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lucalullo/README.md

Luca Lullo — Data Scientist

Specializzato in data engineering, advanced data cleaning e machine learning applicato a dati pubblici e socio-economici.

Costruisco dataset, notebook e modelli predittivi con attenzione alla riproducibilità, alla qualità dei dati e all'interpretabilità dei risultati.

🛠 Stack

Python Pandas Scikit-learn XGBoost LightGBM CatBoost TensorFlow Keras PyTorch Plotly SQL

📂 Progetti selezionati

Progetto Tema Highlights
Home Credit Default Risk Credit risk ML XGBoost, SHAP, feature engineering
Italian Justice Workload Dati istituzionali Analisi civile/penale 2003–2024
Global Emissions & Temperature Clima CO₂, GHG, temperature 1950–2024
Used Car Prices Regressione LightGBM, feature engineering
Customer Churn Classificazione Random Forest, class balancing

📊 Kaggle

Datasets Expert — Top 100 globale · 15 dataset pubblici · usability score 10.0

Kaggle

📬 Contatti

LinkedIn

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  1. Customer-churn Customer-churn Public

    Customer churn prediction using Random Forest and class-weight balancing. Detailed EDA and feature engineering on telecom industry data.

    Jupyter Notebook

  2. House-prices House-prices Public

    Predicting house prices using Ridge Regression, Skewness transformation and Advanced Feature Engineering.

    Jupyter Notebook

  3. Used-car-prices Used-car-prices Public

    Machine learning project to predict used car prices with feature engineering and LightGBM.

    Jupyter Notebook

  4. Italian-justice-workload Italian-justice-workload Public

    Multidimensional analysis of the Italian justice system workload (2003–2024). A study of civil and criminal proceedings using judicial pressure and litigation indicators.

    Jupyter Notebook

  5. Home-credit-default-risk Home-credit-default-risk Public

    Machine learning project to predict credit default risk with feature engineering, XGBoost and SHAP interpretability.

    Jupyter Notebook

  6. Global-emissions-and-temperature-1950-2024 Global-emissions-and-temperature-1950-2024 Public

    Global climate analysis covering 75 years of CO₂, greenhouse gas emissions and mean surface temperatures across countries (1950–2024). Built with Pandas, Matplotlib, Seaborn and Plotly.

    Jupyter Notebook