1. Data Ingestion & Loading
DONE-Load CSV files- Changed from pandas to polars
DONE-Load Excel files (XLSX, XLS)- Changed from pandas to polars
DONE-Load JSON data- Changed from pandas to polars
DONE-Load XML data
DONE-Load SQL databases (Postgres, MySQL, SQLite)
DONE-Load API data (REST APIs)
DONE-Load GeoJSON data
DONE-Load Parquet files
DONE(only spss)-Load Stata/SPSS data
DONE-Load NetCDF data (climate, weather, food security)
2. Data Cleaning & Preparation
DONE-Handle missing values (drop, impute)
Done-Normalize column names
DONE-Merge multiple datasets
DONE-Handle duplicate rows
DONE-Standardize date formats
DONE-Standardize country names/codes (ISO3, ISO2)
DONE-Translate categorical variables
DONE-Handle inconsistent number formats
DONE-Handling outliers. remember that i added outlier isolation using IsolationForest of Sklearn.
DONE-Clean text fields (lowercase, punctuation removal)
DONE-Geocode location names to lat/long
DONE-Resolve administrative boundaries (ADM0–ADM3)
3. Data Transformation
DONE-Aggregate data by region
DONE-Normalize per population size
DONE-Calculate percentages (e.g., % of population in need)
DONE-Calculate ratios (e.g., children vs adults)
DONE-Pivot/unpivot data (wide ↔ long)
DONE-Rolling averages (moving mean)
DONE-Growth rates (month-over-month, year-over-year)
DONE-Calculate humanitarian severity index
DONE-Calculate humanitarian needs coverage
DONE-Weighted averages by population
DONE-Standardize age groups
DONE-Standardize gender categories
DONE-Convert categorical codes to human-readable labels
DONE-Calculate z-scores for anomalies
DONE-Calculate cumulative totals
4. Data Validation & Quality
DONE-Check for missing mandatory fields

DONE-Detect negative values where invalid

DONE-Flag impossible values (population <0)

DONE-Validate country codes against ISO list

DONE-Validate date ranges (not before 1900, not in future)

DONE-Detect inconsistencies (e.g., reached > targeted)

DONE-Validate humanitarian indicators against standards (Sphere, IPC)

DONE-Automatic data profiling report

DONE-Generate summary statistics per dataset

5. Geospatial Features

Plot data on maps

DONE-Choropleth maps by country/region - Tested

DONE-Heatmaps for crisis intensity

DONE-Overlay multiple indicators on maps

DONE-Display refugee camp locations

DONE-Visualize flood/drought-affected areas

DONE-Show conflict-affected zones (geospatial polygons)

DONE-Connect with OpenStreetMap data

DONE-Generate buffer zones (e.g., 50km radius)

DONE-Cluster humanitarian facilities (hospitals, schools)

6. Analysis Functions

DONE-Time-series trend analysis

DONE-Forecasting needs (ARIMA, Prophet)

DONE-Correlation analysis between indicators

DONE-Regression models (linear, logistic)

DONE-Cluster analysis (k-means for regions)

DONE-PCA/dimensionality reduction for indicators

DONE-Inequality measures (Gini, Theil index)

DONE-Impact analysis of interventions

DONE-Coverage gap analysis

DONE-Needs vs funding gap analysis

DONE-Seasonality detection

DONE-Anomaly detection (sudden spikes in needs)

DONE-Predicting displacement flows

DONE-Mortality/morbidity analysis

DONE-Food security phase classification

DONE-Shelter adequacy analysis

DONE-Health facility accessibility analysis

DONE-Education access analysis

DONE-Livelihood resilience analysis

DONE-Gender-disaggregated analysis

7. Humanitarian-Specific Metrics

DONE-% population in need

DONE-% targeted vs reached

DONE-Coverage ratio (reached/targeted)

DONE-% of children affected

DONE-% of women affected

DONE-% of IDPs/refugees affected

DONE-Food consumption score

DONE-Coping strategy index

DONE-Livelihood coping strategies

DONE-WASH access indicators

DONE-Health facility density per 10k

DONE-Education facility density per 10k

DONE-Malnutrition rates (GAM, SAM, MAM)

DONE-Crude mortality rate (CMR)

DONE-Under-five mortality rate (U5MR)

DONE-Conflict incident counts

DONE-People displaced per 1000

DONE-Humanitarian access constraints index

DONE-Funding received vs requested

DONE-Donor contribution tracking

8. Visualization

DONE-Bar charts

DONE-Line charts

DONE-Pie charts

DONE-Histograms

DONE-Box plots

DONE-Stacked bar charts

DONE-Multi-series line charts

DONE-Heatmaps (correlation, intensity)

DONE-Bubble charts

DONE-Sankey diagrams (flows of aid)

DONE-Treemaps (sector allocations)

DONE-Radar/spider charts (multi-sector needs)

DONE-Interactive dashboards

DONE-Time slider visualizations

DONE-Animated crisis progression maps

DONE-Compare multiple countries side by side

DONE-Plot funding vs needs

DONE-Plot targeted vs reached

DONE-Show gaps with color-coded visuals

DONE-Export plots as PNG, SVG, PDF

9. Interoperability

DONE-Export to CSV

DONE-Export to Excel

DONE-Export to JSON

DONE-Export to Parquet

DONE-Export to SQL database

DONE-Export to Stata/SPSS formats

DONE-Export to GIS (shapefile, GeoJSON)

DONE-Export to HDX-compatible datasets

DONE-Share dashboards as HTML

DONE-API integration for outputs

10. Automation & Workflows

DONE-Automate monthly report generation

DONE-Automate humanitarian snapshot dashboards

DONE-Automate dataset downloads from APIs

DONE-Scheduled ETL pipelines

DONE-Version control for datasets

DONE-Data lineage tracking

DONE-Change detection in datasets

DONE-Auto-refresh visualizations

DONE-Save reusable analysis templates

DONE-Batch processing of datasets

11. Machine Learning for Humanitarian Data

DONE-Predict displacement flows

DONE-Predict food insecurity levels

DONE-Predict mortality risks

DONE-Classify crisis severity

DONE-Detect misinformation trends

DONE-Predict funding shortfalls

DONE-Predict supply chain bottlenecks

DONE-Detect anomalies in survey responses

DONE-Crisis event classification from text

DONE-Early warning system modeling

12. Text & Document Processing

DONE-Process PDF reports (extract tables)

DONE-Process Word reports

DONE-Extract humanitarian indicators from text

DONE-Natural language search on datasets

DONE-Translate indicators (English ↔ Dari, Pashto, Arabic, French)

DONE-Named entity recognition (organizations, locations)

DONE-Sentiment analysis of field reports

DONE-Classify needs assessments by sector

DONE-Extract crisis-relevant keywords

DONE-Build humanitarian knowledge graph

14. Security & Ethics

DONE-Ensure GDPR compliance

DONE-Ensure humanitarian data protection principles

DONE-Anonymize sensitive individual data

DONE-Aggregate personally identifiable data

DONE-Redact GPS of vulnerable populations

DONE-Ethical AI usage guidelines

DONE-Bias detection in models

DONE-Transparency reports on datasets

DONE-Data sharing agreements compliance


