imran ghafoor
I build AI applications with Python, machine learning, computer vision, and large language models. My projects include plant disease detection, customer churn prediction, and DocuM... Show moreI build AI applications with Python, machine learning, computer vision, and large language models. My projects include plant disease detection, customer churn prediction, and DocuMind AI—a document assistant that combines OCR and retrieval-augmented generation (RAG).
I enjoy working across the development process, from preparing data and evaluating models to building interfaces and deploying applications. My toolkit includes PyTorch, Flask, React, and tools for document processing.
Here on Coder Legion, I share what I build, the challenges I encounter, and the lessons I learn. I’m interested in open-source collaboration and opportunities to solve practical problems with AI. Show less
Python
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Artificial Intelligence
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Machine learning
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Computer Vision
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SQL
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Scikit learn
Machine Learning Intern
Neurofive solutions
Jul 2026 - Aug 2026
• Built and deployed Daily Stress Predictor (Streamlit), comparing 3 regression models in one scikit-learn pipeline
Machine Learning Interne
Teyzix Core
Jun 2026 - Jul 2026
• Delivered end-to-end ML projects, from data preparation to deployment, including Customer Churn Prediction and Smart Electricity Consumption Prediction (Flask app)
Riphah InternationalUniversity
Bachelor of Science (BS), Artificial Intelligence
2024 - 2028
Currently pursuing a Bachelor of Science in Artificial Intelligence at Riphah International University, focusing on Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing.
Gaining practical experience through hands-on AI projects, including predictive modeling, computer vision applications, and LLM-powered systems. Passionate about developing intelligent, scalable solutions to real-world problems.
Smart Electricity Consumption Prediction
Visit Project open_in_new
Jun 2026 - Jul 2026
Compared 7 regression models on a self-created 600-record dataset (best: R² 0.964, MAE 1.57 kWh, 5-fold CV) and built a Flask app that returns daily usage, bill estimates and energy-saving tips. Results are based on synthetic data.
PlantScan AI: Plant Disease Detection
Visit Project open_in_new
Mar 2026 - Jun 2026
Built a web app that diagnoses plant diseases from a leaf photo and recommends treatment. Fine-tuned EfficientNetB0 and MobileNetV2 with two-phase transfer learning on 38 PlantVillage classes (98% and 95% validation accuracy). Added live camera capture with brightness, sharpness and motion checks for auto-capture.
verified
Introduction to Artificial Intelligence
Simplilearn
Issued Dec 2025
Credential ID 9559811
LLM agents, retrieval-augmented generation (RAG), AI agent security, and real-time object detection. I’m also learning how to evaluate AI systems more rigorously and deploy reliable applications.
• Named to the Dean’s Honor List and Vice Chancellor’s Honor List for academic performance, receiving certificates of recognition.
• Built and deployed DocuMind AI, a document assistant combining OCR, RAG, and open-weight language models.
• Developed a plant disease classification project covering 38 classes using transfer learning.
• Completed two internships in AI/ML.
My laptop has 8 GB of RAM, so my bigger machine learning experiments get a temporary home on Colab or Kaggle.
A model is only as convincing as the data you didn’t train it on.
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