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Eraj Muhammad Abid

Eraj Muhammad Abid

Software Engineering student with specialized focus on Data Science and Machine Learning

Talent
KarachiMember since June 26, 2026
PythonPandasscikit-learnTF-IDFStreamlitGroq APIEDAFeature engineering
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PythonPandasscikit-learnTF-IDFStreamlit

About

Software Engineering student with a specialized focus on Data Science and Machine Learning, operating at the intersection of technical innovation and practical business solutions. Proven ability to clean, analyze, and transform complex datasets into actionable insights, with hands-on experience building end-to-end ML pipelines, feature engineering, and model evaluation. Experienced in developing intelligent systems—including multi-agent AI architectures and conversational AI assistants—while managing projects through agile methodologies and deploying them via modern frameworks like Streamlit.

Known for a detail-oriented approach to problem-solving, clean code practices, and the ability to collaborate effectively in fast-paced group environments to meet tight deadlines. Brings a strong foundation in data structures and algorithmic thinking, with a growing interest in expanding into advanced AI research and scalable cloud deployments to create impactful, data-driven software solutions.

Experience

AI Internship (REMOTE) - Codantix Tech - Remote

(2025-08 - 2025-09)

  • Engineered an end-to-end NLP pipeline to process and classify a large-scale corpus of 44,000+ text articles, successfully identifying misinformation with high precision.
  • Designed a robust text preprocessing framework by implementing tokenization, stop-word elimination, and TF-IDF vectorization to convert unstructured textual data into high-dimensional numerical feature matrices.
  • Conducted comprehensive Exploratory Data Analysis (EDA) on class distribution, article length variation, and vocabulary density to mitigate model bias and guarantee balanced training data.
  • Trained and benchmarked multiple ML algorithms including Naive Bayes, Support Vector Machines (SVM), and Logistic Regression, optimizing hyperparameters to achieve a peak performance of AUC-ROC ≈ 0.90.
  • Validated generalization capabilities by creating a testing pipeline for real-world, out-of-dataset news content, ensuring the model's reliability for live text streams.

Education

Bachelor in Software Engineering - Software Engineering - Newport Institute Of Communication And Economics (2024-09)

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