Choosing a Segmentation Algorithm

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Missing Values: Impute (fill in) missing data using statistical methods (mean, median) or remove rows/columns with excessive missingness.

Outliers: Identify and manage extreme values that could skew your analysis.

Data Types: Ensure columns are in the correct format (e.g., dates as datetime objects, numerical values as integers/floats).

Feature Engineering: This involves creating new, more informative variables from existing ones. For example:

Calculating days_since_last_purchase from transaction dates.

Once your data is clean and prepared, you'll select an country email list algorithm to group your customers. For segmentation, unsupervised learning (clustering) algorithms are commonly used as they identify inherent groupings without needing a pre-defined target variable.

K-Means Clustering: This is one of the most popular and straightforward clustering algorithms. It partitions data points into 'k' number of clusters, where each data point belongs to the cluster with the nearest mean (centroid).

How it works:

Initialize 'k' centroids randomly.

Assign each data point to the closest centroid.

Recalculate the centroids based on the mean of the assigned points.

Repeat steps 2 and 3 until the centroids no longer move significantly.


Deployment and Actionable Insights Segmentation is only valuable if it leads to action.