RHoMIS Analytics — DVA Capstone 2

Newton School of Technology | Data Visualization & Analytics A 2-week industry simulation capstone using Python, GitHub, and Tableau to convert raw survey data into actionable agricultural intelligence.

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Project Overview

Field	Details
Project Title	RHoMIS Analysis
Sector	Agriculture / Rural Development
Team ID	G-7
Section	C
Faculty Mentor	TBA
Institute	Newton School of Technology
Submission Date	TBA

Team Members

Name	GitHub Username
Mahir Abdullah	mahir-m01
Manas Vivek Saxena	ManasSaxena14
Manya Verma	manyaverma727
Rajat Srivastav	rajatrsrivastav
Rajdeep Sanyal	rajdeep-2004
Sankalp	Sankalp13353

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Business Problem

Smallholder farming households across 35 countries face chronic food insecurity and low farm income. Which regions are most at risk, and what combination of farm size, crop diversity, land productivity, household demographics, and other factors drive these outcomes?

Core Business Question

Which regions and farm profiles are most vulnerable to food insecurity and low income — and what factors drive these outcomes?

Decision Supported

Farmers, policymakers, and development organisations can identify which regions and farm profiles are most vulnerable and decide whether to act on income support, crop diversification, land productivity improvements, or targeted food assistance programmes.

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Dataset

Full title: The Rural Household Multi-Indicator Survey (RHoMIS) data of 54,873 farm households in 35 countries

Attribute	Details
Source Name	RHoMIS (Rural Household Multi-Indicator Survey)
Published By	Harvard Dataverse — Gorman, Hammond, Frelat, Caulfield et al. (76 contributors)
Direct Access Link	DOI: 10.7910/DVN/WS38SA
Version	Version 1 — January 30, 2025
License	CC0 1.0 (Public Domain)
Row Count	54,873 farm households
Column Count	1,599 raw survey variables
Countries Covered	35 across Latin America, Africa, and Asia
Time Period Covered	2015 – 2023
Format	CSV

Key Columns Used

TBA — to be populated after column selection in 01_extraction.

For full column definitions, see docs/data_dictionary.md.

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KPI Framework

TBA

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Tableau Dashboard

TBA — see tableau/dashboard_links.md once published.

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Key Insights

TBA — to be completed after analysis.

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Recommendations

TBA — to be completed after analysis.

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Repository Structure

Section-C_G-7_RHoMIS-Analytics/
|
|-- README.md
|
|-- data/
|   |-- raw/                         # Original dataset (never edited — gitignored)
|   `-- processed/                   # Cleaned output from ETL pipeline
|
|-- notebooks/
|   |-- 01_extraction.ipynb
|   |-- 02_cleaning.ipynb
|   |-- 03_eda.ipynb
|   |-- 04_statistical_analysis.ipynb
|   `-- 05_final_load_prep.ipynb
|
|-- scripts/
|   `-- etl_pipeline.py
|
|-- tableau/
|   |-- screenshots/
|   `-- dashboard_links.md
|
|-- reports/
|   |-- project_report.pdf
|   `-- presentation.pdf
|
`-- docs/
    `-- data_dictionary.md

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Analytical Pipeline

1. Extract — Raw dataset loaded and validated; data dictionary drafted (01_extraction)
2. Clean & Transform — Column selection, missing value handling, standardisation, feature engineering (02_cleaning)
3. EDA — Missing value analysis, distribution plots, cross-variable exploration (03_eda)
4. Statistical Analysis — Correlation, segmentation, and gap analysis (04_statistical_analysis)
5. Final Load Prep — Cleaned, analysis-ready CSV exported for Tableau (05_final_load_prep)
6. Visualise — Interactive Tableau dashboard published on Tableau Public
7. Report — Final report and presentation deck exported to reports/

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Tech Stack

Tool	Purpose
Python + Jupyter Notebooks	ETL, cleaning, EDA, statistical analysis
Google Colab	Cloud notebook execution (supported)
Tableau Public	Dashboard design and publishing
GitHub	Version control and contribution audit

Python libraries: pandas, numpy, matplotlib, seaborn, scipy, missingno

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Submission Checklist

GitHub Repository

• All notebooks committed in .ipynb format with outputs visible
• data/raw/ contains the original, unedited dataset
• data/processed/ contains the cleaned pipeline output
• tableau/screenshots/ contains dashboard screenshots
• tableau/dashboard_links.md contains the Tableau Public URL
• docs/data_dictionary.md is complete
• All members have visible commits

Tableau

• Published on Tableau Public with public URL
• At least one interactive filter included
• Dashboard directly addresses the business problem

Report

• Final report exported as PDF into reports/
• Final presentation exported as PDF into reports/

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Contribution Matrix

Team Member	Dataset & Sourcing	ETL & Cleaning	EDA & Analysis	Statistical Analysis	Tableau Dashboard	Report Writing	PPT & Viva
Mahir Abdullah
Manas Vivek Saxena
Manya Verma
Rajat Srivastav
Rajdeep Sanyal
Sankalp

Declaration: We confirm that the above contribution details are accurate and verifiable through GitHub Insights, PR history, and submitted artifacts.

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Academic Integrity

All analysis, code, and recommendations in this repository are the original work of the team listed above. Contributions are tracked via GitHub Insights and pull request history.

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Newton School of Technology — Data Visualization & Analytics | Capstone 2