This project focuses on analyzing real estate data using R programming. It includes data exploration, filtering, grouping, visualization, and machine learning techniques such as K-Means clustering and KNN classification. The goal is to extract meaningful insights and build predictive models for better decision-making.

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Modelo de regresión logística para analizar y predecir la rotación Base analizada: dataset rotacion del paquete MODELOS

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This Data Dive explores the IPL Player Performance Dataset by building a time series and analysis of the series via plotting the series across different windows, checking for overall trends using linear regression, applying rolling averages and LOESS smoothing to reveal seasonal patterns, and using ACF/PACF to understand temporal dependence in the data.