Project Title: Unsupervised Learning for Income Segmentation
Overview: The aim of this project is to identify the population segment with annual earnings greater than $50,000 using unsupervised learning techniques. We will analyze which clusters are over-represented in the general population and which clusters are over-represented in the high-earning population after feature extraction, clustering, and feature selection.
Approach:
Data Preprocessing: We will start by cleaning and preprocessing the data to remove any missing or irrelevant values and encode categorical variables.
Feature Extraction: We will extract relevant features from the data using techniques such as PCA or feature selection methods.
Clustering: We will apply clustering algorithms such as K-Meansannd Kmedians into clusters.
Evaluation: We will evaluate the performance of each clustering algorithm using metrics to determine the optimal number of clusters.
Analysis: We will analyze which clusters are over-represented in the general population and which clusters have a higher proportion of individuals with annual earnings greater than $50,000.
Visualization: We will visualize the clustering results using scatter plots, heat maps, or other visualization techniques to gain insights into the data.
Impact: By using unsupervised learning techniques to identify the population segment with high annual earnings, we can help businesses and policymakers understand the characteristics of this group and tailor their strategies accordingly. This project demonstrates our proficiency in data preprocessing, feature extraction, clustering, and visualization, as well as our ability to apply these techniques to real-world problems.