Pandas Jobs

I’m looking for a clean-coded Python application that pulls product-detail JSON from an e-commerce site and turns it into a neatly formatted Excel workbook in one click. Here’s what I have in mind: • The script should call the site’s public JSON endpoint (or simulate the site’s own XHR requests if no open API exists), parse the response, and map fields such as SKU, title, price, availability, image URL, and category into columns. • A single command—CLI or lightweight GUI—is enough, as long as it lets me paste or import the target URL and instantly receive the Excel file (.xlsx). • The output file must preserve data types (numbers as numbers, dates as dates) and use sensible column headers. I prefer Python because I’ll mai...

I am building a small learning project for my Diploma in ICT and need a clear, well-commented Python solution that reads a JSON dataset, performs descriptive statistics, and presents the results in a way a newcomer can understand. Here is what I have in mind: • The script should load a supplied JSON file, validate its structure, and extract numerical fields automatically. • Compute key descriptive measures such as count, mean, median, mode, min-max range, standard deviation, and any other basic metrics that add insight. • Display the findings in a tidy console table; a simple Matplotlib or Seaborn histogram for at least one variable would be a plus but not mandatory. • Keep everything beginner-friendly: straightforward function names, docstrings, and inline c...

I want to put a complete, web-ready AI expert system online that suggests the best laptop for a user based on a few quick questions. The core of the project is a rule-based engine built with Experta in Python and wrapped in a Streamlit interface. Scope of work • Create or refine the rule base so that recommendations place the strongest weight on RAM and CPU, while still considering GPU, storage, OS and intended usage. • Design a simple, clean Streamlit front end—no clutter, just concise inputs and an easy-to-read results panel. • Load and preprocess a laptop data set (I can supply one, or you can extend it) and keep it in a Pandas-friendly format. • Return a ranked list of laptops with a brief rationale for each choice. Deliverables 1. Fully comment...

I need an end-to-end algorithm that trades a single share-market instrument entirely on technical chart analysis. The core logic must follow a technical trading approach, relying on candlestick patterns, moving averages, RSI, and manually-defined trend lines to decide entries, exits, and position sizing. Scope The build starts from raw market data ingestion, moves through signal generation, and finishes with live order execution and performance reporting. All elements—data cleaning, feature engineering, back-testing, walk-forward validation, risk controls, and real-time monitoring—should sit inside one coherent codebase so I can flip from historical tests to live trading with minimal re-configuration. Implementation Notes • Python is my preferred language; common...

I need a complete solution that pinpoints abnormal behaviour across my wireless sensor network. You will have direct access to three rich data streams—raw sensor readings, detailed network-traffic captures, and accompanying environmental context—so the approach can fuse information rather than relying on a single source. Your job is to design, implement, and validate an anomaly-detection model that flags faulty nodes, suspicious traffic, or out-of-range measurements in near-real time. Feel free to select the technique that best fits the data volume and complexity; I am open to statistical, classic machine-learning, or deep-learning pipelines as long as the final system is transparent and reproducible. Python (NumPy, Pandas, Scikit-Learn, TensorFlow/PyTorch) is preferred for ...

I have a raw customer dataset and I need a predictive model that can automatically segment these customers into actionable groups. The job begins with cleaning and preparing the data, moves on to feature engineering, and culminates in training and validating a segmentation model in Python—ideally using pandas, scikit-learn (or similar), and clear visual output via matplotlib or seaborn. What matters most is accuracy and interpretability. I want to understand which features drive each segment, so well-commented code, concise documentation, and an executive-level summary of insights are essential. Final deliverables should include: • A reproducible Python script or notebook • The trained model (saved or easily retrainable) • A brief report highlighting key segments...

I have several datasets coming in from different systems where the numeric fields are stored as plain ASCII files in some cases and UTF-8 exports in others. My goal is to run solid, reproducible data analysis on them, but first they need to be cleaned and standardised. Here is what I need from you: • Build a repeatable cleaning pipeline in Python (Pandas, NumPy) that detects the file encoding, converts everything to UTF-8 where necessary, and validates that all numeric columns are truly numeric—no stray characters, misplaced separators, or locale-specific quirks. • Where the raw files already sit in SQL Server, create T-SQL or Python-based routines that achieve the same checks and corrections in-database so I can schedule them in an Agent job. • Document each s...