Telecom customer churn prediction
An end-to-end churn pipeline: SQL data normalization, exploratory analysis, model training in Python and an interactive Streamlit app that ranks at-risk customers.
- Churn prediction
- SQL
- Streamlit

Data scientist and automation developer, Faisalabad, Pakistan
I build predictive models and the software that puts them to work.
I take a problem from raw data to something people can use: cleaning and exploring the data, training the model (churn prediction, recommendation systems), then wrapping it in an app or pipeline.
Lately that work is agentic. I build LangGraph systems that retrieve, grade their own answers and correct course, a voice assistant that works in Urdu and English with 35 tools, and pipelines that research, write and publish on their own.
I am a data scientist at Optimum Tech and a computer science student at the University of Agriculture, Faisalabad. I also take freelance work: ML models, RAG chatbots, data pipelines and automation.
An end-to-end churn pipeline: SQL data normalization, exploratory analysis, model training in Python and an interactive Streamlit app that ranks at-risk customers.
A LangGraph agent that routes each question, retrieves with dense plus BM25 search, reranks with a cross-encoder, grades what it found and corrects itself when the answer is weak.
An Urdu and English voice assistant behind a wake word and PIN, driven by a LangGraph agent with 35 tools and RAG over local files.
An agentic RAG chatbot for a luxury fashion brand, built as a LangGraph ReAct agent running on Groq.
A Streamlit app where a Gemini-powered LangGraph workflow writes, themes, illustrates and sends personalised bulk email.
A multi-agent pipeline that finds a trending data science topic, researches it, writes a post, fact-checks it and publishes it to LinkedIn.
A Flask and MySQL web app for scheduling university classes, with Admin, Teacher and Student roles, a 7-table schema and automatic clash detection.
Six TensorFlow/Keras projects in one repo: MNIST with an ANN and a CNN, CIFAR-10, Cats vs Dogs with transfer learning, a tuned California Housing ANN and an LSTM next-word predictor trained on Kafka's The Metamorphosis.
Four scikit-learn projects in one repo: a heart disease classifier, insurance cost regression with random forests, an end-to-end housing price pipeline and a movie recommender.
A desktop app (Python + PyQt6 + SQLite) to keep track of your prize bonds, record draw results, and let a win-checking agent find winners for you. No win is recorded until you approve it.
Personal portfolio website (Flask)
Small RAG and LangGraph agent experiments
Exploratory data analysis and BI projects in Python and Excel
A retrieval-augmented chatbot built with LangGraph, LangChain, and Gemini. It features a FastAPI backend and web UI wired around a self-correcting RAG pipeline. It answers queries using uploaded documents, targeted websites, or live web search fallbacks—evaluating and correcting its own answers before returning them.
Python-based ETL pipeline that uses Selenium to scrape student results from the UAF LMS portal and Pandas to clean data, calculate GPAs, and track historical academic performance.
An LLM-driven agentic RAG system built on LangGraph with hybrid search and a Streamlit UI.
LangGraph AI agent using Gemini to write and send personalized bulk emails with custom themes.
LangGraph AI shopping assistant using agentic RAG, and FastAPI.
LangGraph multi-agent pipeline using LLM to research, fact-check, and auto-publish LinkedIn posts.
Assistants that answer from real product and document data, and decide for themselves when to look something up instead of guessing.
Multi-step agents that plan, call tools, check their own output and finish the job, from research and writing to fact-checking and publishing.
Neural networks built and trained end to end, from CNNs and transfer learning to sequence models for text generation.
Predictive models from feature work to evaluation, including customer churn prediction and recommendation systems.
The whole lifecycle: cleaning and exploring data, modelling it, and turning the result into insight people can act on. This is my current role at Optimum Tech.
LLM applications with better retrieval: hybrid dense and BM25 search, cross-encoder reranking, and agents that grade what they found and correct themselves.
My main language for data work, automation and small web apps, from Selenium scrapers to Flask and Streamlit interfaces.
Professional web development on Laravel at Optimum Tech and IMPEKT Limited, before moving into data science.
Open to data science, AI and automation projects. Send a message and Asad will reply by email.