# Overview of Data-Driven Target Audience Segmentation
Data-driven segmentation is a technique used to categorize audiences or entities based on shared characteristics derived from data. By grouping data into segments, businesses can better target their marketing efforts, personalize services, and understand customer behavior. Two primary methods used for segmentation are rule-based segmentation and machine learning clustering.

## Rule-Based Segmentation:
This approach relies on predefined rules or thresholds to classify data points. Features are weighted based on their importance, and data is segmented into discrete bins or clusters. It is particularly useful when the business has specific criteria or thresholds that need to be applied to group the data.
 **Use Case:**  Marketers may use rule-based segmentation to categorize customers into "low," "medium," and "high" engagement based on their transaction volumes, engagement scores, or loyalty points.
## Machine Learning Clustering:
This technique employs unsupervised learning algorithms (e.g., KMeans) to group data points based on their similarities without requiring predefined rules. The algorithm identifies patterns within the data and creates clusters, making it a powerful method for discovering natural groupings.
 **Use Case:**  A business may use clustering to discover groups of customers with similar purchasing behaviors without having pre-existing knowledge of their traits, enabling the discovery of hidden patterns.
 
# Technical Explanation of Segmentation Methods
## 1. Rule-Based Segmentation Implementation Details
Rule-based methods are easy to interpret and configure, but they depend on prior knowledge of how features influence the segmentation. The weights control feature importance, while bins determine segment boundaries.
 **Function** : ``` rule_based```
The function takes a dataset and ranks the selected features on a percentile basis and applies weights to each feature. It computes an average score and divides the data into bins (Bin_1, Bin_2, etc.) based on specified thresholds. Binning is done using ```pd.qcut```, which ensures an equal number of data points per bin if possible.
- Key Considerations and Checks
 - Uniform Features: If a feature has no variability (i.e., all values are the same), it is excluded, and a warning is returned.
 - Error Handling: The function handles errors if there is insufficient data variability, suggesting reducing the number of bins or selecting more diverse features.

## Function for Applying Existing Rule-Based Segmentation
 **Function** : ```apply_existing_bounds```
This function uses pre-existing segmentation bounds and weights to update the segmentation of a dataset. It reads saved rules (weights and bounds) and applies them to a new dataset. It combines the original and updated data, ensuring consistent segmentation while allowing users to compare changes.
- Handling Updated Data: The function ensures that only data points that have changed are flagged, and an updated segment is assigned.
Overall  Rule-based methods are easy to interpret and configure, but they depend on prior knowledge of how features influence the segmentation. The weights control feature importance, while bins determine segment boundaries.

## 2. Machine Learning Clustering (KMeans) Implementation Details
[KMeans clustering](https://en.wikipedia.org/wiki/K-means_clustering) dynamically creates groups based on data characteristics, enabling users to find patterns without predefined rules. However, interpreting these clusters can be challenging, as the algorithm does not explicitly explain why data points are grouped together.

 **Function** : ```kmeans_clustering```
The core of the method is the [KMeans algorithm](https://scikit-learn.org/1.5/modules/generated/sklearn.cluster.KMeans.html), which partitions data into n clusters by minimizing the distance between points and the cluster centroids.
- Key Considerations and Checks
 - Uniform Features: Features with no variability are removed to avoid skewing the clustering process.
 - Preprocessing: The function creates pipelines for preprocessing numerical (scaling) and categorical (encoding) features.
 - Error Handling: It handles scenarios where the number of clusters exceeds data points, insufficient distinct clusters, or memory issues due to data size.

## Function for Applying Existing KMeans Model
 **Function** : ```apply_existing_model```
This function allows users to apply a pre-trained KMeans model to new data. It reads the model from a managed folder, uses it to predict new clusters, and maps the results to pre-defined segment names using a remapping dictionary.
- Preprocessing Consistency: The saved preprocessor ensures that data is transformed consistently, preserving the original segmentation logic.