Python

Project Overview We are looking for a capable pre-final or final-year Computer Science / IT student to build an open-source Enterprise Data Analytics Dashboard that interfaces with SAP environments. The application will securely pull transactional data (such as simulated SAP sales, inventory, or procurement records) via open OData/REST protocols, store it in an open-source analytical database, and apply machine learning algorithms to automatically spot fraud, inventory stockouts, or supply chain anomalies. Key Features & Functional Requirements • SAP Open Data Integration: Establish an integration layer that connects to the SAP Business Accelerator Hub Sandbox APIs (or ingestion of standard SAP mock data generated via Python scripts). • Automated Data Pipeline (ETL): An open-source worker script that cleanses raw operational logs, extracts key numeric parameters, and stores them systematically. • Machine Learning Anomaly Engine: An isolation forest or clustering model that flags outliers in financial transactions or warehouse inventory levels (e.g., duplicate invoices, unexpected stock depletion). • Interactive executive Dashboard: A responsive, interactive analytical dashboard displaying real-time Key Performance Indicators (KPIs), trend predictions, and visual alert queues for flagged anomalies.

Project Overview We are looking for a capable pre-final or final-year Computer Science / IT student to build Snap Calorie, a next-generation mobile or web application designed to automate dietary tracking using Computer Vision. Instead of manually searching for and logging every single ingredient, users simply upload or take a live picture of their food plate. The system will leverage a Deep Learning / Object Detection pipeline to isolate multiple food items, estimate their volume/portion size, map them against a verified nutrition database, and present an instantaneous breakdown of total macro and micronutrients. Key Features & Functional Requirements • Computer Vision Detection Engine: An optimized object detection pipeline capable of identifying multiple distinct food items on a single plate simultaneously. • Portion & Volume Estimation: A heuristic or regression-based module that uses visual bounding boxes or reference objects to approximate weight/portion sizes (e.g., small, medium, large). • Nutrient Mapping Pipeline: An automated API worker that matches detected labels with exact food profiles to calculate calories, protein, carbohydrates, fats, and sodium. • Daily Analytics Dashboard: An intuitive dashboard displaying daily/weekly consumption progress against a user-defined caloric target using interactive charts. • Manual Correction Layer: A fallback interface allowing the user to easily adjust estimated portion sizes or edit a food label if the model misidentifies an item.

We are looking for a talented pre-final or final-year Computer Science / IT student to build NutriCoach, an intelligent, context-aware AI agent designed to generate hyper-personalized weekly meal plans. The system will ingest user health parameters (age, weight, goals, medical conditions like Type-2 Diabetes or Hypertension, and allergens) and use Generative AI with RAG (Retrieval-Augmented Generation) to create safe, clinically structured dietary plans. The system must cross-reference public medical/nutrition databases to strictly eliminate high-risk ingredients based on the user's health profile.

Build a web application that parses PDFs, extracts skills using Natural Language Processing (NLP), ranks resumes against a job description, and suggests keyword optimization.