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I am preparing a quantitative study on “Inequality in participation versus visibility in online communities” . The raw material will come from one or more publicly-available Kaggle datasets; the challenge is to turn those data into a coherent, publication-level research project. Here is what I need from you: • Help me locate or combine the right Kaggle datasets, then document the download and preprocessing steps (Kaggle API, Python pandas, or R tidyverse are fine). • Define robust operational metrics for participation (e.g., post frequency, comment depth) and visibility (e.g., up-votes, follower counts, ranking on leaderboards). • Build the analysis pipeline—cleaning scripts, exploratory statistics, and the main inferential models (regression, GEE, or...

I need a reliable algorithmic bot that can execute an intraday strategy on the stock market. The core idea is simple: pull real-time equity data, apply my day-trading rules, and place orders automatically through a brokerage API (Interactive Brokers, Alpaca, or any comparable platform you are comfortable with). Low-latency data handling, solid risk management (position sizing, stop-loss, and max-drawdown limits), and accurate order execution are critical. Speed matters—I’d like a working version delivered as soon as realistically possible, so please factor rapid development and clear communication into your timeline. Python is my preferred language because of its mature ecosystem (Pandas, NumPy, TA-Lib, backtrader, etc.), but I’m open to alternatives if you can prove com...

I will give you direct access to our secured Government health databases so you can extract the figures we monitor daily on infectious-disease cases, laboratory confirmations, and sentinel surveillance sites across Nepal. Your task is to turn this raw information into clear, evidence-based insights that guide our infectious-disease control policies. I need clean, reproducible code (Python, R or Stata are all acceptable), an annotated dataset ready for archiving, and a concise report that highlights trends, hotspots, and any statistically significant anomalies. Please visualise the main findings in intuitive charts or dashboards so they can be shared quickly with provincial teams and senior officials. Deliverables • A documented data-cleaning and analysis script • The pr...

I’m putting together a comprehensive backtest that focuses exclusively on stock market data from the NYSE and NASDAQ, spanning the last 10 years. To do this effectively I need a well-structured historical dataset that I can drop straight into my models without hours of manual cleanup. Here’s what I’m after: • Daily (or finer) OHLCV prices, fully adjusted for corporate actions, splits, and dividends. • Consistent symbol mapping so delistings, mergers, and ticker changes don’t break the series. • A single, tidy delivery format—CSV files are fine, but a lightweight SQL or Parquet database also works if you prefer. • A short README that explains field definitions, adjustment methodology, and any known data caveats. If you already have a...

I need a skilled data professional to turn my historical sales records into reliable projections for the months ahead. The raw files are already exported from our POS and e-commerce platforms; they cover daily transactions, product categories, promotions, and regional outlets. Your first task will be to explore and clean this sales data, handle any missing values or outliers, and engineer features that capture seasonality, campaigns, and other business drivers. My primary goal is an accurate sales forecast and projection roadmap. I am particularly interested in machine-learning approaches—think gradient-boosted trees, LSTM, Prophet, or any other model you feel best suits the data’s structure. Classical time-series or regression techniques are fine as benchmarks, but the core d...

I am preparing a full-length manuscript for submission to a Q1 SSCI economics journal on the theme of machine learning–driven economic-growth prediction. The core of the article must showcase concrete applications and real-world case studies rather than abstract algorithmic discussions. I want a genuinely global perspective, so the empirical section should compare or combine economies across different income levels rather than concentrating on a single region. All quantitative work has to rely on publicly available government databases—think World Bank, OECD, IMF, national statistical offices—so that review-ers can easily replicate the results. You are free to merge multiple sources as long as every dataset is openly accessible. Key expectations • 8,000–1...

I am looking for an experienced data analyst / ServiceNow expert to help transform CMDB data into actionable insights and predictive analytics. The CMDB currently receives data from three sources: • Network devices • Software applications • Servers / hardware assets ⸻ Scope of Work: 1. Data Consolidation & Preparation • Review and consolidate CMDB data • Clean and standardise datasets for analysis • Ensure data is usable for reporting and modelling 2. Predictive Analysis • Build simple predictive models to identify early warning signals such as: • Capacity issues • Performance degradation • Configuration drift 3. Dashboards / Reporting / Alerts • Create easy-to-use outputs for operations teams (e.g., dashboard...

? HAGO Contest #2 – April ? For data scientists and analytics enthusiasts, we present a new challenge with Amazon sales dataset. ? Tasks: Explore the dataset in depth Analyze patterns and trends Build a predictive model using Python Submit results in a PDF file within our private community ? Prize: The winner receives $15 USD for the best analysis and prediction ? Don’t forget to join the IFAI Contest for future forecasting, where the winner earns $10 USD. ✨ Tips to increase your chances of winning: Make your analysis comprehensive yet easy to understand Add clear and insightful visualizations Focus on prediction accuracy and creative ideas

Strategy Name: Footprint Dot-Based Auto Exit Strategy (Profit-Constrained) (DOT indicator referenced already has been built) $30 Flat. ______________________________________________________________________________________________________________________________________ 1. Purpose This strategy is designed to: Automatically exit manually placed positions (Long and Short) Based on pre-existing footprint indicator signals (Red and Blue dots) Ensure that ALL exits are profitable Operate on a Line Break chart Process both historical and real-time data ________________________________________________________________________________________________________________________________________ 2. Dependencies Requires an existing indicator that provides: Red Dots Plotted on positive (up) ...