In this project I tried to create a Time Series Model to learn the patterns of a series of Gross Revenue from the brazilian industry, from 2005 to 2020, in order to forecast this series along future time steps. Approaches: SARIMA Model: using Box & Jenkins Methodology, testing endogenous features as well. LSTM Model: using the same features as before, even first and second differentiation of the series.

This is the second part of an old project that I recently replicated in Python, it was presented as my Conclusion Work of my Post-Graduation at FIA School. This study is focused on my Facebook friends data and their friendship connections, collected through Facebook Netvizz App. This app treats json returns from Facebook Graph API, and formats in two files of nodes (friends) and edges (friendship relations). I used the Python package Networkx to detect communities based on nodes relationships and to compute centrality measures, in order to understand patterns on detected communities and identify aspects of leadership and popularity in my personal friends.