Pandas Jobs

I am looking for an experienced Python developer to build a trading analysis tool for synthetic indices (such as volatility indices). The tool will help analyze market data, test strategies, and provide a live dashboard for monitoring Requirements Data Collection Connect to a trading platform API (example: Deriv or similar). Collect and store historical price data. Ability to update data automatically. Market Analysis Engine The tool should: Analyze price movements. Detect patterns or indicators. Allow custom strategy rules. Backtesting System Test strategies using historical data. Show results such as: Win rate Profit/loss Drawdown Number of trades Live Analysis Run the strategy on live data. Generate signals or alerts when conditions are met. Dashboard A simple web da...

I’m looking to start a Python-based project purely for Personal use and I’m intentionally keeping the brief open so creative developers can pitch ideas that excite both of us. Whether it’s a handy automation script, a data-driven dashboard, a lightweight Flask or Django web app, a web-scraping utility, or even a small game, I’m happy to explore any direction—as long as it showcases clean, well-documented Python code. Because I do not have a strict deadline (No time limit), I prefer quality over speed. Take the time to think through the concept, architecture, and tech stack; then send me a Detailed project proposal that explains: • The core idea and its personal value • Key Python libraries or frameworks you plan to use (e.g., Pandas, Selenium, Fa...

Project Title: MERN Stack Web Application Development Project Description: I am looking for a developer who can build a web application using the MERN Stack (MongoDB, , React.js, Node.js). This project is for academic purposes. Requirements: • Frontend: React.js with responsive UI • Backend: Node.js and • Database: MongoDB • Features: User authentication (login/signup), CRUD operations, basic dashboard • Clean and well-structured code Additional Requirements: • Source code must be provided • Basic documentation of the project • Proper comments in the code Budget: Open for discussion Deadline: 5–7 days If you have experience with MERN Stack development, please place your bid and share your previous work if available.

Title Build Comprehensive Global Movie & TV Metadata Database for Recommendation Engine Project Overview I am building a personal recommendation engine that predicts my rating for movies and TV shows based on a large history of titles I have already rated. To support accurate predictions, I need a global media metadata database that contains rich structured information for movies and TV series. The dataset should combine multiple trusted sources and be designed for machine-learning comparison against my rating history. Scope of Work Build a master dataset containing global movie and TV metadata. This will serve as the candidate pool for prediction models. The database should include titles from: • IMDb official dataset • The Movie Database API • Optional enri...

I am a recent graduate and want a structured skill-development course that takes me from absolute beginner to confident practitioner in data analysis. The program should combine theory with plenty of hands-on practice so I can start applying what I learn right away. What I expect • A clear learning path that begins with fundamental concepts (data types, basic statistics, exploratory analysis) and steadily introduces more advanced topics. • Practical exercises and mini-projects using widely adopted tools such as Excel, SQL and Python (Pandas, NumPy, Matplotlib or similar), so I can build a small portfolio along the way. • Short quizzes or checkpoints after each module to confirm I have grasped the material. • Real-world datasets for practice plus guidance on where...

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

I have already trained and deployed a Logistic Regression model in Streamlit that classifies breast-tumour samples as malignant or benign. What I need now is a polished data-visualization layer so users can quickly grasp how each feature influences the prediction. My immediate focus is on bar-chart visualisations. I want clear, well-labelled bars that compare malignant vs. benign distributions, show feature importances, and surface any other insight you think adds value. The work should plug straight into my current Streamlit app and read from the same Pandas DataFrame I am already passing to the model. Although the main task is visualisation, I am also experimenting with feature selection, so if your code can be structured in a way that makes it easy to toggle feature subsets, that wi...

I want a single Excel file that lets me drop in fresh sales and purchase data and instantly see what’s happening in the business. The heart of the job is an interactive dashboard—clean, professional, and easy for anyone on my team to grasp at a glance. Here is what I need the file to do: • Automatically refresh whenever I upload a new data file, so no manual copy-pasting. • Display bar, line, and pie charts that highlight both sales trends and purchase trends. • Use pivot tables and slicers to let me drill down by product, date, or supplier without breaking the layout. • Track current inventory levels in real time, flagging low-stock items. • Surface the two KPIs I care about most—product-wise performance and overall revenue growth&mdas...

I am looking for a part-time Python developer to build a dynamic financial simulation and data visualization tool. We are currently in a strategic planning phase where we need to model various **exit scenarios** and calculate **profit potential** based on fluctuating market variables. The goal is to move from "uncertain claims" to "data-driven probabilities." ### **Key Responsibilities / Scope of Work** * **Dynamic Modeling:** Create a Python-based simulation engine that accepts multiple inputs (revenue growth, operational costs, market multiples) to project ROI. * **Exit Scenario Analysis:** Build logic to test different exit criteria (e.g., acquisition at Year 3 vs. Year 5) and their impact on terminal value. * **Data Integration:** Fetch and process market benchma...

I need an 8- to 10-page conference paper that presents a hybrid machine-learning Security Information and Event Management (SIEM) framework combining Random Forest and Isolation Forest for network-threat detection. The manuscript must follow either Springer LNCS or Scopus proceedings guidelines, complete with the correct template, figure sizing, and reference style. Core structure • Introduction and literature review that positions the problem, surveys recent SIEM advances, and justifies the hybrid approach. • Methodology and data analysis describing data-preprocessing, feature engineering, model building in scikit-learn, and experimental evaluation on publicly available cybersecurity datasets (e.g., CIC-IDS 2017, UNSW-NB15, or similar). • Conclusion and future work...

I need a small, reliable script that pulls historical option-chain data from NSE, aggregates the last-traded price (LTP) of every call and every put for each date/expiry, and then outputs the two totals side-by-side so I can quickly compare how calls and puts behaved over time. The focus is purely on historical analysis—no real-time streaming or alerting. I’m not interested in fancy visualisations; simple numeric results (CSV, Excel, or a neatly formatted table) and a brief comparison metric or ratio are perfect. Python with libraries such as requests, pandas, or nsepython is ideal, but I’m open to any language that can handle the NSE site’s quirks and respect its rate limits. Please include: • A script that can be rerun for any date range I specify. ...

I want a clean, well-documented Python application that plugs into the Fyers REST API and runs a fully automated intraday strategy. The logic is straightforward: a Moving Averages crossover confirmed by a Relative Strength Index (RSI) filter triggers entries, then the system exits when the opposite MA crossover appears or a user-defined RSI level is hit. Time-frames (1-min, 5-min, etc.) and all indicator parameters should be editable from a single config file so I can tweak the behaviour without touching the code. The program must: • Authenticate to Fyers using my API keys, refresh tokens automatically, and reconnect on dropouts. • Stream live market data, compute the MAs and RSI in real time (pandas_ta or TA-Lib are fine), and generate long/short orders the instant conditions...

I have already deployed a full Streamlit application that predicts loan approvals in real time (live demo: , source: ). The pipeline currently includes Logistic Regression, K-Nearest Neighbors, and Naive Bayes models with standard scaling and the usual EDA-driven feature engineering. What I want now is a measurable lift in overall model performance, with the F1-score as the guiding metric. Feel free to explore more advanced algorithms (e.g., Gradient Boosting, XGBoost, LightGBM, calibrated ensembles, or even a tuned version of my existing classifiers) as long as they integrate cleanly with the existing Python | Pandas | NumPy | Scikit-learn stack and can be surfaced through the current Streamlit front-end. Key points you should address • Re-examine preprocessing and feature sele...

Our historical sales data is signalling shifts that we don’t fully understand yet, so the priority is a diagnostic analysis that tells us not just what changed, but why it changed. The raw tables cover order details, customer attributes, product SKUs and daily revenue going back three years. Primary questions on the table: • How have sales trends evolved month-to-month and season-to-season? • Which customer segments are driving (or dragging) revenue, and how has their purchasing behaviour shifted? • Which products or product groups are over- or under-performing once promotions, returns and stock-outs are factored in? A clean, reproducible workflow in SQL, Python (Pandas, NumPy, Sci-Py) or R is essential so the team can rerun the analysis after future data drops...