Welcome to my GitHub!

Dedicated and result-driven Data Science Trainer with strong expertise in Statistics, Machine Learning, and Data Analytics. Skilled in providing hands-on and engaging training with a focus on practical implementation, real-world case studies, and industry readiness. Adept in simplifying complex concepts and empowering learners through structured teaching methodologies and clear outcomes.

• 🔭 Data Analyst & Trainer — Delivering hands-on training in Python, Statistics, and Machine Learning
• 🤖 Machine Learning Enthusiast — Building models, clustering (PCA, DBSCAN), time series (ARIMA, Prophet), and computer vision basics
• 📊 Data Storytelling — Focusing on data preprocessing, feature selection, statistical analysis, and visualization tools
• 🎓 Educator — Creating learner-centric sessions, interactive content, and mentoring for real-world applications
• 🌱 Currently exploring: Advanced ML workflows, image preprocessing, and interactive dashboards
• 💼 Portfolio: Check out my projects on GitHub

• 2+ years of experience in teaching and training AI/ML and Data Science concepts
• Strong foundation in Statistics, Probability, and Data Analytics for real-world applications
• Delivered hands-on training with end-to-end projects and mentoring for learners
• Designing structured curriculum, assignments, and assessments, including the creation of effective teaching materials like presentations, cheat sheets, and content guides
• Skilled in creating learner-centric and interactive training sessions

• Core Data Science & ML: Machine Learning, Statistics, Data Analysis, Statistical Analysis, Predictive Modeling, Clustering, Time Series
• Deep Learning: Neural Networks, Frameworks (TensorFlow, PyTorch)
• Web & Deployment: Flask (for ML model serving/apps)
• Image & Computer Vision: Image Preprocessing (via Roboflow)
• Tools & Visualization: Power BI, Tableau, Excel, Matplotlib, Seaborn

🔹 Forecasting Retail Demand : Built a forecasting system for retail demand prediction using ARIMA, Moving Averages, and Time Series models.
🔹 Image Recognition & Analysis : This project explores and analyzes images of the 12 Wonders of the World using computer vision and basic data analytics techniques.

🔹 Wine Quality Clustering : Performed PCA and DBSCAN clustering to classify wine quality based on physicochemical properties.

🔹 EDA & Machine Learning Models : Exploratory Data Analysis and model building for structured datasets.

More notebooks in my pinned repos — feel free to explore & fork!

• 💼 LinkedIn
• 📧 Email: shahmarahiman6@gmail.com
• 💬 Open to: collaborations on educational content, student queries, or data/ML projects

Thanks for stopping by! 🌟 If you find my work useful, give my repos a ⭐ or drop a message — I love discussing stats, ML, and data stories.

Happy learning & analyzing! 📈