Hi, I am Amine, Data scientist with a strong background in statistical analysis and mathematics. I have extensive experience in solving real-world problems with the help of AI and Machine learning
I am problem-solving oriented with a keen ability to learn quickly and adapt to diverse settings
I can help you with every aspect of the data science workflow, including business understanding, data exploration, modeling, validation, deployment and monitoring.
My business intuition and communication skills allows me to make a quick impact and deliver high-quality results
Supervised and unsupervised machine learning algorithms, performance evaluation and validation, packaging and deployment
Univariate and bivariate analyzes, hypothesis tests, treatment of missing values and outliers
Textual data processing, topic modelling, text classification, sentiment analysis
Implementation of deep learning algorithms using neural network architectures
Creating a Data Lake on AWS, to analyze covid-19 effect on airbnb data and extract insights, using Spark and airflow
Big Data Analytics using elasticsearch, logstash, kibana and kafka with real time streaming data of bike sharing API
Sentiment Analysis and visualisation of tweets, using python, ELK, kafka and Docker
End-to-end data workflow with kafka, spark streaming, postgres, superset and Docker
Creating a Docker stack using MLflow, mysql and Minio to manage the lifecycle of TensorFlow models.
Training and deploying XGBoost as a web service Model with MLFlow, flask, Docker, Cloud Run and github actions.
Training and deploying LightGBM Model with MLFlow, Fastapi, App Engine and github actions for mental health risk prediction
Training and deploying a TensorFlow DNN model on Azure Machine Learning and azure App Service for diamond price prediction.
Training and deploying CatBoost Model with MLFlow, Flask, Docker, AWS ECS and github actions
RFM Segmentation, Cohort Analysis, Market Basket Analysis and Customer lifetime value
Benchmarking of different algorithms used fot time series forecasting (ARIMA, Prophet, LSTM)
Implemeting Recommender System using Deep Neural Networks architectures
Deploying machine lerning app with Streamlit for house price prdiction
Machine learning based customer churn prediction model, created with pycaret and deployed using streamlit
Deploying a Keras LSTM model with Flask and Docker for Spam detection
Smoker Detection deep learning model served via a Web App using TensorFlow, tensorflow-serving, flask and Docker compose
Face Recognition and Identification using python, openCV and deep learning
Text summarization using seq2seq model and attention mechanism
Real time object detection using OpenVC and Yolo V3
Real time object detection using OpenVC and Yolo V3
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