Descriptive Data Mining

Unlock the power of Descriptive Data Mining using Microsoft Excel! In this video, we break down four essential data mining concepts and demonstrate how to perform them step-by-step using standard Excel tools and functions.

What you'll learn in this video:
1. Dimension Reduction: Learn how to identify redundant variables using Correlation analysis and simplify your dataset into meaningful "meta-variables". We'll use the Data Analysis Toolpak to spot high correlations (redundancy) and combine columns like "Satisfaction," "Trust," and "Likelihood to Recommend" into a single "Loyalty Score".

2. Cluster Analysis: Discover how to group customers into distinct segments based on similarities like Income and Spending Score. We'll visualize these clusters using Scatter Plots to identify high-value targets versus conservative spenders.

3. Association Rules (Market Basket Analysis): Ever wonder how stores know "If you buy Bread, you'll buy Milk"? We'll replicate the Apriori algorithm logic using TEXTJOIN and COUNTIFS to calculate Support and discover hidden relationships in transaction data.

4. Text Mining (Sentiment Analysis): Turn unstructured customer reviews into actionable data. We'll build a basic Sentiment Analysis tool using a lexicon-based approach (Positive/Negative word lists) and formulas like SUMPRODUCT or COUNTIF to score customer feedback automatically.