Feature/customer segmentation

Customer Segmentation/README.md

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+# Customer Segmentation Analysis
+## Overview
+This project focuses on customer segmentation using a dataset that includes various attributes such as income levels, product consumption, and family structure. The goal is to analyze customer spending patterns based on income levels and family size, providing insights that can inform targeted marketing strategies.
+
+## Dataset
+The dataset used in this analysis is marketing_campaign.csv available in this folder or you can access using this link:  
+https://www.kaggle.com/datasets/rodsaldanha/arketing-campaign
+
+## Objectives
+The main objectives of this analysis are:
+
+### - Data Filtering: 
+To segment the customer base based on demographics like income levels, age and the presence of kids and teenagers at home and product consumption behaviour and purchasing behaviour
+Product Consumption Analysis: To calculate and visualize the total consumption of different product categories based on these segments.
+### - Visualization: 
+To create pie charts or pair maps that represent the distribution of product consumption for customers varying income brackets, number of kids or teenagers at home, age based engagement metrics like web or store purchases etc. 
+The distribution is primarily made after clustering the data with respect to all the features using K-nearest neighbor Clustering. 
+
+
+## Methodology
+
+### Data Preprocessing:
+
+Filter the dataset to create subsets based on income levels.
+Calculate the total spending for various product categories.
+
+### K-Means Clustering
+K-means clustering is an unsupervised algorithm that partitions data into K distinct clusters based on features. It starts by randomly initializing K centroids, then assigns each data point to the nearest centroid. Afterward, centroids are recalculated as the mean of assigned points. This process repeats until centroids stabilize. In customer segmentation, K-means groups customers by behavior or demographics, informing targeted marketing strategies and insights. However, selecting the right K and initialization can influence results.
+
+### Data Visualization:
+Generate pair charts using Seaborn for visulaizing clustering. 
+Generate pie charts to visualize the percentage of product consumption for different income segments.
+
+## Technologies Used
+
+- Python
+- Data Analysis Libraries (e.g., Pandas, NumPy)
+- Data Visualization Libraries (e.g., Matplotlib, Seaborn)
+
+## Results
+Upon running, the clustered customer csv file is generated that filters the data into clusters through K-Nearest Neighbors algorithms. 
+
+## Conclusion
+The insights derived from this customer segmentation analysis can assist businesses in crafting targeted marketing strategies based on customer spending habits. By understanding which products are favored by specific income segments, companies can enhance their product offerings and promotional campaigns.
+
+## Contributing
+Contributions to this project are welcome! If you have suggestions, improvements, or bug fixes, please open an issue or submit a pull request.
+
+## Addressing Issue #4 Customer Segmentation