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A collection of projects and notebooks covering the end-to-end data science workflow. The repository includes: Data Analysis: data cleaning, descriptive statistics, and exploratory data analysis; Machine Learning: predictive modeling, regression, and classification; Visualization & Dashboards: interactive insights and reporting with Python.

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Data Science Portfolio

Welcome to my Data Science portfolio! This repository contains a collection of projects that demonstrate my skills in Data Analysis, Machine Learning and Artificial Intelligence. Each project is organized in its own folder and includes a detailed explanation, dataset (if public), code and results.


About Me

🎓 Graduated in Computer and Automation Engineering and Corporate Legal Practitioner, with a Master in Data Science.
💼 Experienced professional working in Guardia di Finanza since 2019, with strong skills in leadership, teamwork and problem-solving under high standards.
💻 Passionate about data-driven solutions, AI and software development.


Projects Overview

Projects Overview

Project Description Skills & Tools
Dataset Analysis Explore and analyze real-world datasets, perform data cleaning, visualization and generate actionable insights. Python, Pandas, NumPy, Matplotlib, Seaborn, Jupyter, R
ML Projects Develop and train machine learning models for prediction and classification tasks, evaluating performance with metrics and visualizations. Python, Scikit-learn, Pandas, NumPy, Jupyter
Dashboard Python Build interactive dashboards and web apps to present data analysis and ML results in a user-friendly way. Python, Flask, Dash, Plotly, Jupyter

Technical Skills

  • Languages & Tools: Python, SQL, Git/GitHub, Jupyter, Pandas, NumPy, Scikit-learn, TensorFlow, Flask, R
  • Areas: Data Analysis, Machine Learning, AI, Big Data, Software Development
  • Soft Skills: Leadership, Effective Communication, Team Management, Analytical Thinking

Contact

Feel free to explore my projects and reach out for collaboration or feedback!

About

A collection of projects and notebooks covering the end-to-end data science workflow. The repository includes: Data Analysis: data cleaning, descriptive statistics, and exploratory data analysis; Machine Learning: predictive modeling, regression, and classification; Visualization & Dashboards: interactive insights and reporting with Python.

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