Muhammad Usman Zahid

A skilled professional with expertise in machine learning, data analysis, and statistical modelling. Demonstrates strong problem-solving abilities and project management skills, complemented by analytical thinking and a background in electrical engineering. Committed to leveraging technical competencies to drive innovation and efficiency in complex projects.

Early Detection of Parkinson's Disease Using voice-Based Acoustic Features.

• Developed machine learning models using Logistic Regression, Random Forest, SVM and XG Boost.
• Perform feature engineering on jitter, shimmer, HNR and spectral features.
• Achieved high classification performance with explainability using SHAP.

• Python (Pandas, NumPy, Scikit-learn, Matplotlib and Seaborn)
• Jupyter Notebook , Google Colab
• Git and GitHub (Basic)

Machine Learning And Data Science

• Supervised and Unsupervised Learning
• feature engineering and scaling
• Model Evaluation (Accuracy, Precision, Recall, F1-Score and ROC-AUC )

Data Analysis

• Data cleaning and Preprocessing
• Exploratory Data Analysis
• Visualization
• Statistical analysis

• Available for full time work from February 2026
• Eligible to apply for Graduate Route (PSW)