Donation for child's Education!

Wanna make change and create impact !! Then why to wait for ngo's to reach out to us!! Let's Donate and create impact . It's just a ask for 100rs , your 100rs an change a child's life!!

Prachi Nimbalkar

Investment Memo Outline: EduMentor

Mudit Kumar Singh

Investment Memo Outline: EduMentor

Mudit Kumar Singh

Optimizing Material Flow: A Live Project Case Study on Petroleum Refinery

The project explores the efficient management of material flow at Indian Oil Corporation Limited’s refinery, focusing on the processes of procurement, tendering, transportation, and inventory management. It examines the structured roles within the Material and Contracts Department, highlighting each stage from purchase requisition to material arrival and inspection. Emphasis is placed on the perpetual inventory system, the use of SAP software, and the ABC analysis method for inventory categorization. The study also discusses the importance of quality control during material reception, proper record-keeping, and the structured approach to storing and handling materials to support smooth plant operations.

Fashion ReVibe – Combines “revitalize” with the youthful “vibe” of your audience.

Fashion ReVibe breathes new life into TrendTide’s fast fashion brand by blending sustainability, inclusivity, and digital-first strategies tailored for Gen Z and Millennials. Anchored in data-driven insights, the campaign includes influencer-driven Instagram Reels, community-driven “StyleSwap” pop-ups, an AI-powered style quiz, and limited-edition drops—all designed to re-engage a youth-driven audience. By addressing key consumer demands—sustainable materials (30%), inclusive sizing (25%), and authentic, interactive content—the project aims to increase Instagram engagement to 4%, reduce churn to 35%, and boost online sales by ₹1–1.4 crore, revitalizing TrendTide’s brand for a more ethical and vibrant future.

Predicting Hospital Readmission Risk Among Diabetic Patients

This project analyzes the readmission patterns of diabetic patients at HealthFirst Multispeciality Hospital. Using a dataset from the UC Irvine Machine Learning Repository, it leverages data cleaning, Principal Component Analysis (PCA), Random Forest, K-Means clustering, and logistic regression to predict readmission risk. While Random Forest achieved moderate accuracy (48.25%), logistic regression underperformed due to data imbalance. Key insights revealed that treatment complexity, hospitalization history, age-related risks, and emergency visits significantly influence readmission. Actionable strategies include optimizing medication regimens, enhancing telehealth monitoring, and leveraging data analytics to proactively identify high-risk patients, improving patient care and hospital efficiency.