👋 Hi, I'm Vineet Patel

🧩 Problem Statement

Financial education content is often unstructured and spread across lengthy documents, making it difficult for users to quickly find precise, context-aware answers.

💡 Solution

Built a GenAI-powered finance assistant using Retrieval-Augmented Generation (RAG) to enable document-based contextual Q&A.

🔑 Key Contributions

• Designed an end-to-end RAG pipeline for grounded responses
• Implemented semantic vector search using Pinecone
• Developed backend APIs with FastAPI and UI using Streamlit
• Deployed the application on AWS for scalable access

🛠️ Tech Stack

🧩 Problem Statement

Financial institutions face significant losses due to credit card defaults, while traditional rule-based systems fail to identify high-risk customers early.

💡 Solution

Developed a machine learning-based credit risk prediction system to proactively detect potential defaulters.

🔑 Key Contributions

• Performed EDA & feature engineering on credit data
• Trained and evaluated multiple ML models, selecting Random Forest
• Built and deployed a Streamlit app for real-time predictions

🛠️ Tech Stack

🧩 Problem Statement

Telecom companies experience high customer churn and revenue loss without clear visibility into churn drivers and at-risk customers.

💡 Solution

Conducted an end-to-end churn analytics project to identify churn drivers, quantify revenue at risk, and support data-driven retention strategies.

🔑 Key Contributions

• SQL-driven churn analysis using MySQL
• Python EDA to uncover behavioral churn patterns
• Designed Power BI dashboards for executive decision-making

🛠️ Tech Stack