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Made with Python MIT License

Clustering Households into Rural and Urban Segments using Spending Patterns — Exploratory Data Analysis

This repository hosts an in-depth Exploratory Data Analysis (EDA) on the Ground Truth Household dataset, aimed at uncovering patterns in household expenditure, transaction behavior, and sector-wise trends.

The analyses are performed in the Jupyter Notebook: eda_ground_truth_households.ipynb

Overview

This EDA project follows a structured pipeline:

  1. Data Loading & Cleaning
  2. Descriptive Statistics & Distribution Analysis
  3. Sector-based Comparative Visuals
  4. Correlation Analysis
  5. Clustering & Pattern Discovery

Visualizations include histograms, boxplots, scatterplots, and heatmaps to illustrate key insights. Performing unsupervised learning using the KMeans clustering algorithm to classify households into two segments — likely Rural and likely Urban — based on spending and transaction behavior. he dataset contains household-level parameters:

Total expenditure
Average transaction value
Total transactions
Unique items purchased

Quickstart

Get up and running in a few minutes:

git clone https://github.com/11saishiva/eda-ground-truth-households.git
cd eda-ground-truth-households

Environment Setup

python -m venv venv
source venv/bin/activate        # macOS / Linux
venv\Scripts\activate           # Windows

pip install -r requirements.txt

Run the Notebook

jupyter notebook eda_ground_truth_households.ipynb

Dataset

Key variables:

  1. total_expenditure: Overall spending per household
  2. avg_transaction_value: Average amount per transaction
  3. total_transactions: Number of transactions per household
  4. common_id: Unique ID for each household

Insights & Findings

-> Households in different sectors exhibit varying spending distributions. -> Average transaction values correlate strongly with total expenditure. -> Clustering analysis reveals distinct behavior groups based on transaction patterns.

Model performance metrices

screenshot1

Mean values of variables with respected to predicted Area type (Rural or Urban)

screenshot2

Parameters used

-> spend_per_capita threshold range = total_expenditure/household_size

-> transactions_per_capita threshold range = total_transactions/household_size

Visualization

Scatterplot visualizing the households classified as Rural or Urban - Predictions

Scatterplot

Accuracy of spend_per_capita threshold and transactions_per_capita threshold represented as a heatmap

Heatmap

License

This project is shared under the MIT License. Feel free to use, modify, and distribute freely.