Pandas işler

...ve layout temizliği için kural tabanlı + gerekirse ML tabanlı pipeline oluşturmak. Kalite ölçütlerini (token sayısı, Türkçe oranı, örnekleme QC raporları) tanımlayıp düzenli raporlamak. NLP eğitim ekiplerine teslim edilecek final dataset (jsonl / parquet) ve dokümantasyon üretmek. Aradığımız kişi: Büyük ölçekli metin temizleme / data wrangling tecrübesi (en az 3 yıl). Python ekosistemine hâkim; pandas, multiprocessing, regex, BeautifulSoup, ftfy, spaCy/FastText tabanlı dil tespiti, vs. Tercihen Spark/Dask veya benzer dağıtık sistem deneyimi. Türkçe dil yapısına aşinalık ayrıca avantaj. Veriden rapor üretebilen (ör. Jupyter, Metabase) ve pipeline’ı otomasyona bağla...

...Gereksinimler: - Python - BeautifulSoup - Requests - Pandas - Matplotlib #### Adımlar: 1. **Veri Toplama:** - Web scraping ile bir hava durumu sitesinden veri çekmek. - Örneğin: `` 2. **Veri İşleme:** - Çekilen verileri anlamlandırmak ve Pandas DataFrame'ine dönüştürmek. - Gereksiz bilgileri temizlemek ve analiz için gerekli olanları seçmek. 3. **Veri Analizi:** - Günlük ortalama sıcaklık, nem oranı gibi bilgileri hesaplamak. - Zaman içinde hava durumu değişikliklerini gözlemlemek. 4. **Veri Görselleştirme:** - Matplotlib kullanarak verileri görselleştirmek. - Örneğin, belirli bir süre için sıcaklık değişim grafiği oluşturmak. #### Örnek K...

Python da (pandas) exceli okuyarak tabloya atmak. sonrasında bazı kolon ve rowlarda işlemler yaparak (örneğin excelde filtreleme, vlookup gibi) yeni tablolar olusturmak ve bu tabloları yeni bir excele atmak. eğer çok iyi biliniyorsa bazı grafikler ve social network analizi de isteyebilirim. Reading excel with python (pandas), making some adjustments in the data like sorting or vlookup or some text mining. then extracting each table to the new excel files.

...document or answer instantly, and feel as if they are speaking with a well-trained human agent. Alongside the chat experience, I need an end-to-end AI pipeline that automatically extracts raw data from the web, aggregates and cleans it, performs analysis, and then publishes clear visualisations—including map views—so insights are always one step away. I’m comfortable with tools such as Python, Pandas, LangChain, Node, SQL, Power BI, Tableau, or any similar stack you can justify. Key deliverables • Deployed WhatsApp agent(s) connected through the WhatsApp Business API - WhatsApp channel is ready. • Retrieval-augmented knowledge base so the bots surface the latest information without hallucinations .. Critical • Automated ETL jobs (n8n, Airfl...

...three pillars inside a single, well-structured repository: • Automated trading – place, modify, and cancel orders programmatically (market, limit, SL). The engine should be able to run multiple strategies concurrently and execute through Zerodha’s regular and MIS product types. • Market analysis – fetch historical and live tick data, calculate technical indicators on the fly (you’re free to use pandas, numpy, TA-Lib, or your own functions), and generate entry/exit signals according to parameters I will supply. • Risk management – position sizing, max daily drawdown, per-trade stop-loss, overall exposure caps, and a safety kill-switch if connectivity or margin issues arise. I will provide API credentials and strategy logic; your job...

...analysis drawn from the cleaned data. The core of the job is data analysis, with a focus on market analysis. Once the information is re-entered and verified, I’d like you to identify key market trends hidden in the numbers—patterns in customer behaviour, shifts in demand, emerging opportunities, and any risks you notice. Feel free to use Excel, Google Sheets, or your preferred tools (Python + Pandas or SQL are fine too) as long as the final files remain easy for me to open and follow. Deliverables • A fully re-typed and proof-checked spreadsheet, aligned with my existing column structure • A concise market analysis report (slides or doc) that explains the trends you found, the methods you used, and your recommendations I’m available for qu...

Excel & Python Data Analysis for Sales and Operations ...cleaning and validation, and building Python-based analysis scripts to calculate KPIs such as revenue trends, inventory turnover, order fulfillment rates, and cost variations. The final output should include well-structured Excel reports and optional visual summaries generated via Python. Key Requirements: Strong experience with Excel (advanced formulas, pivot tables, data validation) and Python (Pandas, NumPy, Matplotlib or similar). Ability to handle messy datasets, automate repetitive analysis, and ensure data accuracy. Deliverables: Cleaned datasets, analysis scripts, summary reports, and clear documentation explaining the workflow. This project is ideal for someone who enjoys turning raw data into actionable insight...

...different methods agree or diverge. • At least one live session (Zoom, Meet, or similar) to walk me through your approach; the project involves explanations that are too involved for chat alone. • A concise technical memo or slide deck summarising conclusions and recommended next steps. Acceptance criteria – Code runs end-to-end on a fresh environment with standard libraries (scikit-learn, pandas, numpy, matplotlib/plotly, shap, lime, etc.). – Plots and tables directly link back to the underlying calculations, allowing me to reproduce every number. – During our live review you can clearly justify each methodological choice and articulate trade-offs between SHAP, LIME, PDP and ICE for the three target models. This assignment is scoped as a f...

...hours a day with collecting, cleaning, and summarising data for several market intelligence projects. You’ll log into shared Google Sheets, pull information from public sources or APIs, verify accuracy, and present concise insights that let me make quick decisions. Experience with Excel or Sheets functions (LOOKUPs, pivot tables, basic charts) and any lightweight statistical tool such as Python/pandas or R is a plus, but solid attention to detail matters most. I’ll set clear weekly objectives up front. Typical deliverables include: • A cleaned spreadsheet ready for analysis • A brief written summary (no more than one page) highlighting key findings and anomalies Before we start, send a short note telling me your favourite dataset you’ve worked on...

I need to turn a tangle of monthly transaction files into one clean, consistent source of truth. The job touches three tools: • Python – Write well-structured scripts (pandas, openpyxl, maybe pyodbc) that handle data cleaning, exploratory analysis, and the recurring automation needed to pull from / push to my databases and workbooks. • MS Access – Build and fine-tune queries, keep the tables organised, and set up ready-to-run reports that mirror the logic in the Python workflow. • MS Excel – Craft the right formulas, dynamic pivot tables, and clear visualisations so stakeholders can slice the data without touching code. The core flow should be: ingest raw Excel dumps, clean and reconcile them in Python, store the harmonised tables in Access, th...

...and connects to my broker’s API out of the box. • Parameter file or dashboard so I can tweak risk limits, symbols, and indicator thresholds. • Back-test results on one year of one-minute data plus a walk-forward test on the last 90 trading days, both clearly outperforming buy-and-hold under equal risk. • Quick-start guide and a brief screen-share hand-off. Tech stack is open—Python with pandas and TA-Lib is fine, but if you prefer something lower-latency like C++ or a paired Python/C++ engine, I’m game as long as installation is straightforward on a standard U.S. VPS. I’m ready to begin as soon as I find the right coder-trader hybrid. Send a short note describing your live trading experience and one or two algos you’ve built th...

...hours a day with collecting, cleaning, and summarising data for several market intelligence projects. You’ll log into shared Google Sheets, pull information from public sources or APIs, verify accuracy, and present concise insights that let me make quick decisions. Experience with Excel or Sheets functions (LOOKUPs, pivot tables, basic charts) and any lightweight statistical tool such as Python/pandas or R is a plus, but solid attention to detail matters most. I’ll set clear weekly objectives up front. Typical deliverables include: • A cleaned spreadsheet ready for analysis • A brief written summary (no more than one page) highlighting key findings and anomalies Before we start, send a short note telling me your favourite dataset you’ve worked on...

...straight into my existing systems. My current bottleneck is doing this by hand, so the solution must run unattended once configured. Although my immediate goal is straightforward data processing, I’m open to seeing how you’d weave in text extraction, transformation, or even lightweight analysis if that simplifies the pipeline or adds value. I work comfortably with Python, so a solution built with pandas, spaCy, or similar libraries would fit right in, but I’m not married to any single toolset as long as the final deliverable is easy for me to maintain. Deliverables • Source code and any dependency list • A short README explaining setup, configuration variables, and how to run the job • One worked example showing the raw input and the process...

...user and for the population as a whole. • Compute entropy (Shannon or Rényi—please justify your choice) across sliding and tumbling windows so we can compare immediate behaviour to longer-term baselines. • Raise an alert when an entropy shift exceeds a configurable threshold, returning the supporting metrics and the related raw events. I expect well-structured, runnable code—Python with pandas/NumPy/SciPy is typical, though another language is fine if it delivers the same reproducible results—along with a concise README that shows how to install dependencies, feed sample logs, and interpret the output. Success is measured by: • Clean execution on my sample dataset (≈1 GB of mixed user activity). • Alerts that capture ...

...simple list of all business names found at that spot (no tables, no extra formatting). Key points you should respect: • The CSV is finished and ready to ingest as-is. • If more than one business is tied to an address, keep every name. • Output in Google Docs: one address followed by its full list, then move on to the next address. I’m open to whatever stack feels most efficient—Python with pandas, Google Apps Script, Zapier, , or a custom API integration—as long as the final flow can run on demand and is documented well enough that I can swap in a new CSV whenever needed. Deliverables I expect: • Working automation (script, scenario, or add-on) that accepts my CSV and produces the Google Doc exactly as described. • Brief setup/u...

...─── 12. RECOMMENDED TECH STACK ──────────────────────────────────────── ### Backend • Node.js (NestJS) OR Laravel • REST + WebSockets • PostgreSQL • Redis ### Fleet Core • Fleetbase (extended via plugins) ### Frontend (Web Dashboards) • React.js or • Tailwind CSS • Mapbox or OpenStreetMap ### Mobile Apps • Flutter (preferred) OR React Native ### AI & Analytics • Python • FastAPI • Pandas • Scikit-learn / TensorFlow (later phase) ### Payments • Stripe SDK • PayPal SDK • CMI API ### DevOps • Docker • CI/CD • Cloud hosting (AWS / GCP / Azure) ### Design • Figma (mandatory) • Design System • UX focused on minimal interaction ─────────────────────────────────────...

I have a collection of raw Excel spreadsheets that I need turned into clear, decision-ready insights. The work starts with cleaning and structuring the sheets, then moves into exploratory analysis and the creation of easy-to-read visuals and a concise written summary of the findings. You are free to use the tools you work fastest in—Python (pandas, NumPy, matplotlib / seaborn), R, Power BI or even advanced Excel formulas and Power Query—so long as the final deliverables are returned in both an annotated workbook and a slide-ready PDF or dashboard that a non-technical stakeholder can understand. Accuracy, transparent methodology (well-commented code or documented steps), and a short call or Loom video walking through the results will complete the engagement.

...are straightforward TXT—no embedded markup or JSON structures—so the solution can focus entirely on pattern analysis rather than parsing exotic formats. Because the anomalies are behavioural rather than simply extreme numeric values, the detection logic must look for irregular sequences, unexpected combinations of fields, or sudden structural deviations. I’m happy with a Python‐based approach (pandas, scikit-learn, PyOD, or similar libraries come to mind), but I’m open to another language if it keeps the setup lightweight and easily deployable on a Linux server. Speed matters: each hourly log is time-stamped and should be processed within the hour so downstream analytics always work with clean data. Your script or small tool should run from the command...

I have a raw set of market data that needs to be turned into clear, actionable insights as quickly as possible. The sole objective is to uncover trends—seasonal patterns, growth pockets, emerging segments, anomalies—anything that can sharpen my next strategic move. You will receive t...obvious and hidden trends. • Visualize the findings (Tableau, Power BI, or even polished Excel charts are fine) so they are easy to grasp at a glance. • Deliver a concise report summarizing the trends, your methodology, and any recommendations worth noting. Speed matters: I’m looking for an ASAP turnaround without sacrificing accuracy. If you’re comfortable diving straight in with Python (pandas, NumPy, seaborn) or R, and you have a talent for presenting insight...

...establish a Volume SMA baseline. ​Real-Time Trigger Logic (1-Min Candles): ​Value: Candle Value (LTP × Volume) ge ₹10 Crore. ​Volume: Current 1-min Volume ge 10x the 2,000-candle SMA. ​Momentum: Price change in the current 1-min candle ge 0.3%. ​Scan Frequency: Refresh the current forming candle every 20 seconds. ​Dashboard & Persistence: ​Display results in a tabular format (using PrettyTable or Pandas) with a timestamp (hh:mm:ss). ​All stocks that satisfy the criteria must be stored in a persistent list for that day's session (no separate history tab needed). ​One-Click Charting: ​When a stock appears in the table, it must be clickable or easily selectable (e.g., via a keyboard shortcut) to instantly open the corresponding Fyers TradingView chart in the defaul...

I’m refining a series of logistic-regression models i...Hessian output for standard-error estimation. • Suggest parameter-regularisation strategies available directly in SciPy or via light dependencies I can bolt on. • Join one or two live screen-share sessions (30-45 min each) so we can step through residual plots, goodness-of-fit tests, and any edge-case handling. All work will happen in a clean Python 3.11 environment with NumPy, SciPy 1.11, Pandas, and Matplotlib already installed, so no need for heavy-weight ML frameworks. Deliverables are the commented revisions to my script plus a concise summary of the changes and reasoning. If you’ve previously tuned logistic models in SciPy (not just scikit-learn) and enjoy explaining the “why” as mu...

Saya memiliki kumpulan data penjualan yang perlu dianalisis secara menyelu...waktu. • Visualisasi: sajikan temuan dalam grafik atau dashboard interaktif yang mudah dipahami. • Insight & rekomendasi: rangkum hasil analisis dengan penjelasan jelas serta saran strategis yang dapat langsung diimplementasikan. Dataset tersedia dalam format Excel/CSV dan dapat saya kirim segera setelah proyek dimulai. Saya terbuka menggunakan alat pilihan Anda—Excel, Google Sheets, Python (pandas), atau Power BI—selama hasil akhir akurat dan mudah dipresentasikan kepada tim manajemen. Silakan ajukan proposal singkat berisi metode analisis yang Anda rencanakan, perkiraan waktu penyelesaian, serta contoh ringkas proyek serupa jika ada. Saya menghargai komunikasi proaktif dan ...

...file (again, PDF, Word, or Excel). 3. System parses, maps terms, and asks me to confirm any uncertain matches. 4. It outputs a clean, well-formatted Excel workbook showing requirement vs. each vendor’s offer, along with an automated “best-fit” recommendation. Future iterations may add scoring weightings, but the first milestone is the Excel comparison itself. Tech is up to you—Python with pandas, open-source NLP libraries such as spaCy or transformers, or a lightweight web front end that calls a backend service. Whatever stack you choose, the deliverable must run on a standard Windows machine or be easily deployed to a small cloud instance. Acceptance criteria • Uploads accepted: PDF, Word, Excel. • Comparative sheet created in .xlsx wi...

Hello, my name is fathalishah. i am from afghanistan i have been working for 3 years as full stack developer which can i work with html ,css,javascript,react,tailwindcss and bootstrap and backend i can work with Python+django django-restframe api , sql,mysql, and as desktop developer i can work with tkinter and also i can work as data scientest work with NUMPY ,PANDAS,scikit-learn,Matplotlib i hope i can do my best for you Thanks..

...is to move everything into the standardized Excel import template required by our new SAP environment without typing line-by-line. I would like an automated solution—preferably a lightweight AI-assisted script or model—that can: 1. read the existing Excel files, 2. map each column to the exact fields SAP expects, and 3. export a clean, ready-to-upload template with zero data loss. Python (pandas / openpyxl), VBA, or a low-code Power Query setup are all acceptable as long as the end result is fast and repeatable for future batches. Built-in data checks for duplicates, negative balances, or mismatched contract IDs will be essential so we catch issues before the SAP import stage. Deliverables • Reusable script or macro with clear in-line comments • O...

...et des scripts Python ou R pour automatiser les extractions ; • élaborez des visualisations interactives dans Power BI ou Tableau afin de suivre nos indicateurs clés ; • recommandez des actions à partir des tendances détectées. Je fournis l’accès aux bases, la documentation technique et un canal Slack dédié. De votre côté, j’attends : – une expérience avérée en analyse de données (Python / Pandas, SQL, outils de BI) ; – la capacité à livrer des synthèses courtes et précises chaque semaine ; – un engagement régulier, 15 à 20 heures réparties comme vous le souhaitez. Si vous aimez explore...

...establish a Volume SMA baseline. ​Real-Time Trigger Logic (1-Min Candles): ​Value: Candle Value (LTP × Volume) ge ₹10 Crore. ​Volume: Current 1-min Volume ge 10x the 2,000-candle SMA. ​Momentum: Price change in the current 1-min candle ge 0.3%. ​Scan Frequency: Refresh the current forming candle every 20 seconds. ​Dashboard & Persistence: ​Display results in a tabular format (using PrettyTable or Pandas) with a timestamp (hh:mm:ss). ​All stocks that satisfy the criteria must be stored in a persistent list for that day's session (no separate history tab needed). ​One-Click Charting: ​When a stock appears in the table, it must be clickable or easily selectable (e.g., via a keyboard shortcut) to instantly open the corresponding Fyers TradingView chart in the defaul...

...CSV, and PDF files * Clean and normalize messy real-world data * Write clear, maintainable utility scripts * Deliver working code (not just prototypes) --- ### Required Skills * Strong Python fundamentals * Real experience with web scraping * Data parsing and data cleaning * Comfortable working independently and async --- ### Nice to Have * BeautifulSoup, Scrapy, Playwright, or Selenium * pandas / numpy * Experience scraping government or legacy websites * Experience handling PDFs (text extraction, OCR) --- ### How We Evaluate * This role includes a **paid trial task (1–3 days)** * We care about **output and correctness**, not resumes * Clean, working code matters more than clever abstractions --- ### Important * Please include **2–3 sentences** describing...

I’m ready to bring the same structure and depth I saw in BCG’s virtual consulting exercise into my own finance function. You’ll have direct access to our raw sales data (CSV export from our ERP), and your brief is to surface two concrete insight streams: • Cl...we can refine cash-flow planning, discounting, and inventory strategy Once the analytics are in place, I’ll need a short slide deck or memo that converts those findings into finance-focused recommendations—for example, timing large expenditures, reshaping payment terms, or reallocating working capital. I’m comfortable with whichever tool chain suits you best (Excel, Power BI, Tableau, Python pandas). What matters is transparent methodology, reproducible calculations, and visuals tha...

I have a stack of 65 supplier invoices sitting in PDF format, each listing multiple SKUs and quantities. I need those PDFs processed so that, for every unique SKU, I can see the running total qu...can see the running total quantity across all invoices. The final result should land in a clean, flat Excel / CSV file ready for upload into my inventory system. Besides the SKU and its consolidated quantity, please include the related invoice date so I can quickly trace back any discrepancies later. No other fields are required. However you choose to handle the PDFs—whether with Python, pandas, OCR, or dedicated PDF-parsing libraries—accuracy is critical. I should be able to open one spreadsheet, filter by SKU, and instantly know how many units are on hand according to s...

...a Python script is throwing errors whenever it calls certain library functions. I need a sharp eye to dive into the code, pinpoint the exact cause, and leave me with a clean, reproducible fix. Here’s the situation as clearly as I can put it: • The faulty piece lives in a Python script that glues together key stages of the workflow. • The issue surfaces inside library-level calls—think NumPy, pandas, scikit-learn, TensorFlow, or similar. • Once corrected, the rest of the training and inference steps should run without manual work-arounds. What I’m expecting from you • A patched script that runs end-to-end on my side. • A concise changelog or inline comments explaining what broke and why your fix works. • Any environment twe...

...Module 3 — SELL STRATEGY ENGINE Handles only short trades. Examples of rules: Price below VWAP Breakdown patterns Bearish flag Volume breakdown Entry, SL, Target logic Output: SELL signals only Module 4 — AlgoTest Strategy Formatting Convert BUY signals → Buy strategy format Convert SELL signals → Sell strategy format Backtest-ready rule structure Technical Requirements ✔ Python (Pandas, NumPy) ✔ TA indicators (VWAP, EMA, Volume) ✔ Modular coding structure ✔ Knowledge of AlgoTest rule system ✔ Experience in intraday trading logic Deliverables : Separate Python files: Chartink stock fetch script Strategy logic documentation Editable conditions Setup guide Note : Payment will be released only after successful setup and testing on ...

...parsing, and structured export—and then hand me a ready-to-use CSV or JSON. I will share a sample dataset and the exact transformation rules once we start, but in broad strokes the script should: • run from the command line with a few clear arguments or a simple config file • handle varying file sizes without choking on memory • rely only on standard libraries or popular, easily installed ones (pandas, regex, nltk, etc.)—list any extras in • log basic progress so I can trace issues quickly • exit gracefully on bad input and flag the error rather than crash Deliverables 1. The Python (.py) script, fully commented. 2. with exact versions if you add dependencies. 3. A short README showing setup, one-line usage, and an example of ...

...project has started throwing errors, and I need it back in working order within 48 hours. The bug sits somewhere between the data-loading step and model training; once you jump in you’ll have full access to the Python code, sample data, and the original (working) training logs so you can compare behaviours. I work entirely in Python and TensorFlow, with a handful of standard utilities such as NumPy, pandas, and Matplotlib. No other frameworks are involved, so deep familiarity with TensorFlow’s debugging tools, graph/symbolic APIs, and eager execution will let you move quickly. Here is what I expect at hand-off: • A corrected script that runs start-to-finish on my machine (TensorFlow 2.x, Python 3.9). • A concise changelog or inline comments so I can see ...

...PostgreSQL database, then turns the insights into clear, interactive visuals and downloadable reports. Core scope • Build the Django backend, including models that capture standard patient information, visit history, diagnostics, medications, and lab results. • Create a secure REST or GraphQL layer so new records can be ingested from our existing EHR feed. • Process the incoming data with Pandas or a comparable library, preparing it for analysis. • Develop dashboards that allow clinicians to explore trends (e.g., vitals over time, medication adherence) using Plotly, , or another modern JS charting tool. • Implement one-click PDF/CSV report generation per patient and for cohort summaries. Key requirements – The entire stack must be co...

I need a skilled analyst who can dive into my sales data and surface clear, time-based trends using a mix of SQL and Python. The raw figures sit in a relational database; from there you’ll write efficient SQL queries, pull the results into Python (pandas, NumPy, SQLAlchemy), tidy everything, then explore daily, weekly, and monthly patterns. Visualisations in matplotlib or seaborn and a concise written summary (PDF or Jupyter Notebook) will round out the story so stakeholders can quickly grasp how revenue is evolving and where seasonality or anomalies appear. Deliverables • Well-commented SQL scripts that reproduce the dataset • A Python notebook (or .py script) with all transformation and analysis steps • Three to five clear charts illustrating sales tren...

...entries—that needs exhaustive permutation matching. The goal is to scan every possible combination quickly in Python, identify all matches (or near-matches if you choose to add fuzzy logic), and return the results in a single Excel file ready for immediate analysis. Raw data and a short spec on what constitutes a “match” will be supplied; your task is to design an efficient, memory-savvy routine (pandas, NumPy, itertools, or any other high-performance approach you prefer) that can churn through roughly one hundred million comparisons without freezing up my workstation. Multithreading, vectorisation, or chunked processing—all are acceptable so long as they keep runtime practical and the output accurate. Deliverables • A well-commented Python script...

...Module 3 — SELL STRATEGY ENGINE Handles only short trades. Examples of rules: Price below VWAP Breakdown patterns Bearish flag Volume breakdown Entry, SL, Target logic Output: SELL signals only Module 4 — AlgoTest Strategy Formatting Convert BUY signals → Buy strategy format Convert SELL signals → Sell strategy format Backtest-ready rule structure Technical Requirements ✔ Python (Pandas, NumPy) ✔ TA indicators (VWAP, EMA, Volume) ✔ Modular coding structure ✔ Knowledge of AlgoTest rule system ✔ Experience in intraday trading logic Deliverables : Separate Python files: Chartink stock fetch script Strategy logic documentation Editable conditions Setup guide Note : Payment will be released only after successful setup and testing on ...

...side, the agent will read incoming tickets, suggest or send context-aware replies, and escalate anything outside predefined confidence levels. At the close of each cycle it will compile a concise report: updated record counts, uptime metrics, customer-support KPIs, and a short trend analysis showing spikes or deviations. A Python stack with REST integration, async handling, and libraries such as pandas, LangChain or similar for LLM prompts would suit us, but I’m open to other reliable options if they meet the same goals. Execution can be triggered by cron, a lightweight scheduler, or a serverless function; what matters is that it runs unattended and keeps detailed logs. Deliverables • Production-ready agent script(s) with clear setup instructions • API inter...

I'm a fundamental analyst looking to automate my trading strategy to improve discipline and ...Automates entry/exit decisions based on my rules 3. Shows real-time charts and portfolio status 4. Manages risk with simple safety rules 5. Runs reliably on my computer Technology Requirements Essential Stack · Backend: FastAPI (for async handling) · Database: SQLite (with basic persistence) · Frontend: Simple dashboard with TradingView charts · Broker API: DhanHQ (Indian broker) · Libraries: pandas for calculations System Architecture ``` Simple Personal Trading System: 1. Data Layer: Fetches prices from DhanHQ 2. Logic Layer: Applies my trading rules 3. Execution Layer: Places orders (optional paper mode) 4. Local Web based or something simila...

...Monitoring and Model Reliability Implement hooks or interfaces for: Model performance tracking (e.g., MAPE, error distributions). Data drift and feature distribution changes. Data freshness and pipeline execution health. Inference latency and failure rates. A full MLOps stack is not required, but production-awareness is mandatory. Technical Requirements Python (production-quality code, not notebooks) Pandas, NumPy scikit-learn, CatBoost, XGBoost, or equivalent Time-series forecasting techniques SQL for intermediate storage or aggregation (if applicable) REST API framework (FastAPI or similar) Experience designing multi-tenant data systems Cloud provider and infrastructure details are flexible. Deliverables: Modular Python codebase covering: Data ingestion and validation Config-d...

...depreciation report, and the underlying data file reflects the same numbers. Deliverables 1. Fully commented Python code with a brief README explaining any third-party libraries. 2. A sample ledger template populated with dummy data so I can test before feeding live numbers. 3. Instructions for installing dependencies and running on Windows 10. Use whatever modern Python tooling you prefer—pandas, openpyxl, sqlite, or a lean custom parser—as long as setup stays simple. If the above flow works end-to-end, the job is done....

...extracted from them. The work begins with consolidating the raw CSV files, cleaning inconsistent or missing values, and structuring the data so it is ready for analysis. From there, the core task is to uncover how key customer-related metrics have shifted—month-over-month, quarter-over-quarter, or year-over-year—and to highlight any significant growth or decline patterns that surface. Python with pandas (or R if you prefer) is perfectly fine for the data preparation steps; feel free to bring in SQL for joins or look-ups if it expedites the process. For the analysis itself, I expect visualisations—line graphs, heat-maps, or cohort-style charts—generated in Matplotlib, Seaborn, Power BI, Tableau, or a comparable tool. Choose whichever gives the clearest sto...

...social engagement and any other fields stored in our CRM. Everything will be shared in its original format so you can dive straight into exploration. What I need is a complete analytical workflow: data cleaning, feature engineering, exploratory insights and, most importantly, a predictive model that flags patterns worth acting on. I am comfortable with you choosing proven tools such as Python (pandas, scikit-learn), R, SQL or similar; visual output in Tableau or Power BI is welcome if it speeds up interpretation. Deliverables • A documented notebook or script that ingests the full dataset and produces a trained model ready for deployment • A concise report (slides or PDF) highlighting key drivers behind the forecast and actionable recommendations • Optional...

...through every page of results, and export the data to a single-sheet .xls workbook. Required columns • Ministry Name • Date • Member name • Session No • Question No • Answer Link (the full URL for the answer PDF or page) Please have the code automatically iterate through all available result pages so nothing is missed, then save everything in row order to one sheet named “Questions”. Pandas, BeautifulSoup, requests, xlwt, or Selenium are all fine as long as the final file is a standard .xls that opens in Excel without warnings. Deliverables 1. The .py file, clearly commented so I can modify the URL later if needed. 2. A sample .xls generated by the script, showing that every question currently on the site has been captu...

...correlation, or logistic regression) to reveal which knowledge or attitude variables significantly predict donation decisions. • A concise written report (Word or PDF) that explains methods, key findings, and practical implications, with clearly annotated tables and graphs that I can drop straight into a manuscript or presentation. • The analysis script (R, SPSS syntax, Stata do-file, or Python/pandas—use whichever you’re most comfortable with) so results are fully reproducible. Everything should tie back to the core research question: how do knowledge and attitudes among healthcare professionals translate into actual donation intention or behavior? If you see additional tests or visualisations that would clarify that relationship, feel free to includ...

...and API errors; restart should pick up open positions correctly. • Clear, timestamped logging to CSV or SQLite for fills, PnL and key signals. • Simple config file for API keys, instrument whitelist, risk caps per trade and per session. Deliverables 1. Python 3.x source with inline comments and docstrings. 2. Quick-start README explaining environment setup, required packages (requests, pandas, numpy, ccxt or native DeltaExchange SDK, whichever you prefer) and example configuration. 3. A short back-test notebook or script that demonstrates the momentum logic on historical exchange data so I can validate its edge before switching to live mode. 4. One short hand-off call or text walkthrough to ensure I can run, tweak and extend the bot on my VPS. If you have ...

...datasets that need a careful sweep so every value left in the file genuinely reflects reality. My only objective is to boost overall data accuracy, and I want to follow a clear-cut, statistical approach: any record sitting outside a set number of standard deviations from the mean should be flagged and deleted outright—no replacement, no imputation. You’re free to work in Excel, Google Sheets, Python (pandas / NumPy), R, or any tool you trust, so long as the final files return in the same structure and encoding they arrived in. A concise log of how many rows were dropped per file will help me double-check the results. Deliverables • Cleaned CSV files, identical column order and headers • A brief summary (CSV or TXT) listing for each source file: total r...

...mind. I specify a ticker (or a list of tickers) plus a date range, hit “run,” and the tool reaches out to a reliable data source, fetches the daily OHLC values, volume, adjusted closes, and spits everything out in CSV or JSON. A concise command-line interface or a simple GUI is fine; what matters most is accuracy, resilience, and ease of use. I’m agnostic about language, but Python with requests, pandas, and perhaps yfinance or a direct exchange API feels natural. If you prefer Node, Go, or another language, that’s okay—just make sure the end result is well-documented and easy to deploy on a standard Linux server. Deliverables (acceptance criteria) • Fully commented source code and requirements file • A short README explaining setup, ...

...demographics, maps behavioural segments, and highlights high-value cohorts. • Benchmarks our performance against key competitors and quantifies where we are winning or losing ground. • Detects wider market trends that could influence product positioning and future growth. I will share the CSV files plus any supporting market-research PDFs. Use the tools you are most comfortable with—Python (Pandas, NumPy, scikit-learn), R, SQL, Tableau or Power BI—so long as the code, queries, and dashboards are reproducible. Deliverables: 1. Cleaned, well-documented dataset. 2. Jupyter notebooks or R scripts with comments explaining each step. 3. Interactive dashboard for leadership to drill into findings. 4. Concise PDF/Slides report summarising key metrics, vi...