Pandas Jobs

I have an AI-driven application written in Python that is already live, but its performance is no longer where it needs to be. Rather than rewriting anything from scratch, I want a developer who can dive into the current codebase, spot the weak points, and apply machine-learning best practices to bring the system up to date. You’ll start by cloning the repo, running the existing training and inference routines, and profiling the data pipeline so we can identify bottlenecks together. From there, your focus will be on tightening the algorithms, modernising any outdated libraries, and introducing cleaner, more modular code that makes future feature work painless. Expect to work with familiar ML tooling—Pandas, NumPy, scikit-learn, TensorFlow or PyTorch—using whichever stack...

I need one reliable freelancer who can move easily between code and copy. 1. Python task I have a folder full of plain-text files. Your script should read each file, filter out obvious noise (blank lines, stop-words, non-ASCII junk), group the remaining content by topic, then write a single CSV where each row contains: filename, detected topic, and the cleaned text. Prefer standard libraries plus pandas / scikit-learn (or another lightweight classifier) so I can run the code with nothing more than Python 3 installed. A brief README, clear inline comments, and a quick demo command are essential. 2. Editing task I’ll hand over several published blog posts that need a polish. Keep the author’s voice, but fix grammar, tighten structure, and raise overall readability. I’...

I have several raw datasets from different business systems that need to be turned into a single, reliable source of truth before we explore the story hidden inside. For the cleaning phase I’ll rely on both Python (think pandas, NumPy, Jupyter notebooks) and advanced Excel techniques such as Power Query and dynamic arrays—whichever makes the job faster and more transparent for later hand-off. All tables ultimately live in MySQL, so you’ll be writing efficient joins, sub-queries, and window functions directly against that database to create analysis-ready views. Performance matters; I want the queries optimized and commented so they can be reused by the in-house tech team. Once the data is shaped, the next step is to craft an interactive, executive-level dashboard in Pow...

I’m sitting on large, messy numeric datasets and need them cleaned, engineered, and split so downstream models train smoothly. Your mission is to design a reproducible preprocessing pipeline: audit the raw CSVs, tackle missing or extreme values, scale or normalize where it counts, generate meaningful features, and output train/validation/test sets ready for model development. Python with pandas, NumPy, and scikit-learn is my default tech stack, but feel free to weave in any modern data-ops tricks—as long as the final workflow runs inside a Docker container that can drop into our CI pipeline. Deliverables • Clean, documented datasets (CSV or Parquet) • Version-controlled preprocessing scripts/notebooks • Dockerfile plus a concise README for one-command rep...

I’m preparing a predictive analytics initiative for a finance-focused dataset stored in traditional relational tables and well-formatted spreadsheets. The core objective is to turn this structured historical information into forward-looking insights—think probability scores for default, churn, or other key risk and revenue indicators. You will take ownership of the full machine-learning pipeline: data ingestion from CSV/SQL sources, exploratory analysis, feature engineering, model selection and training, validation, and a concise performance report. I expect reproducible Python code (preferably using pandas, scikit-learn, XGBoost or similar), clear comments, and a short write-up that explains both the methodology and how to refresh the model with new data. Deliverables: &bull...

# Collaboration Opportunity: SafeLink AI **Project:** Explainable Phishing and Scam Message Detector **Duration:** 7–14 days **Collaboration type:** Unpaid academic / portfolio collaboration **Technology:** Python, Pandas, scikit-learn, Streamlit, Joblib, Git/GitHub ## 1. Project overview I am developing SafeLink AI as a college group experiential learning project. The objective is to build a simple web application that analyses text messages, predicts whether a message belongs to the classes supported by the selected dataset, highlights observable warning indicators, and provides general safety guidance. I already have a detailed project proposal explaining the objectives, scope, methodology, mathematical logic component, evaluation metrics, and expected deliverables. I am...

I need a Python-driven analysis of my market research data to pinpoint our ideal target audience. The raw survey and demographic files are already compiled; what I’m missing is a clear, data-backed profile that we can hand off to marketing and product teams. Here is what I expect: • Clean and structure the datasets so they’re ready for analysis. • Run exploratory data analysis and any relevant statistical tests or clustering to surface audience segments. • Translate the findings into a concise executive summary with charts and actionable insights. You are free to use pandas, NumPy, scikit-learn, or similar libraries, provided the final notebook or script is well-commented and reproducible. I’ll supply the data and be available for quick clarifications alo...

For my portfolio, I want to develop a complete Python data-science walkthrough based on a public healthcare dataset. The work should start with careful data cleaning (handling missing values, outliers, type conversions) and move through exploratory analysis before arriving at polished visual insights created with pandas, NumPy and Matplotlib. After the EDA, I would like you to implement at least one simple machine-learning model that suits the data—classification, regression or clustering—so feel free to choose whichever fits best once you have explored the variables and target. Please keep the modelling lightweight and well-commented so another student can easily follow the logic. Deliverables • Jupyter notebook (.ipynb) containing all code, visualisations and nar...