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Zafii ML
@muh_zafran
From raw data to intelligent ML models, fast and accurate
Pakistan
Englisch, Urdu
Über mich
ML Engineer | Arch Technologies Intern | 98.6% spam detection & 99.2% digit recognition accuracy.
I build real, deployed ML web apps — not just notebooks. Live app: zafii-diabetes-prediction.streamlit.app
Skills: Classification, Regression, NLP, Time Series Forecasting, Computer Vision, Data Analysis (Python, TensorFlow, Scikit-learn, Streamlit, Pandas)
11 internship projects. 14 certifications (Simplilearn, GDG, Anthropic, Arch Technologies, and excelerate, etc.). Clean code. On-time delivery. 100% satisfaction guaranteed.
Message me — I respond within 1 hour.
Kompetenzen
Zafii ML
offline •
Meine Dienstleistungen
Maschinelles Lernen
I will build a custom machine learning model and web app for your dataset using python
KI-Integrationen
I will build a time series forecasting model for sales and stock prediction using ml
Portfolio
Arbeitserfahrung
Machine Learning Engineer
CS Technologies • Teilzeit
Feb 2026 - Present • 3 mos
Working as a part-time Machine Learning Engineer, designing and developing ML solutions for real-world business problems. Responsibilities include data preprocessing, feature engineering, building predictive models using supervised and unsupervised learning techniques, and deploying models as web applications using Python and Streamlit. Focused on delivering clean, well-documented, and production-ready code with measurable results
ML Engineer
IMS Peshawar • Teilzeit
Sep 2024 - Present • 1 yr 8 mos
Developing and deploying machine learning models as a part-time ML Engineer, working on real-world projects including predictive analytics, classification, and time series forecasting. Built and launched multiple ML web applications using Python, Scikit-learn, and Streamlit, including a live Diabetes Prediction app deployed on Streamlit Cloud. Focused on clean code, model accuracy, and practical AI solutions.
Internee
Arch Technologies Internship • Freiberufler
Jul 2025 - Dec 2025 • 5 mos
Completed an ML internship where I built and deployed 4 real-world machine learning projects: Email Spam Detection (98.6% accuracy), MNIST Handwritten Digit Recognition (99.2% accuracy), Housing Price Prediction using regression, and Iris Flower Classification. Worked with Python, Scikit-learn, TensorFlow, Pandas, and NumPy. Gained hands-on experience in data preprocessing, model training, evaluation, and optimization.
