In this repository, I address missing values in the Prosper dataset using advanced data cleaning techniques. The refined data is then seamlessly uploaded to a pre-configured Azure Postgres Database via a Jupyter Notebook, showcasing efficient data management and cloud database integration.

Provisioning a jupyterlab and a postgres database, where they can see each other, and thus, we will successfully fill some aggregated data into the database from a notebook.

Practice with postgreSQL: set up a database with fabricated data from a fake company, run analysis queries, and finish analysis by connecting to the database in Pandas with Jupyter notebook.

Sentiment Analysis project that focuses on classifying the interactions of customers with support agents from different brands on X (formerly Twitter). The project is developed starting from an ETL process through advanced NLP techniques and ML models for classification, written in Python leveraging Jupyter Notebooks.