These notebooks provide a comprehensive workflow, from start to finish, for processing and analyzing the SWAN-SF dataset. They include detailed steps for reading the dataset files, performing full preprocessing, and executing classification.

A Jupyter Notebook comparing two imputation methods for a dataset.

Simple notebook can imputation or forecasting time series using CNN

In this notebook, i show a examples to implement imputation methods for handling missing values.

This project, carried out in Jupyter Notebook, aims to explore the main Data Analysis techniques with Python tools. Pandas, Numpy, Seaborn, Matplotlib, Plotly and sklearn are used. Divided into three notebooks, I separate the data cleaning, data analysis and machine learning part. For more details and goals, see README

This notebook loads a time series of gas concentrations in the air to train a recurrent neural network to predict the next hour of data

Jupyter notebook using machine learning techniques to explore the complex drivers of modern slavery. Models from a research paper are replicated and evaluated . Actions also include filling missing data, training regression models, and analyzing feature importance.