Muhammad Basharat Asghar

I am a dedicated Data Science and AI professional with a strong foundation in Python, Statistics, and Machine Learning. My expertise includes Exploratory Data Analysis (EDA), data preprocessing, feature engineering, and developing end-to-end machine learning pipelines for both supervised and unsupervised learning tasks. I am proficient in SQL and have hands-on experience in Deep Learning, particularly with Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) using TensorFlow. I am passionate about leveraging data-driven solutions to solve real-world problems and continuously advancing my skills in the field of AI.

🩺 Diabetes Risk Prediction – End-to-End Machine Learning Web Application

This project is an end-to-end machine learning solution designed to predict the risk of diabetes based on lifestyle, demographic, and clinical health indicators. The goal is to help identify individuals who may be at higher risk of diabetes so that early interventions and preventive care can be encouraged.
The complete system follows industry best practices, including:
Modular coding architecture
Feature engineering
Data transformation
Machine learning pipelines
Model training and evaluation
Web application development using Flask
Cloud deployment with Render.com

🎵 Music Genre Classification – End-to-End Machine Learning Project

This project is a production-grade machine learning system that predicts the genre of a music track based on extracted audio features.
It follows industry best practices including modular code structure, dimensionality reduction, model validation, hyperparameter tuning, and cloud deployment.
The system is built to be scalable, maintainable, and deployment-ready, making it suitable for real-world ML applications and portfolio presentation.

Used Car Price Prediction (Pakistan Market)

This project predicts used car prices based on scraped data from Pakwheels.
It demonstrates the Data Science workflow:
data collection → cleaning → EDA → feature engineering → model building → evaluation.

Telecom Customer Churn Prediction

This project is an end-to-end Machine Learning pipeline to predict whether a telecom customer will churn (leave the service) or not.
The solution covers everything from EDA → Model Building → Hyperparameter Tuning → Threshold Optimization → Deployment with Streamlit, and is structured following data science best practices.

House Price Prediction

This is an end-to-end machine learning project that predicts house prices using real estate data.
Key highlights:
✅ Cleaned and engineered features with domain knowledge (sqft conversions, bath/bed ratios, etc.).
✅ Applied outlier detection (location-based price sanity checks).
✅ Built and compared multiple models.
✅ Saved and deployed models using joblib + flask.
This project demonstrates the full data science pipeline: from raw data → preprocessing → feature engineering → modeling → deployment.