NumPy Jobs

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 ...

Please Sign Up or Login to see details.

I need a browser-based application where a user can drag-and-drop CSV, Excel, or JSON files, then talk to the system in plain English to fix messy data—especially rows or columns riddled with missing values. After the cleaning step, the same conversational interface should let the user request summary statistics, build quick charts, or even forecast trends. The workflow must always begin with a rock-solid imputation or other suitable treatment of nulls. Core flow I have in mind: 1. Upload: accept the three formats above and automatically profile the dataset. 2. Clean: surface any fields with gaps, let the user choose (or confirm an AI suggestion) for how those gaps are handled—mean/median substitution, forward fill, deletion, etc.—and produce a cleaned version that can b...