Overview
A collection of analytical projects focused on understanding patterns, relationships, and trends within datasets. The projects apply statistical and analytical techniques to answer specific questions and turn historical data into meaningful insights.
Approach
The analysis begins with data cleaning, exploratory analysis, and identification of suitable analytical methods. Time series projects apply forecasting techniques such as Exponential Smoothing and ARIMA to model historical patterns and estimate future values.
Multidimensional Scaling (MDS) is used to represent similarities or dissimilarities between observations in a lower-dimensional space. Other projects combine descriptive statistics, correlation analysis, and exploratory techniques to investigate relationships among variables.
Outcome
The projects demonstrate practical experience in moving from raw data to interpretable analytical findings. The results can be used to understand historical behavior, identify relationships, explore multidimensional patterns, and support planning through forecasting.
