Walid M.

Key Skills

Software

Other

Top Subject Matter

Vibro-acoustic / structural dynamics model evaluation

Credit default prediction / supervised ML

Top Data Types

Computer Code Programming

Top Task Types

Classification

Freelancer Overview

FRF Comparison Tool – Vibro-Acoustic Data Analysis (Machine Learning/Data Science internship project). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Science in Computer Science Engineering, Mohammed VI Polytechnic University (UM6P) (2023) and High School Diploma, Al Imam Al Ghazali High School (2023). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

UM6P Credit Default Prediction Association – classification modeling

OtherClassification

Built machine learning classification and evaluation workflows using supervised learning algorithms for credit default prediction. Performed exploratory data analysis, feature engineering, and model selection using cross-validation. Evaluated model performance with classification metrics such as F1-score. • Implemented regression and multiple classifiers (e.g., trees, random forest, KNN) • Performed preprocessing, EDA, and feature engineering • Used grid search and K-fold cross-validation • Compared models and reported F1-score results

2026 - Present

Machine Learning and Data Science Intern (Vibro-Acoustic FRF Analysis) - Stellantis

Classification

Developed and implemented a Django-based web application for vibro-acoustic Frequency Response Function (FRF) comparison in structural dynamics analysis. Built evaluation workflows using correlation metrics and error measures to support machine learning model assessment. Applied Python, NumPy, and SciPy for data processing, similarity computation, and visualization-ready outputs. • Implemented FRF correlation metrics including FRAC, RMS Error, Peak Picking, and Nyquist • Performed signal processing, data cleaning, and similarity analysis for model evaluation • Developed and deployed a frontend to visualize comparisons using HTML, CSS, JavaScript, and Matplotlib • Used regression and classification concepts to guide feature engineering and model evaluation

2026 - 2026

FRF Comparison Tool – Vibro-Acoustic Data Analysis (Machine Learning/Data Science internship project)

Other

Developed and implemented an FRF analytics workflow to assess machine learning model similarity and performance in structural dynamics analysis. Used correlation metrics and error measures to evaluate outputs and guide model evaluation. Built supporting visualization and deployment components to make the evaluation results accessible. • FRAC, RMS Error, Peak Picking, and Nyquist metrics • Applied signal processing and similarity assessment • Performed data cleaning and AI-assisted evaluation • Implemented visualization tools and deployment for analysis

2026 - 2026

Education

Work History