Hello, I'm

Raj Jaiswal

Aspiring

I build practical AI applications using Machine Learning, FastAPI, LangChain, LLMs and modern web technologies.

Developer Illustration

About Me

Get to know me

I'm a B.Tech Computer Science student passionate about Machine Learning, Artificial Intelligence, and Data Science. I enjoy building practical AI applications that solve real-world problems.

My goal is to become a Machine Learning Engineer by continuously learning, building projects, and improving my problem-solving skills.

🎓 Education

B.Tech (CSE)

💻 Interests

Machine Learning & AI

🚀 Current Focus

Building AI Projects

🎯 Goal

ML Engineer

Skills

Technologies I work with

Python

Pandas

NumPy

Matplotlib

Seaborn

Scikit-Learn

TensorFlow

FastAPI

SQL

Git

Featured Projects

Some things I've built recently

01

YouTube Video Chat Assistant

An AI-powered Chrome Extension that enables users to chat with any YouTube video's transcript. It uses Retrieval-Augmented Generation (RAG) with LangChain, FAISS, and Google Gemini Embeddings to deliver context-aware answers based solely on the video's content.

Key Features

  • ✔ Chat with any YouTube video's transcript
  • ✔ RAG pipeline with semantic search using FAISS
  • ✔ Context-aware responses powered by Google Gemini
  • ✔ Chrome Side Panel Extension with custom HTML/CSS/JS UI
  • ✔ FastAPI backend with automatic transcript extraction
Python FastAPI LangChain Gemini API FAISS Chrome Extension HTML CSS JavaScript
View on GitHub →
02

Telecom Customer Churn Prediction Platform

Built an end-to-end machine learning platform that predicts telecom customer churn and translates model predictions into business-friendly insights using Google Gemini and SHAP Explainability. The application also includes an interactive strategy simulator that helps evaluate customer retention initiatives through what-if scenario analysis.

Key Features

  • ✔ Predict customer churn risk with confidence scores in real time
  • ✔ Generate AI-powered business explanations and actionable retention recommendations using Google Gemini
  • ✔ Explain model predictions with SHAP and interpretable AI
  • ✔ Simulate customer retention strategies and compare their impact on overall churn rates
Python Pandas Scikit-learn SHAP Google Gemini API Streamlit Joblib
View on GitHub →
03

🎬 Movie Recommendation System

A full-stack movie recommendation platform powered by Machine Learning. Built with FastAPI and a Content-Based Filtering engine, it delivers intelligent movie suggestions using TF-IDF vectorization and Cosine Similarity on the TMDB 5000 dataset. The application also features fuzzy movie search, trending movies, detailed movie information, and a fully responsive user interface.

Key Features

  • ✔ Intelligent Movie Search
  • ✔ Content-Based Recommendations
  • ✔ Trending Movies Section
  • ✔ Deployed on render
  • ✔ Responsive Full-Stack UI
Python Pandas Scikit-Learn FastAPI HTML CSS JavaScript
View on GitHub →

Let's Connect

Open to internships, collaborations, and conversations about AI and Machine Learning.