Francesco Sannicola
Machine Learning | Software Engineering
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Document-Claim Evidence Mapper
A document-claim analysis tool that uses LLMs to extract and rank relevant text supporting or refuting claims. Combines prompt-based NLP with customizable evaluation for fact-checking and analysis in legal and financial contexts.
ML-based classification of customer complaint drivers
A data-driven approach to understand and predict customer dissatisfaction through data processing, exploratory analysis, predictive modeling, rigorous evaluation, and actionable visualization.
LLM-Powered Email Generation and Multi-Class Classification
Building and evaluating a machine learning pipeline for email classification utilizing synthetic data generated by a GPT-based email generator with Few-Shot Learning.
Blind Binary Classification: ML Model Development & Evaluation
Development, assessment, and evaluation of machine learning models for a binary classification task. However, the dataset, although divided into two parts, completely lacks any field descriptions.
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Top Articles
Understanding Random Number Generation
Exploring Random Number Generators (RNG), Linear Congruential Generators (LCG), and Permuted Congruential Generator (PCG) focusing on their mechanisms.
GenAI and LLM Questions with In-Depth Answers
From prompt engineering to advanced concepts, this guide demystifies large language models (LLMs) while preparing you for technical interviews with detailed, practical examples.
See All Articles
Document-Claim Evidence Mapper
A document-claim analysis tool that uses LLMs to extract and
rank relevant text supporting or refuting claims. Combines
prompt-based NLP with customizable evaluation for fact-checking
and analysis in legal and financial contexts. 
ML-based classification of customer complaint drivers
A data-driven approach to understand and predict customer
dissatisfaction through data processing, exploratory analysis,
predictive modeling, rigorous evaluation, and actionable
visualization.
LLM-Powered Email Generation and Multi-Class
Classification
Building and evaluating a machine learning pipeline for email
classification utilizing synthetic data generated by a GPT-based
email generator with Few-Shot Learning.
Blind Binary Classification: ML Model Development &
Evaluation
Development, assessment, and evaluation of machine learning
models for a binary classification task. However, the dataset,
although divided into two parts, completely lacks any field
descriptions.
Understanding Random Number Generation
Exploring Random Number Generators (RNG), Linear Congruential
Generators (LCG), and Permuted Congruential Generator (PCG)
focusing on their mechanisms.
GenAI and LLM Questions with In-Depth Answers
From prompt engineering to advanced concepts, this guide
demystifies large language models (LLMs) while preparing you for
technical interviews with detailed, practical examples.