NumPy Jobs

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...

I need an experienced Full Stack AI/ML developer to build a modern, responsive AutoML web application with a professional dashboard. The application should allow users to upload CSV, Excel, and JSON datasets, perform automated data profiling, cleaning, preprocessing, feature engineering, and visualization, and automatically detect whether the problem is classification or regression. It should train and compare multiple machine learning models, recommend improvements such as handling missing values, SMOTE, feature selection, and overfitting reduction, and generate downloadable PDF reports, cleaned datasets, and trained models. The project should include secure Firebase authentication, an intuitive dashboard with interactive charts, cloud deployment, and a scalable architecture using Python ...

AI-Powered Sales Analytics Dashboard & Business Insights Developed an intelligent AI-Powered Sales Analytics Dashboard that transforms raw sales data into actionable business insights using Machine Learning and Business Intelligence techniques. The solution enables organizations to monitor sales performance, identify customer buying patterns, predict future sales trends, and make data-driven strategic decisions. The project leverages Python, Pandas, NumPy, Scikit-learn, and Power BI to clean, process, analyze, and visualize large volumes of sales data. Machine Learning algorithms are used for sales forecasting, trend analysis, customer segmentation, and predictive analytics, allowing businesses to anticipate demand, optimize inventory, and improve revenue growth. AI-driven insights hel...

I’m looking for a complete, end-to-end data science workflow that starts with pulling structured data from APIs and ends with a predictive model I can trust and iterate on. You’ll write clean Python code (Pandas, NumPy, Scikit-learn) that ingests the data, handles missing or inconsistent values, performs robust preprocessing, and walks through exploratory data analysis with clear, insightful visualizations using Matplotlib or Seaborn. Once the data foundation is solid, build and evaluate several machine-learning models focused on prediction, document why each algorithm was chosen, and compare their performance with the usual metrics. I value transparency, so every notebook or script must be well commented, and a short, readable methodology report should explain your decisions,...

Our raw sales dataset needs a full analytical pass that starts with rigorous data cleansing. The main focus is on standardising every field—dates, currencies, product codes, customer IDs—so the data can be trusted for downstream reporting. Once the formats are consistent, I want a concise exploratory analysis in Python (Pandas, NumPy, Matplotlib or Seaborn) that highlights high-level trends, seasonality, and any obvious outliers. If you spot missing values or duplicate rows along the way, feel free to resolve them; the primary acceptance criterion, however, is clean, consistently formatted data backed by a short insight summary. Deliverables • Python notebook or script that performs the cleaning and analysis • A cleaned CSV (or Parquet) file ready for further ...

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