Aymane BENBACER

- Implementation of Data Preprocessing and Machine Learning Pipelines: -> Developed data preprocessing and machine learning pipelines using Python, Keras, FastAPI, MongoDB, MLflow, Docker, LSTM, GBM, and CNN. -> Created autonomous ML modules with automatic tuning approaches. - Data Analysis and Reporting -> Conducted data analysis and reporting on customer feedback and geolocation data using React.js, Python, and TOUCAN TOCO. -> Led the creation of a new paid storytelling product that helps customers make informed decisions. - Data Processing and Anomaly Detection -> Processed and analyzed data for anomaly detection using Python, SQL, MongoDB, and GoLang. -> Developed modules for automatic data reliability enhancement. - NLP and LLM Applications -> Developed, using NLP and LLM (NLTK, Rasa, SpaCy, Word2Vec, and Dialogflow), a chatbot that is offered as an optional service to customers. The chatbot can understand customer needs and propose solutions such as remote box maintenance or troubleshooting guides. - Data Science Project Analysis -> Analyzed Data Science projects to identify and mitigate potential risks. -> Provided clear insights into the technical and functional aspects of projects, helping to avoid wasted time and effort.

- Creation and Prediction of Transportation KPIs (RandomForest, LGBM, LSTM, ARIMA, Decision Trees) -> Developed algorithms with optimized MAE (e.g., +/- 4 hours of deviation over 220 hours) enabled clients to avoid driver's hour violations, improve driver behavior, analyze activities by region, and optimize routes, fuel consumption, and CO2 emissions. - Back-end Programming -> Proficient in back-end programming languages and technologies, including Python, NumPy, Pandas, scikit-learn, Spark, Hadoop, and FastAPI. - Front-end Programming -> Skilled in front-end programming languages and frameworks, including Python, Flask, Plotly Dash, Matplotlib, Mapbox, and Plotly.

- Data Extraction, Transformation, and Loading -> Extracted, transformed, and loaded data using SQL, NoSQL, Pandas, and NumPy in Python, facilitating data access and preparation for analysis. - Integration and Mapping of External Data (Accident Data, CO2 Emissions, Traffic) -> Integrated and mapped external data sources (accident data, CO2 emissions, traffic) with internal data to enrich the data set for deeper analysis. - Development of a Data Prediction and Visualization Prototype -> Developed a data prediction and visualization prototype using scikit-learn, seaborn, Plotly, Bokeh, Pandas, and JavaScript.