This interactive dashboard presents key performance metrics for a microfinance institution, focusing on monthly income trends, borrower growth, portfolio at risk (PAR) by branch, and member-to-borrower conversion rates. Using pre-processed data and visualized through ggplot2, plotly, and DT, the dashboard provides actionable insights to support financial sustainability and operational decision-making across branches.

Penjelasan mengenai Regresi

This document introduces the use of Autoregressive (AR) models for time series forecasting in Python. AR models provide a simple yet useful framework to predict future values based on past observations. We begin by estimating the AR(1) model. We then implement a forecast evaluation strategy by splitting the data into training and testing samples, which allows us to assess the model’s out-of-sample predictive accuracy.

Just testing

Elaboración propia a partir de datos de CONAPO e INEGI, como parte del curso de R con Noé Osorio