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Muhammad Obaid Farooq

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Computer Science professional with strong expertise in Python, Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing (NLP)
Pakistan
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Résumé

Jobs verified_user 0% verified
  • P
    Machine Learning Engineer
    Punjab Oil Mills Limited
    Mar 2025 - Jul 2025 (5 months)
    Designed and deployed machine learning models for demand forecasting, supply chain optimization, and quality control.Built predictive analytics systems for production planning and inventory management, reducing operational costs and minimizing wastage.Implemented computer vision solutions for automated quality inspection of raw materials and finished goods.Developed customer behavior and sales prediction models using Random Forest, XGBoost, and regression algorithms.Performed data preprocessing, feature engineering, and hyperparameter tuning to improve model accuracy and scalability.Deployed ML models into production pipelines using Python, TensorFlow, and Scikit-learn, integrated with SQL databases.Conducted exploratory data analysis (EDA)
  • Daraz
    Daraz seller
    Daraz
    Feb 2022 - Aug 2025 (3 years 7 months)
Education verified_user 0% verified
  • HITEC University
    Bachelor of Computer Science
    HITEC University
    Oct 2021 - Aug 2025 (3 years 11 months)
    CGPA: 3.16/4.0Relevant Coursework: Artificial Intelligence, Machine Learning, Deep Learning
Projects (professional or personal) verified_user 0% verified
  • HITEC
    Plant Disease Detection
    HITEC
    Jan 2026 - Current (4 months)
    Developed a machine learning model using Convolutional Neural Networks (CNN) to classify plant leaf diseases from images. Trained on a labeled dataset with multiple disease categories, applying preprocessing and augmentation to enhance generalization. Achieved high classification accuracy in early plant disease detection for precision agriculture.
  • Inspiratek
    Image Caption Generator (Computer Vision + NLP)
    Inspiratek
    Nov 2025 - Dec 2025 (2 months)
    Created an AI system that generates natural language captions for images by combining CNN for feature extraction and LSTM for sequence generation. Trained on MSCOCO dataset with transfer learning, achieving meaningful and context-aware image descriptions.
  • H
    Healthcare Disease Prediction (Diabetes/Heart Disease)
    HITEC University,
    Nov 2024 - Feb 2025 (4 months)
    Built classification models using Logistic Regression, SVM, and Deep Learning to predict disease risks from patient health records. Performed feature selection, missing value imputation, and SMOTE for class balancing. Deployed model with Flask API for practical usage.
  • HITEC
    Infrastructure and Human Damage Detection in Disasters
    HITEC
    Oct 2024 - May 2025 (8 months)
    Built a multimodal disaster assessment system using deep learning models on images and text (tweets) from the CrisisMMD dataset. Utilized CNN (VGG16) for image classification and Word2Vec with Random Forest for text classification. Combined both modalities to accurately detect infrastructure and human damage in crisis scenarios.
  • Hitec University, Taxila
    Fraud Detection in Financial Transactions
    Hitec University, Taxila
    May 2022 - Jul 2022 (3 months)
    Implemented anomaly detection models to identify fraudulent transactions using supervised (Random Forest, XGBoost) and unsupervised (Isolation Forest, Autoencoders) methods. Applied feature engineering on transaction metadata and optimized models for real-time fraud detection.
  • H
    Big Data Pipeline for Real-Time Analytics
    HITEC University,
    Aug 2021 - Oct 2021 (3 months)
    Developed an end-to-end pipeline using Apache Spark and Kafka for real-time data streaming. Processed large-scale log data, implemented transformations, and visualized KPIs using Power BI dashboards. Deployed on AWS for scalability.
  • H
    AI-Powered Blog Series on Disaster Detection
    HITEC University,
    Mar 2021 - Apr 2021 (2 months)
    Authored a technical blog series demonstrating how AI can detect disaster-related damage using CNNs and Word2Vec. Focused on explaining deep learning concepts for real-world humanitarian applications in a clear, research-oriented format.
  • H
    Sentiment Analysis on Social Media Data
    HITEC University,
    Jan 2021 - Feb 2021 (2 months)
    Built an NLP pipeline using LSTM and BERT models to classify sentiments (positive, negative, neutral) in large-scale Twitter datasets. Preprocessed text using tokenization, lemmatization, and stop-word removal. Applied Word Embeddings (Word2Vec, FastText) for robust feature representation.