Pandas jobs

...classic NLP cleaning—then let a Naive Bayes model trained on TF-IDF features decide what is junk and what I actually want to see. I will be working exclusively with publicly available email corpora, so nothing proprietary needs to be handled. Key pre-processing I need implemented: • Tokenization • Stopwords removal • Stemming or lemmatization The stack is already chosen: Scikit-learn, Pandas and NumPy under Python. Once training is complete, I’d like a concise script or notebook that can: 1. Load a fresh batch of .eml or plaintext messages. 2. Output a CSV (or similar) labeling each as spam or ham. 3. Report precision, recall and F1 on a held-out test split so I can quickly judge performance. Deliverables are the cleaned, well-commented...

I'm looking for a Python developer to create an automation tool specifically for financial data. This tool will not only automate data handling but also provide comprehensive analysis. Key Features: - Data Visualization: Create intuitive and interactive visualizations. - Repo...metrics. - Predictive Modeling: Build models to forecast financial trends. Ideal Skills: - Proficiency in Python, especially for automation tasks. - Strong background in financial data analysis. - Experience with data visualization libraries (e.g., Matplotlib, Seaborn). - Knowledge in machine learning for predictive modeling. - Ability to generate automated reports, possibly using libraries like Pandas and ReportLab. Please ensure the tool is efficient, scalable, and user-friendly. Looking forward to ...

...exploratory and statistical analysis to surface patterns in revenue, volume, seasonality, and product performance. • Build clear visualisations—charts, tables, dashboards—so I can grasp results at a glance. • Summarise insights in a concise narrative report, highlighting opportunities and any red-flag issues you detect. I am open to whichever tool chain you work fastest with—Excel, Python (Pandas, NumPy), R, or a blend—provided the final deliverables are easy for me to review and reuse later. Acceptance criteria 1. Cleaned data file delivered in the original format plus CSV. 2. Reproducible analysis (Jupyter notebook, R script, or documented Excel formulas). 3. Visual report (PDF or interactive dashboard) that clearly explains each ma...

Swiggy Sales Data Analysis Analyzed a large Swiggy orders dataset (196K+...purchasing trends. Used Power Query for data transformation and DAX measures to calculate KPIs, enabling stakeholders to monitor business performance effectively. Used Car Price Prediction Built a machine learning regression model in Python to predict used car prices based on vehicle features. Performed data preprocessing, feature engineering, model training, and evaluation using libraries such as Pandas, NumPy, and Scikit-learn. Airline Passenger Satisfaction Analysis Developed a classification model to predict passenger satisfaction using airline service data. Conducted data preprocessing, exploratory data analysis, feature selection, and model evaluation to identify the key factors influencing customer sat...

...validated dataset with uniform field names, correct date formats, and zero duplicate or conflicting records. • Clear documentation of any assumptions made or authoritative sources used when completing or correcting gaps. • A brief change log so I can trace every modification. The sheet is in Excel, though I have no issue if you prefer bringing it into Google Sheets or using a tool such as Python (pandas) or OpenRefine for the heavy lifting, as long as the final delivery comes back in Excel (.xlsx). Accuracy matters more than speed, yet I would like the first pass delivered within three days so we can review together. If this first batch goes smoothly, additional sets of player statistics will follow....

...Processing & Cleaning Script using Python (Pandas / NumPy) **Project Description:** I am looking for a skilled freelancer who can develop a robust and efficient data processing script using Python. The main goal of this project is to clean, transform, and analyze raw datasets to make them ready for further analysis and visualization. **Key Requirements:** * Load data from CSV / Excel files * Perform data cleaning (handle missing values, remove duplicates, fix data types) * Standardize column names and formats * Handle inconsistent or incorrect data entries * Perform basic transformations and feature engineering * Generate summary statistics and insights * Output clean dataset in CSV / Excel format **Technical Skills Required:** * Python * Pandas, NumPy * Data Clea...

... • Removing duplicates • Handling missing values • Correcting data types Once the tables are tidy, I’d like summary statistics, meaningful visualisations, and plain-language commentary that highlights the key takeaways hidden in the rows. A concise slide deck or notebook showcasing your methodology would be perfect, along with the cleaned dataset itself so I can reuse it later. Python (Pandas, Matplotlib, Seaborn), R, or even Power BI/Excel power tools are all fine—as long as the code or steps are transparent and reproducible. Deliverables • Cleaned CSV/XLSX files • Script or notebook with documented steps • Visual and written summary of insights I’ll be around to answer clarifying questions quickly and look forwar...

...Layer in lightweight AI or rule-based automations—for example, predicting bottlenecks or surfacing overdue tasks—so managers see issues before they escalate. • Provide an architecture that leaves room to add classic workflow features such as task management, automated notifications, or progress tracking when we scale. Preferred stack I’m most comfortable with common analytics tools—Python (Pandas, scikit-learn), SQL, Power BI or Tableau for visualization, and a cloud database (PostgreSQL, BigQuery, or similar). If you have an alternative stack that hits the same goals, feel free to suggest it. Deliverables 1. A functioning web-based (or easily hosted) workflow optimization dashboard tied to my three data streams. 2. Annotated source code plu...

I am preparing a series of research papers on renewable-energy integration within India’s power markets and intend to submit them to ABDC or Scopus index...prepare tables, figures and references (Mendeley, Zotero or similar). Acceptance criteria • Literature review cites at least 40 peer-reviewed sources from the past five years. • Model accuracy and validation metrics are reported transparently. • Manuscript is formatted per selected journal template and ready for direct submission. Proficiency with Excel data handling plus R, Python (pandas, scikit-learn) or equivalent analytics tools is essential, along with strong academic writing skills. If you have prior publications in power-sector journals, mention them when you respond; that will speed up ou...

...is fine, no strict AI needed). · The tool fills the corresponding cells in the "Notes" tab with the correct amounts. · That's it. I download the updated template, open it, and my FS are ready. No checks, no tie-outs, no validation rules required. Important: I do not need complex AI, audit trails, or verification pop-ups. Just a straight data-mapper. --- Skills Required: · Expert in Python (Pandas & OpenPyXL) OR VBA/Macros OR Power Query. · Experience handling Excel files programmatically. --- Deliverables: · Working source code or executable file. · Brief instructions on how to run it. --- Budget & Timeline: · Flexible budget. · Timeline: 1 week. --- How to Apply: Send me a brief message wi...

I have a collection of relational-database exports and sizeable spreadsheets that need to be explored, cleaned, and turned into clear, actionab...the data (handle missing values, remove duplicates, standardise formats). • Perform exploratory and descriptive analysis that highlights key trends, outliers, and correlations. • Present findings in a concise report supported by well-labelled visualisations; an interactive dashboard in Power BI, Tableau, or similar is welcome but optional. • Supply the underlying, fully commented Python (pandas / NumPy / matplotlib or seaborn) or R script so I can reproduce the results. I will share the structured data and a short context document once we start. Insightfulness, clean code, and a visually clear presentation are the mai...

...& Data Science * 5+ years of experience building production ML systems. * Strong understanding of: * Supervised and unsupervised learning * Classification and regression * Clustering * Recommendation systems * Feature engineering * Model evaluation and validation * Time series forecasting (preferred) * Statistical analysis and experimentation * Experience with: * Python * Pandas * NumPy * Scikit-learn * XGBoost / LightGBM / CatBoost * Strong SQL skills and experience working with large datasets. ### Generative AI Hands-on experience with: * Large Language Models (OpenAI, Claude, Gemini, Llama, etc.) * Multi-agent architectures * RAG systems * Prompt engineering * Tool calling / Function calling * Structured outputs * Context management * Memory...

I have a sizeable market-research d...correct category. I would like you to: • Perform exploratory analysis to spot anomalies and guide preprocessing. • Build several candidate classifiers (e.g., logistic regression, random forest, gradient boosting, or any modern alternative you find suitable), compare them with cross-validation, and select the best. • Document the whole process in a clear, reproducible Jupyter notebook (Python, pandas, scikit-learn, or comparable libraries). • Deliver the final trained model, the notebook, and a concise report highlighting key metrics (accuracy, precision-recall, confusion matrix) so I can judge real-world performance. A clean codebase, thoughtful comments, and explanation of any assumptions you make will be part of th...

...Kaggle sitting in a MySQL database, populated each time my existing Python ETL script runs. The broad pipeline works; now I want to drill down on one thing only: how entry-level attrition is evolving over time. Here’s what I need from you: • A clean, well-commented SQL query (or set of queries) that calculates monthly and quarterly attrition rates for entry-level employees. • A short Python (pandas) routine that executes those queries, pushes the results to an Excel file, and time-stamps each export so historical runs are preserved. Acceptance criteria 1. SQL returns the correct counts and percentages when spot-checked against sample rows in MySQL. 2. The Python script runs from the command line without manual edits, captures the latest data, and dr...

...implemented objectively and rule-based) Historical Backtesting Requirements Backtest period: 2017 – Present Please provide: Win Rate Profit Factor Maximum Drawdown Average Trade Total Trades Average Holding Time Best Performing Indicator Combinations Worst Performing Indicator Combinations Buy & Hold Comparison Equity Curve Technical Requirements Preferred Skills: Pine Script v5/v6 Python Pandas or similar frameworks TradingView Quantitative Trading Crypto Markets Important Requirements The system should: Avoid repainting signals Use only objective and reproducible rules Be statistically validated Minimize false positives Work primarily for XRP but be adaptable to other cryptocurrencies Deliverables Historical validation report Statistical performance analysis ...

...Accurate data extraction from those three file types • Concise English-language summaries generated on demand • Reliable keyword and phrase identification that feeds a searchable index Everything will run in English for now, so no multilingual processing is required. I’m open to whichever stack you prefer—Python, Node, or another language—as long as modern NLP libraries (think PyPDF, docx, pandas, spaCy, transformers, LangChain, etc.) are used cleanly and the code is well-commented. Here’s how I picture the workflow: I upload a document or folder, the system parses each file, stores the structured output in a database or vector store, and exposes a simple interface (CLI, web dashboard, or API endpoint) where I can fire off queries such as &...

...project is to develop a Python-based automation system capable of systematically collecting publicly available NGO information after authorized user interaction where required (such as CAPTCHA), processing the extracted data, storing it in a structured database, and generating reports and analytical dashboards. The system combines Selenium for browser automation, BeautifulSoup for HTML parsing, Pandas for data processing, SQLite/MySQL for storage, and Streamlit for dashboard visualization. The proposed solution minimizes manual effort, improves data accuracy, supports large-scale data management, and enables researchers, government agencies, CSR departments, and nonprofit organizations to analyze publicly available NGO information efficiently while respecting applicable website t...

I have a folder of 12 short-duration sound samples and I need two metrics extracted from every file: the three tristimulus values and the spectral centroid. All data must be processed in Python, ideally with Librosa at the core; you are free to lean on NumPy, SciPy or Pandas for supporting math and I/O. Please send back: • A clean, well-commented .py script (or a Jupyter notebook) that loads an arbitrary batch of WAV/AIFF files and outputs a table containing filename, tristimulus 1-2-3, and centroid. • A CSV (or similar) generated by running that script on my dataset. • A short README describing assumptions (e.g., window size, hop length, any pre-emphasis) so I can reproduce results or tweak parameters. I will share the audio samples as soon as we start; the to...

...DigitalOcean. The bot must nail three things: • Accurate entry and exit rules exactly as I specify • Robust, hard-coded risk management (stop-loss, position sizing, slippage buffers, safe-shutdown logic) • Lightning-fast execution that minimises latency and handles disconnects without missing trades I already have the mathematical indicators and rule-set; you translate them into efficient Pandas-based logic, wrap them in a class structure, and expose config variables so I can tweak parameters without touching the core code. Use logging and exception handling that lets me audit every fill and debug issues quickly. Deliverables • Git-based Python project with clear README and • Dockerfile or equivalent for one-command deployment on AWS or Di...

I have a technology-sector sales dataset that first needs to be made analysis-ready, then explored for product-performance insights. The workflow I have in mind is straightforward: Data preparation – Remove duplicates – Handle missing values – Standardise dates, currencies, and any other inconsistent formats Analysis & visuals Using Python (Pandas, NumPy, Matplotlib/Seaborn) or Excel where it makes sense, drill into product performance and surface the metrics that truly matter—sales volume, revenue, margins, and any patterns that jump out. I’m happy to connect the raw tables to a local SQL instance if SQL querying will speed things up. Dashboard Once the story is clear, translate it into an interactive Power BI report. I’d like ...

We are looking for an experienced Python trainer to work with our team, starting with one participant and scaling up over time. The training will span 90 days and focus on building practical skills in: - Data Analysis: Cleaning, manipulating, and visualizing data (Pandas, NumPy, Matplotlib/Seaborn). - Web Development: Building simple applications using Flask or Django. - Automation & Scripting: Writing scripts to automate routine tasks. The trainer should: - Design a detailed training plan with weekly objectives and assignments. - Conduct interactive sessions (virtual is fine) and provide learning materials. - Provide code feedback and close mentorship throughout the program. - Conclude with a capstone project to assess skill development. This is a great fit if you have expert...

...including: - Win rate - Profit factor - Drawdown - Expectancy - Monthly returns - Equity curve - Trade log --- Reporting Generate detailed reports showing: - Daily performance - Weekly performance - Monthly performance - Trade screenshots (optional) - Analytics --- Technical Requirements Preferred Technology Stack: - Python - Broker API integration (e.g., SmartAPI, Zerodha Kite, or similar) - Pandas - NumPy - TA libraries (only where required) - SQLite/PostgreSQL - Git version control --- Code Requirements The project should be: - Modular - Object-oriented where appropriate - Well documented - Easy to maintain - Easily extendable - Efficient and optimized - Properly logged - Exception handled - Configurable through external configuration files --- Deliverables The...

I have a Data Analytics portfolio with projects in Python, SQL, Power BI, Excel, Machine Learning, Pandas, Scikit-learn, and Predictive Analytics. The goal is to make my GitHub profile stand out as visually appealing and highly organized for recruiters. GitHub: What I'm looking for: - Custom banners and animations for each repository. - High-quality dashboard screenshots, charts, icons, and visual assets based on actual project results. - Rewriting each README as a clear case-study format for quick understanding: * Business Problem * Dataset * Methodology * Key Insights * Business Impact * Technologies Used - Improving repository structure and visual organization to enhance navigation. - Adding professional badges with consistent branding.

...spot. Once the dataset is reliable, I need swift data analysis—basic descriptive statistics, trend identification, and any correlations that jump out—summarised in a short written brief. With the cleaned data and report finished, the final task is to translate those findings into an interactive dashboard. Power BI is my preferred tool, but I am open to an equivalent solution built in Python (pandas, matplotlib/Plotly, Dash) or even Excel’s own Power Query and Pivot Charts if that speeds things up. Regardless of platform, the dashboard should let me drill down by date range and category and surface key KPIs at a glance. Because timing is critical, please outline how quickly you can start and the checkpoints you would propose to make sure we stay on track. ...

I’m looking for a reliable Python developer who is comfortable writing clean, well-documented code entirely on their own—no AI code-generation tools in the workflow. The work is part-time and ongoing. The core of the job is to help me streamline and optimise several business processes. On any given week you might: • explore raw data in pandas, craft insightful visualisations in Matplotlib or Plotly, and present concise dashboards; • build and tune scikit-learn (or similar) models that predict key operational metrics; • wrap those models or analyses in lightweight automation scripts, then integrate them with our existing systems through REST APIs, scheduled jobs, or direct database hooks (PostgreSQL/MySQL). Your deliverables should run smoothly insi...

I have up to two Excel workbooks that need a quick but thorough...workbooks that need a quick but thorough duplicate-removal pass. The structure of each file should stay exactly as it is—same column order, formulas, and formatting—only the repeated rows must go. Once the duplicates are gone, simply return the cleaned versions in XLSX format so I can drop them straight back into our workflow. You may use the duplicate-filter built into Excel, a VBA macro, Python (pandas), or any other method you prefer, as long as the final files open flawlessly in the latest desktop version of Microsoft Excel and every unique record is preserved. Please let me know how soon you can turn this around and, if helpful, describe the approach you plan to take so I have confidence in the accur...

...Engineering:** Ensure code scalability, reliability, and clean integration with market data APIs and execution platforms. ### **Required Skills & Experience** * **Experience:** 5+ years of professional software engineering experience with a solid track record in quantitative or algorithmic trading. * **Core Languages:** Mastery of **Python** and deep proficiency within its scientific stack (**Pandas, NumPy**). * **Quant Frameworks:** Hands-on experience with advanced backtesting libraries (e.g., VectorBT, Backtrader, or QuantConnect LEAN). * **Quantitative Finance:** Strong foundation in statistics, portfolio theory, risk modeling, and alpha generation. * **Asset Classes:** Direct experience handling data and trading logic for **stocks, futures, or cryptocurrency**. ### **...

...spot. Once the dataset is reliable, I need swift data analysis—basic descriptive statistics, trend identification, and any correlations that jump out—summarised in a short written brief. With the cleaned data and report finished, the final task is to translate those findings into an interactive dashboard. Power BI is my preferred tool, but I am open to an equivalent solution built in Python (pandas, matplotlib/Plotly, Dash) or even Excel’s own Power Query and Pivot Charts if that speeds things up. Regardless of platform, the dashboard should let me drill down by date range and category and surface key KPIs at a glance. Because timing is critical, please outline how quickly you can start and the checkpoints you would propose to make sure we stay on track. ...

I'm seeking a skilled Python developer to work on a project. The specific type of project, primary function, and preferred libraries/frameworks are currently undecided. Ideal Skills and Experience: - Proficiency in Python - Experience with web applications, data analysis, or automation - Familiarity with Django/Flask, Pandas/Numpy, or Scrapy/BeautifulSoup - Strong problem-solving skills - Ability to work independently and meet deadlines Please provide relevant experience and a brief project approach in your bids.

...now because I’m still gathering business requirements, but the essentials are clear: • You’ll clean, explore, and engineer features from whatever data sources we finalise (they may include structured tables, free-text logs, images, or time-series feeds). • When the data story warrants it, you’ll build and validate predictive or prescriptive models in Python or R, leveraging libraries such as pandas, scikit-learn, TensorFlow, Prophet, or similar. • Clear, decision-ready output matters, so expect to create concise visualisations or dashboards—Tableau, Power BI, or matplotlib/Plotly—alongside well-annotated notebooks and a short slide deck summarising key findings. • Every step should be reproducible. Please version your code ...

I want to turn the text already stored in my database into reliable predictions that can drive decisions. The job starts with pulling the raw records straight from the tables, cleaning and preparing the text, and then building a machine-learning pipeline that outputs clear, measurable forecasts. Python with libraries such as pandas, scikit-learn, spaCy or transformers is ideal, but I am open to other proven stacks if they suit the data volume. What matters most is accuracy and reproducibility. Every step—from extraction and tokenisation to model training and evaluation—needs to be scripted so I can rerun the process as new data lands in the database. A lightweight inference endpoint or CLI that lets me feed fresh text and receive the prediction scores in real time will ...

...analysis and visual summaries: word-frequency plots, topic trends over time, and sentiment distribution where relevant. • Advanced NLP: named-entity recognition, key-phrase extraction, and topic modelling (LDA or BERTopic). • Result delivery: a concise written report plus reusable Python notebooks/scripts and clearly commented source code, ready to run on my local environment (Python 3.10, pandas, spaCy, scikit-learn, PyTorch/Transformers as appropriate). Acceptance criteria 1. The pipeline processes at least 1 GB of sample official documents without manual intervention. 2. Visual outputs render correctly in Jupyter and export to PNG/PDF. 3. Code follows PEP 8 and includes a README explaining setup, parameters, and how to extend the model. Once these ste...

Project Overview We develop a fully automated Excel-based solution that eliminates manual data entry, reduces errors, and gen...that eliminates manual data entry, reduces errors, and generates professional reports automatically. Features - Excel data processing and validation - Automatic invoice generation - Sales and inventory dashboards - CSV, Excel, and PDF conversion - Automated report generation - Email delivery of reports - Data cleaning and formatting - Microsoft 365 compatibility Technology Stack - Python - Pandas - OpenPyXL - XlsxWriter - ReportLab - SMTP Email Automation Deliverables - Complete source code - Excel dashboard - Documentation - Installation guide - Deployment support Ideal For - Manufacturers - Traders - Distributors - Accountants - Small and medium ...

I run a series of online courses that teach budgeting and saving skills, and now I need clear evidence of how much they actually help learners. I’m looking for a specialist who can design and execute an impact-measurement plan based on participant feedback collected through surveys. Here’s...that I can share with partners and funders Acceptance criteria • Questions align with established financial-literacy assessment standards • Minimum 95 % confidence level in reported findings • Actionable recommendations clearly linked to survey data If you have a track record in education impact studies, survey design, or data analysis tools such as Qualtrics, Google Forms, R, or Python (Pandas, SciPy), I’d love to see how you’d approach this proje...

...spot. Once the dataset is reliable, I need swift data analysis—basic descriptive statistics, trend identification, and any correlations that jump out—summarised in a short written brief. With the cleaned data and report finished, the final task is to translate those findings into an interactive dashboard. Power BI is my preferred tool, but I am open to an equivalent solution built in Python (pandas, matplotlib/Plotly, Dash) or even Excel’s own Power Query and Pivot Charts if that speeds things up. Regardless of platform, the dashboard should let me drill down by date range and category and surface key KPIs at a glance. Because timing is critical, please outline how quickly you can start and the checkpoints you would propose to make sure we stay on track. ...

...history. * Develop prediction models for win, place, and probability estimation. * Implement model evaluation, calibration, and continuous retraining. * Integrate ML outputs into the existing Horse Intelligence Engine without replacing evidence-based scoring. * Maintain explainability for every prediction. Required Skills * Python * Machine Learning (Scikit-learn, XGBoost, LightGBM, or similar) * Pandas, NumPy * PostgreSQL * FastAPI * Feature Engineering * Probability Calibration * Model Validation * Git Preferred Experience * Sports analytics * Horse racing data * Betting analytics * Time-series modelling * Ranking and recommendation systems Deliverables * Clean, documented code * Production-ready ML pipeline * Model evaluation reports * Integration with existing backend *...

...• Converts each set of odds into implied probabilities, applies a fair-margin adjustment, flags any value bet where the fair price beats the bookmaker’s. • Writes every scrape in real time to a clean CSV: date/time, sport, league, teams, each bookmaker’s odds, fair probability, edge %. I’m comfortable providing the API key and any rate-limit notes once we start. Please keep dependencies light—pandas, requests, maybe python-dotenv are fine—and document the setup in a short README so I can run `python ` straight from Terminal. Acceptance criteria 1. Script executes without errors on macOS Ventura (Python 3.10). 2. CSV output matches the column spec above and updates on each run. 3. For a supplied test fixture you’ll show at least one ...

...Kite Connect API se fetch ho Error handling ho Agar kisi stock ka data missing ho to system skip/error log maintain kare Deliverables Complete Python source code Excel output format Setup guide Required libraries list Zerodha Kite Connect API setup instructions Daily automation setup, Windows Task Scheduler ya cloud/server par Basic documentation Required Skills: Python Zerodha Kite Connect API Pandas Excel automation using OpenPyXL/XlsxWriter Stock market historical data handling API integration Error handling and logging Automation scheduling Preferred Experience: Aise developer ko preference milegi jisne pehle stock market scanner, Zerodha API, historical data fetching ya Excel automation ka kaam kiya ho. Final Output Example: Excel me format kuch is tarah hona chahiye: ...

...developed end-to-end in Python. The system should scan listed equities, generate signals, size positions, and place orders without manual intervention. The approach is algorithmic: I’m open to a mix of quantitative fundamentals, momentum factors, and risk filters, provided the logic is transparent and backed by data. Please write clean, modular code that relies on mainstream libraries such as pandas, NumPy, TA-Lib (or your preferred technical stack), and connects to a reliable market-data/API source—Alpaca, Interactive Brokers, or similar—so I can switch providers later without a rewrite. Back-testing on at least ten years of daily data is essential, with performance metrics (CAGR, max drawdown, Sharpe) clearly reported and plotted. I also want walk-forward te...

...Identify and remove every duplicate record across the full set of files. • Spot and correct obvious data entry errors—misspelled text labels, misplaced decimal points, transposed characters, faulty dates, and similar issues. Missing-value treatment is not required; I already have a routine for that, so you can leave blanks untouched. You are free to work in Excel, Google Sheets, Python (pandas), R, or any other tool you trust, as long as the final deliverable is returned to me as clean CSV/Excel plus a short log outlining what you changed and how you caught it. I’ll review by running my validation script against the cleaned files; zero duplicates and consistent field formats will be the acceptance criteria. Feel free to ask questions up front—the...

Successfully developed a Machine Learning model to predict passenger survival outcomes based on historical maritime data from the Titanic dataset. Key Responsibilities & Tech Stack: Data Preprocessing & Cleaning: Handled missing...advanced imputation for missing ages using passenger titles. Feature Engineering: Extracted meaningful features like passenger 'Titles' and engineered a 'Family_Size' metric to improve model accuracy. Model Training & Evaluation: Implemented and compared classification algorithms (like Random Forest / XGBoost) to optimize performance, achieving a solid accuracy of 76.54%. Tools used: Python, Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn. This project demonstrates strong capabilities in data analysis, feature enginee...

...into a concise case-study format: business question, data sources, methodology, key insights, and final results. • Tidy the folder structure so code, data, notebooks, and assets are separated logically and clone-ready. • Add striking project banners, plus the selected visual elements—dashboard screenshots and charts—drawn from the work I built in Python, SQL, Excel, Power BI, Scikit-learn, and Pandas. • Sprinkle in appropriate badges (build, licence, tools) so each repository looks active and well-maintained. • Create or refine a profile-level README that ties the projects together, keeps tone and styling consistent, and targets roles such as Data Analyst, Business Analyst, and Operations Analyst. Acceptance criteria – Every repo...

I have a Data Analytics portfolio with projects in Python, SQL, Power BI, Excel, Machine Learning, Pandas, Scikit-learn, and Predictive Analytics. The projects are complete and functional, but I want my GitHub to look polished, recruiter-friendly, and visually impressive. GitHub: -->What I need * Create custom banners for each repository. * Add high-quality dashboard screenshots, charts, icons, and visual assets based on the actual project results. * Rewrite every README into a concise case-study format: * Business Problem * Dataset * Methodology * Key Insights * Business Impact * Technologies Used * Improve repository structure * Add professional badges, consistent branding, and attractive layouts. * Create or improve my GitHub Profile README so

...I have a university assignment and I am looking for someone who can help me build the project. The project requirements are: Build a distributed system using Python (Backend) and React (Frontend). The Python server should stream real-time stock or currency data to the React application using WebSockets or Server-Sent Events (SSE). The server should analyze the incoming data in real time using Pandas or NumPy and calculate technical indicators such as: SMA (Simple Moving Average) EMA (Exponential Moving Average) RSI Volatility indicators The system must include background tasks using Asyncio or Celery. The React application should include: A smart dashboard displaying: Live raw data Real-time charts Alert notifications Dynamic charts using libraries such as: Recharts ApexChar...

...stored in a MySQL database and I need a clear, story-driven snapshot of what that data is saying. The goal is straightforward: use descriptive analysis techniques to surface counts, proportions, trends over time, and any notable patterns that can be spotted without predictive modeling. You are welcome to work directly in SQL for initial aggregation, and then move to your preferred tool—Python (pandas, matplotlib, seaborn), R, or even Tableau/Power BI—for the visual and narrative layer. What I will hand over: database credentials to a read-only replica plus a quick schema overview so you can see table relationships. The data are already reasonably clean, but feel free to flag or handle any obvious inconsistencies you encounter. What I need back: • A complete set...

My Python-based market-scanner is connected to the Bybit exchange and is meant to flag when it’s time to buy or sell....need the detection algorithm reviewed, corrected, and stress-tested against live and historical Bybit data so that: • every true buy signal is tagged as such, • every true sell signal is tagged correctly, and • no extra false positives slip through. You’ll have direct access to the existing repository, which uses the Bybit REST/WebSocket APIs along with standard Python trading libraries (pandas, numpy, ta). Unit tests are already in place; feel free to extend them to cover edge cases you uncover while debugging. Deliver a clean, commented patch or pull request plus a brief summary of the changes and the test evidence that the sca...

I’ve developed a Movie Recommendation system using Python, with core libraries such as random and pyttsx3. It’s a customized solution designed to suggest movies based on specific criteria. The system doesn’t rely on standard frameworks like Scikit-Learn or Pandas but instead focuses on a straightforward implementation leveraging Python's capabilities.

I need a Python au...manipulation, web scraping, simple system operations, or another Python-friendly task we identify together. Once we agree on the exact routine, I will share all supporting files, URLs, or process notes. Deliverables • A clean, well-commented .py script (Python 3.x) ready to execute on Windows without manual tweaks • A concise README with setup instructions and a list of required libraries (e.g., pandas, requests, BeautifulSoup, pyautogui, etc.) • Meaningful logging and graceful error handling so I can track what the script is doing and why I value straightforward code I can maintain myself later, so please keep the structure logical and modular. Let me know your proposed approach, turnaround time, and any clarifying questions so we can g...

...centre on Pandas, NumPy and, where useful, basic visualisations in Matplotlib or Seaborn. The dataset itself will be supplied as soon as we start; I’m flexible on its format (CSV, JSON, or even a quick pull from MongoDB). What matters most is speed and accuracy: I’d like preliminary findings within the first 24 hours and the full deliverable finished inside two days. Deliverables (must-have): • A cleaned, well-documented dataset • A concise report (Jupyter Notebook or PDF) highlighting key statistics, trends and any anomalies you discover • Visualisations that make the insights easy to grasp at a glance I’m available for quick check-ins on Slack or Zoom to keep the turnaround tight. If you’ve got the bandwidth to start today a...

I have a stream of numerical information being pulled automatically from several news sites, and I need that raw output cleaned, coded, and delivered in ...scrape, identifies the key figures buried in the articles, applies consistent codes to them, and drops everything into a structured file (CSV or JSON works for me). You’ll receive: • the current scraping script and a sample of the raw dump • a field dictionary showing how each number should be labeled or categorised I’m expecting your returned script (Python preferred—BeautifulSoup/Scrapy plus pandas is perfect, but use what you like) along with the final processed dataset and a brief read-me so I can rerun or extend the pipeline later. Accuracy of the coding and reproducibility of results will be ...