The goal of this section is to better understand the beverage manufacturing dataset before predictive modeling begins. Exploratory Data Analysis (EDA), data cleaning, and data transformations were performed to identify patterns, missing values, outliers, and relationships between variables related to beverage pH. This process is important because understanding the data helps ensure that later predictive models are built using reliable and meaningful information.

Association Rules and Cluster Analysis — Groceries Dataset

Chapter 8 Homework Problems

Homework 8 Problems 7.2 and 7.5

Exercises 6.2 and 6.3 from the textbook

Forecasting Project - ATM Cash, Power Usage & Water Flow

Homework 6 - ARIMA models

Exponential Smoothing

Data Pre-Processing

Transformations

Time Series Graphics Homework assignment