mmsulieman/WFP-Somali-Region-Process-monitoring-Thematic-Grouping

# WFP Somali Region – Process Monitoring Thematic Cleaner This is a small **Streamlit application** developed for the **World Food Programme (WFP) – Ethiopia, Somali Region RAM/M&E Unit**. It converts raw **MoDA/ODK Excel exports** (multi-sheet) into: - A **cleaned, annotated wide dataset** (one row per interview / observation), - A **long-format fact table** (one row per indicator value), - A **column-level thematic mapping** (Protection, CFM, Entitlement, etc.). You can use the outputs directly in **Tableau**, **Power BI**, or Python for FMPI and other analyses. --- ## 1. Features - Detects **Activity** (Relief, Nutrition, Refugees, Social Protection, Market) from sheet names. - Detects **Monitoring Type** (Beneficiary Interview, Distribution Observation, Food Basket, Warehouse, Partner, Market). - Automatically assigns a **Thematic Area** to each column, e.g.: - Protection, Safety & Accessibility - Accountability to Affected Populations (CFM & Information) - Entitlement Adequacy & Commodity Basket - Distribution Process & Procedures - Timeliness & Waiting Time - Targeting & Registration - Warehouse & Storage Monitoring - Partner Performance & Compliance - Market & Mills Monitoring - Demographics & Metadata - Creates: - `Wide_Cleaned` – original data + context columns + extra `_bin` columns (Yes/No → 1/0 where possible); - `Fact_Long` – long-format dataset (id columns + Raw_Column_Name + Value + thematic/meta); - `Indicator_Mapping` – one row per column with thematic classification and clean indicator names. --- ## 2. Installation 1. Make sure you have **Python 3.9+** installed. 2. Create and activate a virtual environment (recommended): ```bash python -m venv venv source venv/bin/activate # on Windows: venv\Scripts\activate ``` 3. Install the required packages: ```bash pip install -r requirements.txt ``` --- ## 3. Running the app From the folder containing `app.py`: ```bash streamlit run app.py ``` Streamlit will open the app in your browser …