeed. Let’s keep the communication tight, the code...

...standard deviation for every variable so the artists immediately see the “typical” trajectory as well as the natural spread. Other metrics such as percentiles or min/max can stay in the background; we will only display them later if the director asks. • A short Python/TensorFlow (or PyTorch) pipeline is preferred so we can regenerate results when the blast parameters change. Feel free to leverage NumPy, Pandas and Plotly or Matplotlib for plotting—whatever keeps the workflow light and editable. • Final delivery should include the source code, a reproducible notebook, the generated CSV/JSON of raw predictions and the rendered charts (PNG or interactive HTML). Acceptance A run of at least 500 simulated blasts must complete in under 10 minutes on...

...into clear, actionable insights. We will begin with a short discovery call to review the data sources and define success metrics. From there, you will: • Set up a reproducible pipeline for data ingestion, cleaning, and exploratory analysis • Prototype models or visual analyses as appropriate, iterating based on feedback • Document every step in well-commented Python or R notebooks (Pandas, NumPy, scikit-learn, ggplot2—or your preferred stack) • Produce a concise summary report or dashboard that a non-technical stakeholder can understand Deliverables will include the cleaned dataset(s), all code/notebooks, and a final insight report. Code must execute on my machine without error, and findings should be backed by clear visuals or statistics. In...

...I WENT WITH IS NOT REAL (10-11) Project: **A Flask + MySQL web system with Machine learning module for preliminary classification and statistical analysis of suspicious death cases in Albania, with expert review and correction.** Use only: * Python Flask * MySQL * HTML/CSS/Bootstrap 5 * * SQLAlchemy * Flask-Login * Flask-WTF or equivalent * Werkzeug security * scikit-learn * pandas, numpy * joblib Important requirements: * Everything must be built **from scratch** * No public/premade dataset * No pretrained/premade model I need an **original synthetic dataset** created from scratch only for this project. It will be temporary and later replaceable with real data. Dataset requirements: * generated with custom Python code * realistic but simple forensic-style patterns * aro...

...suspicious death cases in Albania, with mandatory expert verification and correction.** Use this stack only: * Backend: Python Flask * Database: MySQL * Frontend: HTML, CSS, Bootstrap 5 * Charts: * ORM: SQLAlchemy * Auth: Flask-Login * Validation: Flask-WTF or equivalent * Password hashing: Werkzeug security * Machine Learning: scikit-learn * Model saving: joblib * Data processing: pandas, numpy Very important requirements: 1. **Everything must be built from scratch.** 2. **Do not use any premade dataset.** 3. **Do not use any downloaded/public dataset.** 4. **Do not use any pretrained or premade model.** 5. **Do not use any external AI/API for classification.** I need an **original improvised synthetic dataset** created by you from scratch specifically for this project. It...

...suspicious death cases in Albania, with mandatory expert verification and correction.** Use this stack only: * Backend: Python Flask * Database: MySQL * Frontend: HTML, CSS, Bootstrap 5 * Charts: * ORM: SQLAlchemy * Auth: Flask-Login * Validation: Flask-WTF or equivalent * Password hashing: Werkzeug security * Machine Learning: scikit-learn * Model saving: joblib * Data processing: pandas, numpy Very important requirements: 1. **Everything must be built from scratch.** 2. **Do not use any premade dataset.** 3. **Do not use any downloaded/public dataset.** 4. **Do not use any pretrained or premade model.** 5. **Do not use any external AI/API for classification.** I need an **original improvised synthetic dataset** created by you from scratch specifically for this project. It...

...correct type. Once the dataset is tidy, I want to explore it visually. I am especially interested in bar charts, line graphs and scatter plots that help surface the main trends and relationships hidden in the numbers and categories. Feel free to suggest any additional plots that would add real insight; however, the three mentioned above are the minimum I need delivered. Python with pandas, NumPy and either Matplotlib or Seaborn is perfectly fine, but I am open to R or another proven toolset if you prefer—it just has to be reproducible. Deliverables • Cleaned CSV ready for downstream work • The code or notebook used (well-commented so I can rerun it) • High-resolution bar charts, line graphs and scatter plots (PNG or PDF) with brief captions exp...

I have several months of raw sales data that I need turned into clear, ...those formats you prefer. Here is what I expect back: • A concise analytical report (PDF or slide deck) highlighting the key trends you discover, complete with charts or visualisations. • A clean, well-commented workbook or script so I can replicate or extend the analysis later. • A brief summary of recommendations drawn directly from the data. Your toolkit is up to you—Python (Pandas, NumPy, Matplotlib), R, Power BI, Tableau, Excel Power Query—whichever lets you dig deepest and present findings clearly. Timing is flexible but I would like to see an initial glimpse of your approach within a few days, with the full analysis soon after. If this first project goes well, there i...

...businesses reduce churn, improve customer retention, and increase profitability. Dataset Details Dataset includes customer demographic, financial, and behavioral features: RowNumber, CustomerId, Surname CreditScore, Geography, Gender Age, Tenure, Balance NumOfProducts, HasCrCard, IsActiveMember EstimatedSalary Target Variable: Exited (Churn Status) Tools & Technologies Python Libraries: pandas, numpy (data processing) matplotlib, seaborn (data visualization) scikit-learn (ML modeling) Advanced Techniques: SMOTE (handling class imbalance) Feature scaling & encoding Model evaluation metrics Exploratory Data Analysis (EDA) Performed deep EDA to uncover: Customer behavior trends Churn patterns across geography, age, and balance Correlation between features and churn Create...

I can assist with implementing ML models (e.g., regression, classification, clustering), data preprocessing, feature engineering, and performance evaluation using libraries like scikit-learn, pandas, and numpy. I ensure results are accurate, well-documented, and suitable for academic submission. Deliverables: • Clean, well-commented Python code (Jupyter Notebook) • Data preprocessing and feature engineering • ML model implementation and evaluation • Graphs, results, and interpretation • Concise explanation for report inclusion Ready to start immediately. Share the details and I’ll take it from there. Best regards, Hasan

We are seeking a skilled data analyst to assist with the following tasks: - Data cleaning and preprocessing, including handling missing values and duplicates. - Conducting Explorat...including handling missing values and duplicates. - Conducting Exploratory Data Analysis (EDA) to identify trends and actionable insights. - Building interactive dashboards and reports using Power BI. - Generating actionable business insights to support decision-making. Preferred Tools/Software: - Excel (advanced formulas, pivot tables) - SQL (data extraction and querying) - Python (Pandas, NumPy, Matplotlib) - Power BI The project is expected to be completed within 2 to 3 weeks, depending on complexity. The fixed budget for this project is ₹20,000, with slight flexibility based on scope and ...

...maintenance scheduling models: Baseline approach Greedy algorithm Integer Programming (PuLP or similar) Integrate machine learning predictions into scheduling decisions 5. Analysis and Results Compare AI-based maintenance with traditional methods Perform cost-benefit analysis (target improvement: 10–15%) Conduct sensitivity analysis Technical Requirements: Python (mandatory) scikit-learn, pandas, numpy Optimization libraries (PuLP or OR-Tools) Jupyter Notebook Experience in predictive maintenance or engineering datasets is preferred Deliverables: Clean, well-documented Python code Model outputs and evaluation metrics Simulation results (graphs and comparisons) Brief explanation of methodology for thesis use...

...g., KS test) 4. Optimization & Simulation Develop maintenance scheduling: Baseline model Greedy algorithm Integer Programming (PuLP or similar) Integrate ML outputs into scheduling decisions 5. Analysis & Results Compare: AI-based vs traditional maintenance Perform: Cost-benefit analysis (target ~10–15% improvement) Sensitivity analysis Tech Stack Required: Python (must) scikit-learn, pandas, numpy Optimization tools (PuLP / OR-Tools) Jupyter Notebook Experience with power systems / predictive maintenance (bonus) Deliverables: Clean, well-documented Python code Model outputs + evaluation metrics Simulation results (graphs, comparisons) Brief explanation of methodology (for thesis writing)...

... and lay down a robust preprocessing pipeline that feeds seamlessly into our future predictive models. The end-goal for this phase is clear: extract insights that help us validate product hypotheses and impress early pilot partners. We move fast—ASAP delivery is non-negotiable—so I’m looking for someone who can jump in, spin up notebooks, and iterate daily. Expect to work with Python (Pandas, NumPy, SciPy), SQL, and ideally Spark for the heavier ETL steps; experience deploying workflows on AWS or GCP is a plus because we’ll be productionising soon. Deliverables • Cleaned, well-documented datasets ready for downstream modelling • A reproducible preprocessing pipeline (scripts or notebooks + environment files) • An insights report high...

I need a Python-based trading algorithm that trades both the Nifty and Bank Nifty indices. The code should run locally on Python (feel free to lean on pandas, NumPy, TA-Lib, backtrader or similar libraries) and must be able to import and work with historical market data only—no live feed is required for this milestone. Here is what I expect: • A clean, well-commented Python script (or notebook) that ingests historical data, generates trade signals, executes the logic, and outputs detailed performance metrics and an equity curve. • Clear instructions on how to map the code to CSVs or API endpoints I already use for historical NSE data. • A short README explaining any configurable parameters so I can tweak settings for further experiments. Back-testing acc...

...Python expert to take several CSV and Excel files and turn them into solid, statistically sound insights. The work is focused entirely on data analysis and processing—no web scraping or app development—so your time goes straight into cleaning the raw tables, exploring patterns, and building the statistical models that answer my business questions. You should feel at home with pandas for wrangling, NumPy and SciPy for numerical work, and a modeling library such as scikit-learn or statsmodels to run regressions, clustering, or any technique you recommend. If you prefer working in a Jupyter notebook, that’s perfect; a well-commented .py script is also fine. I’ll supply the data files and a brief outlining the hypotheses I want tested the moment we start. De...

...involves straightforward data entry from spreadsheets, deeper analytical summaries drawn from a database, or shaping web-scraped records into a narrative that can power a report, a machine-learning pipeline, or an interactive dashboard. Here’s what I need from you: • A brief outline of how you would tackle the job once you see the raw files. • Your preferred tools—Excel, SQL, Python (Pandas, NumPy), or any other stack you’re comfortable with. • A sample of comparable work, if available, so I can gauge fit and style. • An estimated turnaround time for an initial milestone, plus how you’d verify accuracy and completeness. Deliverables will ultimately include the cleaned dataset in its native format, a clearly documented descript...

...choices Deliverables • Clean and processed dataset • Skill extraction pipeline (with comparison results) • Salary prediction models + validation analysis • Demand forecasting models + evaluation • Streamlit dashboard for visualization • Research report including: • Methodology • Experiments • Evaluation metrics • Results and insights • Limitations Requirements • Strong Python (pandas, numpy) • NLP experience (regex, spaCy, embeddings) • Machine learning experience • Understanding of evaluation metrics (precision, recall, F1, RMSE, etc.) • Experience with research-style work (preferred) • GitHub usage with regular commits • Must speak Hindi or Gujarati • Must attend ...

...resolution is still too coarse for my needs. I began experimenting with the MUSIC algorithm and even toyed with the idea of adding a continual-learning loop, but I would like a cleaner, expertly implemented solution—whether that is MUSIC, ESPRIT, a refined windowing strategy, or any other modern signal-processing method you can justify. Here is the current landscape: • Language & stack: Python with NumPy, SciPy and Matplotlib. • Key variable: signal-processing methods drive the entire detection chain, so any change must integrate cleanly with the existing pipeline. • Desired outcome: a measurable, demonstrable improvement in range resolution without an unacceptable hit to computation time. What I need from you: 1. Review the present codebase and out...

I need a Python expert to help automate data processing tasks. The data is primarily numeric. Key Tasks: - Write scripts to automate data processing. - Handle large datasets efficiently. - Ensure accuracy and reliability in automation. Ideal Skills and Experience: - Proficiency in Python, especially in scripting and automation. - Experience with libraries like Pandas, NumPy. - Strong problem-solving skills. - Ability to work with large numeric datasets. Looking forward to your bids!

...coordinate-based identifiers within the map. * **Adjacency & Contiguous Logic:** A specialized filter ensuring the system only secures "neighboring/side-by-side" items to keep groups together. **Technical Stack & Core Module Structure:** The framework is a high-performance, asynchronous Python 3.13 environment: * **Stack:** asyncio, curl_cffi (for TLS/JA3 impersonation), orjson (fast serialization), and numpy (for byte-shift decoding of obfuscated server responses). * **Core Structure:** * **Socket Management Layer:** Manages persistent, low-latency WebSocket connections and packet multiplexing. * **API Orchestration Layer:** Handles secure REST requests and JSON response parsing. * **Distribution Engine:** The main logic for Master/Receiver synchron...

...specialist to turn my methodology into clean, reproducible code. The core help I’m after is coding itself—covering the full pipeline from data preprocessing through model training to final evaluation and visualisation. I need datasets, well-documented Python scripts or notebooks that I can run end-to-end on my own machine (or a Colab instance). Expect to work with common libraries such as pandas, NumPy, PyTorch or TensorFlow, Hugging Face Transformers, plus Matplotlib or Seaborn for charts—use whichever combination best suits the objectives while keeping dependencies manageable. Deliverables
 • Data preprocessing module that loads the provided datasets, cleans them, applies any necessary tokenisation and splits them into train/validation/test sets. &bul...

...the taper, the twist, and the elastic root support while the tip remains free. Your task is to compute the natural frequencies and mode shapes across a practical range of rotational speeds, capturing the combined influence of centrifugal force, Coriolis coupling, and rotational stiffening. Please work directly from the matrix form I provide, or recreate it in the tool you prefer (MATLAB, Python/Numpy-SciPy, ANSYS APDL, or comparable). Numerical stability and correct treatment of the gyroscopic terms are essential; results should clearly show how the modes split or couple as speed increases. Deliverables • Clean, well-commented code or solver input deck that assembles M, G, and the individual stiffness matrices and solves the resulting eigenproblem. • A concise tec...

...preferred) that takes raw user transaction data, cleans it, applies regression techniques, and then produces a short-term forecast I can cite in investor conversations. Because time is critical, I’m aiming to move quickly—ASAP means I will review work as soon as it comes in and provide immediate feedback. Here is what success looks like for me: • A well-commented Colab notebook using pandas, NumPy, scikit-learn or statsmodels for the regression and forecasting sections. Every major step—from missing-value handling to model evaluation—needs a clear markdown explanation that a non-technical stakeholder could follow. • Actionable insights tied directly to the product idea: for example, what spending behaviours you uncovered, how regression output...

...script (or self-contained package) should be able to ingest a raw dataset from a file path or stream, apply a clear transformation pipeline that I can modify later, and write the cleaned, structured results back out. The exact data format is still flexible—I’ll provide sample files once we start—so please write modular code that separates the I/O layer from the core transformation logic. Pandas, NumPy, or similar libraries are welcome as long as they are the right tool for the job and you keep external dependencies to a minimum. My priorities are: • Readable, well-commented code that follows PEP 8 • A concise README explaining setup, usage, and how to extend or swap in other data sources • At least one unit test demonstrating that the ...

...should open and close positions within the same session, respect configurable risk parameters, and support rapid position sizing, stop-loss, and take-profit logic. • Exchange connectivity: everything will run on Alpaca for now, but clean modular code will let me extend to Binance, Coinbase, Kraken, or any other REST/WebSocket venue later. I prefer Python, leveraging libraries such as pandas, NumPy, TA-Lib, backtrader (or similar) for strategy logic and backtesting, plus WebSocket streams for real-time ticks. If you can propose a more robust stack that still keeps hosting simple—FastAPI for a lightweight dashboard, Docker for deployment—that’s welcome. Deliverables • Well-structured, documented source code (Git-friendly). • Config file wh...

...Segmentation using modern architectures (UNet with ResNet34 backbone). Hands-on experience with satellite/remote sensing data and real-time ML deployment. Ability to take a complex AI problem from raw data → trained model → live production API. Practical skills in building solutions that solve actual environmental and disaster-related challenges. Technologies Used: PyTorch, UNet-ResNet34, FastAPI, NumPy, Matplotlib, OpenCV, Albumentations, AWS (deployment-ready) Live Demo / GitHub:

Title: Data Analysis Project Using Python (NumPy & Pandas) – Soccer Dataset Description I am a motivated data analyst and engineering student with hands-on experience in Python-based data analysis. I recently completed a comprehensive data analysis project focused on a real-world soccer dataset, applying powerful libraries such as NumPy and Pandas. In this project, I performed: * Data cleaning and preprocessing (handling missing values, correcting data types) * Exploratory Data Analysis (EDA) to uncover patterns and trends * Statistical analysis to answer key business and performance questions * Data visualization to present insights clearly and effectively * Structured reporting following professional data analysis standards This project demonstrates my ability...

...Python developer for long-term collaboration to support automation and data visualization tasks. The work will involve automating repetitive data processes, building Python scripts for analysis, and creating interactive and insightful visualizations to support business decision-making. The ideal freelancer should have strong experience in Python programming, working with libraries like Pandas, NumPy, Matplotlib, Seaborn, and optionally Plotly or Dash for interactive dashboards. Familiarity with handling large or messy datasets and delivering clean, reproducible code is essential. Scope of Work: Automate repetitive tasks and data workflows using Python Clean, process, and transform datasets Generate visualizations and summary reports for insights Maintain organized, documented ...

I already have several Python trading strategies coded and tested in isolation. What I need now is a clean, production-ready pipeline that does three things seamlessly: lets me back-test those strategies on historical data, pushes selected ones live through Zerodha Kite, and keeps an eye on real-time performance. The stack is non-negotiable: Pandas and NumPy for data wrangling, Backtrader as the core engine, and Zerodha’s Kite Connect/WebSocket API for brokerage connectivity. Wherever latency can be shaved, I’d like to see Numba or Cython used for just-in-time speed-ups, especially inside tight loops. The live leg must subscribe to tick data over WebSockets, place/modify orders, and stream portfolio updates without choking. Deliverables • Unified codebase that plu...

...completed. (Task durations = 1-3 hours max) What You’ll Do Design and run A/B experiments and evaluate model behavior. Build/audit data workflows and pipelines (dbt, SQL). Analyze datasets and communicate findings for data-driven decisions. Work with LLM benchmarking and agentic coding workflows. What You Need 4+ years professional experience in DS, ML, or Data Engineering. Expert Python (pandas, NumPy, scikit-learn) and SQL. Proven ability to diagnose ML failure modes and improve model quality. Familiarity with cloud warehouses (Snowflake, BigQuery, or Redshift). Why this role? This is a flexible, remote engagement perfect for those looking to contribute to cutting-edge AI research and work with top-tier industry labs without the commitment of a full-time product role.....

...script (or Jupyter notebook) that loads the dataset, performs the requested exploratory analysis, applies any statistical tests or visualisations called for in the brief, and prints or plots clear results. • Inline comments that clarify the logic, plus a short README or markdown cell summarising how to run the code and interpret the outputs. • Use of standard data-science libraries—pandas, NumPy and Matplotlib/Seaborn are fine unless you suggest better alternatives that still install easily with pip. Acceptance criteria 1. Runs without modification on a fresh Python 3.10+ environment after a simple pip install -r requirements.txt. 2. Passes all stated rubric tests (I’ll share them up front). 3. Code is logically structured, PEP-8 compliant and under...