Hi, I'm Ehtisham Afzal
Data Scientist with a strong foundation in statistics, machine learning, and end-to-end data solutions. Proven ability to build predictive models, perform deep EDA, and automate pipelines using Python, SQL, and Flask. Passionate about transforming raw data into actionable decisions through analytical thinking and modern AI tools.
About Me
Bachelor of Science in Information Technology
Quaid-i-Azam University, Islamabad
Project Management
Coursera
Microsoft Technologies Certification
Pak Finland Capacity Building Project (MUXBAY)
Freelance Data Scientist
Remote | Project-based via Digital Agencies
- • Performed Exploratory Data Analysis (EDA) on structured datasets to uncover trends, identify outliers, and inform feature engineering decisions using Pandas, Seaborn, and Matplotlib
- • Conducted statistical hypothesis testing and applied descriptive/inferential statistical techniques to validate assumptions and improve data quality
- • Built and evaluated predictive models using Linear Regression, Logistic Regression, Decision Trees, and Ensemble methods for both regression and classification problems
- • Developed and compared models using performance metrics such as accuracy, precision, recall, F1-score, RMSE, and AUC-ROC
- • Tuned hyperparameters using GridSearchCV and RandomizedSearchCV to optimize model performance across multiple cross-validation folds
- • Created modular and reusable scripts for data preprocessing, feature scaling, encoding, and train-test splitting to streamline experimentation
- • Documented entire project workflows, including assumptions, model selection rationale, and evaluation results for stakeholders and team sharing
- • Assisted clients by debugging Python code, enhancing academic assignments, and developing small-scale machine learning and data analysis projects
Skills & Expertise
- • Python
- • Model Development (Regression, Classification, Forecasting)
- • Model Evaluation & Validation
- • Hyperparameter Tuning (Grid Search, Random Search)
- • Model Deployment (Flask APIs, Docker, Streamlit)
- • TensorFlow, scikit-learn
- • Data Preprocessing & Cleaning
- • Feature Engineering & Selection
- • Exploratory Data Analysis (EDA)
- • Data Visualization (Matplotlib, Seaborn)
- • Statistical Analysis & Hypothesis Testing
- • Predictive Analytics & Business Intelligence
- • SQL (MySQL, PostgreSQL)
- • Data Modeling & Schema Design
- • GitHub
- • Software Design Principles
- • Software Design Patterns
- • Full SDLC Understanding (Requirement Analysis → Deployment)
- • Agile Methodologies (Scrum)
- • Sprint Planning & Stand-ups
- • Task Management (ClickUp)
My Services
Comprehensive data science and machine learning services to help businesses leverage their data for strategic advantage.
End-to-end ML pipeline development including model training, validation, and deployment using Python, TensorFlow, and scikit-learn.
- • Predictive Modeling
- • Classification & Regression
- • Model Optimization
- • Hyperparameter Tuning
Comprehensive data analysis with statistical insights and interactive visualizations to drive business decisions.
- • Exploratory Data Analysis
- • Statistical Testing
- • Data Visualization
- • Business Intelligence
Full-stack web applications with Flask, Streamlit, and database integration for data-driven solutions.
- • Flask APIs
- • Streamlit Dashboards
- • Database Integration
- • Responsive Design
Database architecture, optimization, and management for efficient data storage and retrieval.
- • SQL Optimization
- • Schema Design
- • Data Modeling
- • Performance Tuning
Advanced deep learning models for computer vision, NLP, and complex pattern recognition tasks.
- • CNN Development
- • Image Classification
- • Neural Networks
- • Model Deployment
Transform business data into actionable insights with forecasting, trend analysis, and performance metrics.
- • Demand Forecasting
- • Trend Analysis
- • KPI Development
- • Performance Metrics
GitHub Statistics
Featured Projects
A showcase of my work across different domains of data science, machine learning, and software development.
Python Projects
Created a responsive web platform for scheduling and managing doctor appointments with authentication and admin dashboards. Integrated CRUD operations with a clean UI.
A web app to track your mood through journal entries, visualize emotional trends with charts, and uncover thought patterns using dynamic word clouds.
Developed a bike rental solution enabling users to book, track, and return bikes with cost calculation and time logging.
A Python-based library management app built with Streamlit, offering separate interfaces for admins and students to manage books, users, and circulation — all without the need for a database server.
Machine Learning Projects
Developed a time series forecasting model using XGBoost to predict item-wise sales and optimize stock levels. Achieved a 20% reduction in overstock incidents.
Engineered an end-to-end ML pipeline integrated into a supply chain ERP system to forecast demand, recommend restocking, and optimize warehouse utilization.
A machine learning project that classifies tumors as benign or malignant based on medical features. It helps support early detection of breast cancer using predictive analytics.
Implemented dual-use prediction systems: one to forecast dynamic flight pricing using regression, and another to classify loan default risk using supervised learning.
Deep Learning Projects
Built a CNN-based classifier to detect brain tumors from MRI images with 95% accuracy, using data augmentation techniques.
Trained a hybrid CNN-LSTM model to classify spoken emotions using MFCC features from audio inputs, with real-time voice input support.
Generate stunning images from text prompts using Hugging Face’s powerful diffusion models. Supports multiple styles including photorealism, anime, and digital art via a clean Gradio web interface. Built with PyTorch + Diffusers.
Designed a multi-output deep learning model to simultaneously predict a person's age and gender from facial images, deployed via a live webcam demo.
Latest Blog Posts
Sharing insights, tutorials, and experiences in data science, machine learning, and software development.
A comprehensive guide to building end-to-end machine learning pipelines that scale in production environments.
Learn advanced EDA techniques to uncover hidden patterns and insights in your datasets using pandas and seaborn.
Best practices for deploying ML models as REST APIs using Flask, including error handling and performance optimization.
Implementing CNN architectures for image classification tasks with TensorFlow and practical deployment strategies.
Understanding when and how to apply different statistical tests to validate your data science findings.
Learn how to build robust time series forecasting models using XGBoost with feature engineering techniques.
Project Standards & Development
Delivering high-quality machine learning and Python projects with industry-standard practices and comprehensive documentation.
High-standard machine learning and deep learning projects with optimized performance and accuracy.
Well-structured, maintainable Python code following PEP 8 standards and best practices.
Detailed documentation including API docs, user guides, and technical specifications.
Efficient algorithms and optimized code for maximum performance and scalability.
- Agile Methodology: Iterative development with regular sprint planning and reviews
- Test-Driven Development: Comprehensive testing with unit tests and integration tests
- Version Control: Git-based workflow with feature branches and code reviews
- CI/CD Pipeline: Automated testing and deployment for reliable releases
- Code Quality: Static analysis, linting, and adherence to coding standards
- Documentation First: Clear documentation throughout the development process
- Production-Ready Code: Scalable, maintainable code ready for deployment
- Model Performance Reports: Detailed analysis of model accuracy and metrics
- API Documentation: Complete REST API documentation with examples
- Deployment Guides: Step-by-step deployment instructions and configurations
- Training Materials: User guides and training documentation for end users
- Ongoing Support: Post-delivery support and maintenance recommendations
Code Quality
- • PEP 8 compliance
- • Type hints and annotations
- • Comprehensive error handling
- • Memory optimization
ML Best Practices
- • Cross-validation techniques
- • Feature engineering pipelines
- • Model versioning
- • Performance monitoring
Documentation
- • README with setup instructions
- • Inline code comments
- • API reference documentation
- • Architecture diagrams
Let's Connect & Collaborate
Whether you have a groundbreaking data science project, need expert consultation, or simply want to explore the fascinating world of machine learning together, I'm here to help. Let's turn your data into meaningful insights and innovative solutions.
Email Me
Drop me a line for project discussions, collaborations, or just to say hello!
ehtisham1053@gmail.comWhat We Can Discuss
Have a data science project in mind? Let's discuss your requirements and explore possibilities.
Interested in data science trends, career advice, or just want to connect? I'm always up for a good conversation.
Need a detailed discussion about your ML/AI needs? Let's schedule a proper meeting to dive deep.
Based in Islamabad, Pakistan
Available for remote work globally
Response Time: Usually within 24 hours
Best Time to Call: 9 AM - 8 PM PKT (UTC+5)
Ready to Start Your Data Science Journey?
From initial consultation to final deployment, I'm here to guide you through every step of your data science project. Let's transform your ideas into reality.