Salsabil Rouahi

🚀 Turning climate data into actionable insights with Machine Learning - Designed and implemented advanced predictive models to forecast environmental, economic, and social impacts of hydrometeorological hazards, combining historical data analysis and Machine Learning techniques. - Developed data pipelines and simulation frameworks to model the evolution and intensity of climatic risks over time, enabling data-driven decision making for vulnerable sectors. - Identified high-risk infrastructures and economic activities using predictive analytics, providing actionable insights for mitigation strategies. - Applied tools and techniques: Python, R, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, simulation models, and advanced statistical methods. - Delivered impactful visualizations and reports that informed strategic planning and risk management at EY. Key Skills: Predictive Modeling | Machine Learning | Data Analysis | Simulation | Risk Assessment | Data-driven Decision Making

🚀 Applying Machine Learning to high-dimensional metabolomics and textual data for predictive modeling and classification - Developed ML pipelines for classification and prediction on high-dimensional metabolomics datasets, applying Random Forest, XGBoost, and supervised models to identify metabolic patterns and biomarkers. - Applied feature selection, dimensionality reduction, and preprocessing techniques to improve model performance and interpretability. - Implemented clustering algorithms (K-means, hierarchical) for unsupervised pattern discovery in complex biological data. - Conducted document classification and NLP tasks, processing textual experimental data to extract insights and support research decisions. - Built predictive models for disease/metabolic state classification, translating raw omics and textual data into actionable insights. - Tools & techniques: Python, R, Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, Matplotlib, Seaborn, PCA, clustering, Random Forest, XGBoost, feature engineering, NLP. - Delivered visualizations, reports, and insights that guided experimental design and decision-making in the BIMS laboratory. Key Skills: Machine Learning | Classification | Prediction | High-dimensional Data | Feature Engineering | Clustering (K-means) | Random Forest | XGBoost | NLP & Document Classification | Data Science | Bioinformatics | Python | R

🚀 Turning network and service data into actionable insights with predictive modeling - Collected and processed network performance and client service data to identify patterns and recurring issues. - Developed basic predictive models and data pipelines to anticipate connectivity problems and optimize WiFi deployment. - Applied data preprocessing, visualization, and exploratory data analysis to extract actionable insights from field data. - Supported the optimization of telecom services using structured data-driven recommendations. - Tools & techniques: Python, R, Pandas, NumPy, Excel, data visualization, basic predictive modeling. Key Skills : Predictive Modeling | Data Pipelines | Data Analysis | Visualization | Python | R | Network Analytics