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marbel89/README.md
  • 👀 I’m interested in Python, IT Security, Data Engineering, DevOps
  • 🌱 I’m currently focussing on containerisation
  • ⚡ Fun fact: The "Simpson's Paradox" is a statistical phenomenon where a trend appears in different groups of data but disappears or reverses when these groups are combined. It puts the spotlight on looking for underlying details and not drawing conclusions solely based on aggregated data.

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  1. Machine-Learning-Production-Pipeline Machine-Learning-Production-Pipeline Public

    A machine learning pipeline for customer churn prediction, including data processing, model training and API integration.

    Python

  2. Data-Ingestion-Pipeline-Deployment Data-Ingestion-Pipeline-Deployment Public

    Creates an automated pipeline from a database via bash and Python. Focus is on unit-testing and error logging.

    Python

  3. DeepLearning_TimeSeries DeepLearning_TimeSeries Public

    Forecasting time series using Deep Learning models. Contains implementations of data windowing, model baselines, and the main script for training, plotting, and evaluating the models.

    Python

  4. Machine-Learning-Hyperparameter-Tuning Machine-Learning-Hyperparameter-Tuning Public

    Performing hyperparameter tuning using RandomizedSearchCV for various machine learning models. The project demonstrates how to tune and evaluate models such as RandomForestClassifier and GradientBo…

    Python

  5. Multiprocessing-and-Parallelization-Playground Multiprocessing-and-Parallelization-Playground Public

    Showcases multiprocessing for ML, multi core hyperparameter tuning, caching and generally processor and memory efficient techniques.

    Python

  6. TimeSeries_SARIMA TimeSeries_SARIMA Public

    Forecast model for number of antidiabetic drug prescription, starting with EDA, finding an appropriate (statistical) model, comparing and evaluating metrics, as well as possible deployments and pip…

    Jupyter Notebook