4.1 Clustering-and-Decision-Trees-in-Business-Data-Analytics

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Clustering and Decision Trees in Business Data
Analytics
Explore unsupervised and supervised machine
learning techniques. We'll cover clustering and
decision trees. Understand the algorithms and
their real-world applications. See how these
tools drive business insights.
by Jitendra Tomar
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Understanding Clustering
Unsupervised Learning
Based on Characteristics
No Predefined Labels
Clustering is an unsupervised method.
The grouping depends on inherent characteristics.
Unlike supervised learning, no labels are needed.
It groups similar data points together.
This reveals hidden patterns.
It uncovers natural data structures.
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K-Means Clustering
Groups into K Clusters
Similarity-Based
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Divides data into K clusters.
Relies on distance metrics to measure similarity.
Each data point belongs to the nearest cluster.
Data points in the same cluster are similar.
Applications
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It can perform customer segmentation.
It can also perform anomaly detection.
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Hierarchical Clustering
Builds a Tree
Creates a tree-like structure of clusters.
Also called a dendrogram.
No Need to Specify K
Does not require specifying the number of
clusters in advance.
Allows exploration at different levels.
Suitable for Taxonomies
Well-suited for creating taxonomies.
It helps identify organizational structures.
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DBSCAN Clustering
Density-Based
Anomaly Detection
Groups data based on density.
Effective for anomaly detection.
Identifies clusters of varying shapes.
Identifies outliers as noise points.
Non-Spherical Clusters
Works well with non-spherical clusters.
Unlike K-Means, it handles complex shapes.
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Applications of Clustering
Customer Segmentation
Market Basket Analysis
Fraud Detection
Flags unusual transactions.
Targets marketing based on customer groups.
Discover buying patterns for promotions.
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Understanding Decision Trees
Supervised Learning
Classification and Regression
Logical Decisions
Decision Trees are a supervised method.
Used for both classification and regression tasks.
Helps make logical decisions.
Based on patterns in data.
They learn from labeled data.
Predicts categories or continuous values.
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Decision Tree Algorithms
Random Forest
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Reduces overfitting.
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CART
Uses Gini impurity for splitting.
XGBoost
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Optimized for speed.
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Decision Tree Applications
Credit Risk Assessment
Customer Churn Prediction
Supply Chain Optimization
Determines loan eligibility.
Identifies customers likely to leave.
Helps in demand forecasting.
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Key Takeaways and Next Steps
Clustering
Decision Trees
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Unsupervised learning for grouping data.
Supervised learning for logical decisions.
Applications
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Wide range of business uses.
Explore more algorithms and tools. Apply these
techniques to your data. Drive better business
insights.