https://github.com/priyanshu501/customer_segmentation_for_targeted_marketing
Analysis of customer information to identify potential segments for targeted marketing campaigns using various clustering techniques.
https://github.com/priyanshu501/customer_segmentation_for_targeted_marketing
clustering customer-segmentation dbscan-clustering exploratory-data-analysis hierarchical-clustering jupyter-notebook k-means-clustering machine-learning python sklearn unsupervised-machine-learning
Last synced: 3 months ago
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Analysis of customer information to identify potential segments for targeted marketing campaigns using various clustering techniques.
- Host: GitHub
- URL: https://github.com/priyanshu501/customer_segmentation_for_targeted_marketing
- Owner: Priyanshu501
- License: mit
- Created: 2024-07-18T17:59:59.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-07-18T19:46:11.000Z (11 months ago)
- Last Synced: 2025-01-24T17:45:41.666Z (5 months ago)
- Topics: clustering, customer-segmentation, dbscan-clustering, exploratory-data-analysis, hierarchical-clustering, jupyter-notebook, k-means-clustering, machine-learning, python, sklearn, unsupervised-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 2.44 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Introduction
Customer Segmentation is a crucial aspect of modern marketing strategies, enabling businesses to tailor their products, services, and marketing efforts to specific groups of customers. By understanding the distinct characteristics and behaviors of different customer segments, companies can improve customer satisfaction, loyalty, and profitability.
In this project, we analyze a dataset of customer information to identify potential segments using various clustering techniques.
The EDA file is dedicated to Exploratory Data Analysis, which provides a detailed examination of the dataset's features, distributions, and relationships. The EDA helps in understanding the data better and preparing it for the clustering models.
## Objective
The primary objective of this project of this project is to perform customer segmentation to help the business modify its products and marketing strategies based on the target customers from different segments. By analyzing customer behavior and characteristics, we aim to identify potential customer segments that are most likely to respond to specific marketing efforts. This targeted approach can optimize marketing spend and improve the effectiveness of marketing campaigns.
## Models Implemented
### 1. K-Means Clustering
K-Means is a popular partition-based clustering algorithm that aims to partition data into K distinct clusters. It does by minimizing the sum of squared distances between data points and the centroids of their respective clusters.
In this project, we used K-Means to segment the customers based on their demographic and purchasing behavior features. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroids until convergence.
### 2. Hierarchical Clustering
Hierarchical clustering is a method of clustering that seeks to build a hierarch of clusters. Unlike K-Means, it does not require specifying the number of clusters in advance. Instead, it creates a tree-like structure (dendogram) that can be cut at different levels to obtain the desired number of clusters.
In this project, we used the Hierarchial Clustering approach, which starts with each data point as its own cluster and merges the closest pairs of clusters iteratively.
### 3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
DBSCAN is a density-based clustering algorithm that groups together points that are closely packed, while marking points that lie alone in low-density regions as outliers. Unlike K-Means and Hierarchical clustering, DBSCAN does not requrie the number of clusters to be specified beforehand. It is particularly effective at identifying clusters of arbitrary shape and handling noise in the data.
## Summary
By implementing and comparing these clustering algorithms, we aim to provide a comprehensive analysis of customer segments. Each algorithm offers unique advantages:
* K-Means is efficient and works well with large datasets.
* Hierarchical clustering provides a clear visualization of the data structure.
* DBSCAN is robust to noise and can find arbitrarily shaped clusters.
The results from these models can help the business tailor its marketing strategies to the specific needs and behaviors of different customer segments, ultimately leading to more effective marketing efforts and improved customer satisfaction.