GitHub - CortexLLMs/morocco-economic-pipeline: Morocco Economic Analysis Pipeline - R/Python ETL, ML/DL models, Kaggle published

Tech Stack

Kaggle Resources

Resource	Link
Dataset (24 CSVs + charts)	amarzouyoussef/economie-maroc-rasd
1GB Bulk Collection (OSM+text+DEM+GADM)	amarzouyoussef/morocco-1gb-real-data
Charts & Insights V2	amarzouyoussef/morocco-charts-v2
Analysis V2 (CSV + HCP)	amarzouyoussef/morocco-economic-analysis-v2
R Kernel (notebook)	amarzouyoussef/maroc-pipeline-r
Forecasting Model	amarzouyoussef/morocco-economic-forecasting

Model Variations

Variation	Framework	Kaggle URL
Random Forest	scikit-learn	ScikitLearn/random-forest
Lasso	scikit-learn	ScikitLearn/lasso
ARIMA	statsmodels	Other/arima
Deep Learning	Keras	Keras/deep-learning

Architecture

Raw Data (WB, IMF, OWID, Casablanca SE)
        |
   [Python ETL]  fetch_wb.py / fetch_owid.py / fetch_imf2.py
        |         clean.py / transform.py / merge.py / load.py
        v
   23 clean CSVs  -->  Kaggle Dataset (economie-maroc-rasd)
   (includes education_real.csv - real World Bank education data)
        |
   [R Kernel]  maroc_pipeline.R  (Kaggle R notebook)
        |         12 sections: Ingest → Clean → Join → EDA → Stats
        |         → Scenarios → ML → DL → Benchmark → Validation
        |         → Export + HTML Report
        v
   HTML Report  rapport_economie_maroc.html
        |
   [Kaggle Models]  4 variations published for inference

ETL Pipeline (Python)

Script	Purpose
`fetch_wb.py`	World Bank WDI indicators (GDP, inflation, debt, trade, etc.)
`fetch_owid.py`	Our World in Data (energy, demographics, health)
`fetch_imf2.py`	IMF WEO forecasts and historical data
`clean.py`	Standardize column names, handle missing values, deduplicate
`transform.py`	Pivot, aggregate, create derived indicators
`merge.py`	Join all sources into a unified master dataset
`load.py`	Export final CSVs for Kaggle upload
`spark_etl.py`	Optional Spark-based distributed ETL for large volumes
`config.py`	Shared configuration (paths, constants)
`run_all.py`	Orchestrator: runs the full ETL in sequence
`get_log.py`	Helper to retrieve Kaggle kernel execution logs
`publish_outputs.py`	Publish kernel outputs to Kaggle dataset

ZenML Scripts (`zenml/`)

Script	Purpose
`run_k8s.py`	Basic K8s pipeline (fetch + train)
`run_enhanced.py`	Enhanced pipeline (HCP+WB+IMF, 130 features)
`fetch_real_data.py`	Download 63 HCP XLSX + 25 World Bank indicators
`fetch_education.py`	Fetch real education data from World Bank API
`fetch_wikidata.py`	Fetch Morocco (Q1028) time-series from Wikidata API
`fetch_all_hcp.py`	Download all HCP datasets from data.gov.ma
`generate_charts.py`	Generate 10 economic charts (enhanced data)
`charts_real.py`	Generate 7 charts with real data
`generate_economic_data.py`	Create enhanced indicator CSVs
`optimize_r2.py`	R2 optimization (basic models)
`optimize_real.py`	R2 optimization with real HCP data
`optimize_honest.py`	Honest optimization (no GDP components)
`optimize_enhanced.py`	Enhanced optimization (all features)
`optimize_comprehensive.py`	Comprehensive model comparison
`optimize_quarterly.py`	Quarterly data optimization
`extract_quarterly.py`	Extract quarterly indicators from HCP
`build_quarterly.py`	Build quarterly dataset
`insights.py`	Economic insights analysis
`patch_validate_path.py`	Base64 monkey-patch for Kaggle SDK
`sitecustomize.py`	Auto-patch on Python startup
`upload_readme.py`	Upload README to Kaggle dataset

R Analysis Pipeline (`maroc_pipeline.R`)

The R notebook runs 12 sections on Kaggle (v22 — real education data integrated):

1. Ingestion

Reads 4 source CSVs: indicators_clean.csv, dim_indicators.csv, bank_prices.csv, benchmark_morocco.csv.

2. Cleaning

Filters to 1960+, removes empty/constant columns.
Interpolates missing values with zoo::na.approx.
Winsorizes outliers at 5th/95th percentiles.
Charts filter early years (before 1980) to avoid fake interpolated lines.

3. Join (Economy x Finance)

Merges macroeconomic indicators with bank stock prices and benchmark data into a master dataset.

4. Exploratory Data Analysis (EDA)

Macroeconomic Trends

Tendances macroeconomiques du Maroc (1980-2024). This chart shows the evolution of key economic indicators over 40+ years. GDP (NGDPD) grows exponentially from ~$20B to ~$140B. Inflation (FP.CPI.TOTL.ZG) stabilizes below 5% after the 1990s. Unemployment (SL.UEM.TOTL.ZS) fluctuates between 8-14%. External debt (DT.DOD.DECT.CD) rises sharply post-2010, reflecting infrastructure investment. Years before 1980 excluded (interpolated data).

Sectoral Composition

Composition sectorielle du PIB (donnees reelles WB). L'agriculture chute de 23% (1965) a 11% (2023), reflet de la modernisation economye. L'industrie reste stable autour de 25-28%, sans industrialisation massive. Les services progressent de 47% a 54%, moteur principal de la croissance. Cette transition est typique des pays a revenu intermediaire.

Evolution sectorielle (lignes)

Evolution detaillee par secteur (1980-2023). On observe clairement le declin agricole (15% -> 11%) et la montee des services (47% -> 54%). L'industrie stagne a 25-28%, revelant l'absence d'industrialisation profonde au Maroc.

Tableau de composition sectorielle (tous les 10 ans)

Annee	Agriculture (%)	Industrie (%)	Services (%)	Observation
1965	23.4	27.5	49.1	Economye largement agricole, 60% de la population active dans l'agriculture
1970	20.5	28.8	50.7	Debut de la diversification, premiers investissements industriels
1980	15.1	28.8	46.7	Choc petrolier, l'industrie stagne, les services prennent le relais
1990	15.1	27.6	45.0	Liberalisation economique, emergence du tertiaire (banques, tourisme)
2000	10.7	24.4	45.7	Mise en zone de libre-echange, decline agricole accelere
2010	12.0	23.7	47.2	Crise mondiale, l'agriculture reste volatile (secheresse 2007, 2016)
2020	10.7	26.0	53.2	COVID-19, les services (digital, sante) progressent
2023	11.1	25.3	53.7	Economie de services, l'industrie stagne a 25% sans industrialisation profonde

Analyse :

Agriculture : Declin de 23% a 11% sur 60 ans. Reste vulnerable aux secheresses (contribution variable au PIB). 30% de la population active mais seulement 11% du PIB = productivite faible.
Industrie : Stagnation a 25-28%. Pas de "miracle industriel" comme en Asie. Les zones franches (Tanger Med, Casablanca Finance City) n'ont pas suffi a transformer l'economie.
Services : Moteur principal (54% du PIB). Tourisme, banques, telecoms, transport. Transition typique des pays a revenu intermediaire.

Correlation Matrix

Matrice de correlations. Strong positive correlations exist between GDP and trade volume (NE.EXP.GNFS.ZS ~0.85). Inflation shows moderate negative correlation with GDP growth (-0.35). Debt-to-GDP correlates positively with infrastructure spending indicators. This informs feature selection for ML models.

Gini Coefficient Trends

Evolution de l'inegalite (Gini). Morocco's Gini coefficient (SI.POV.GINI) fluctuates between 0.39-0.46 over the period. Peaks in 2000 and 2014 coincide with drought years affecting rural incomes. The downward trend post-2018 suggests modest improvement in income distribution, though inequality remains moderate-to-high.

5. Statistical Models

ARIMA Forecast

Projection ARIMA du PIB. ARIMA(2,1,1) model forecasts GDP through 2030. The confidence interval widens with horizon, reflecting increasing uncertainty. Base case projects ~4.0% annual growth, reaching ~$180B by 2030. The model captures the cyclical pattern of Moroccan GDP driven by agricultural output and global trade.

6. Growth Scenarios

Scenarios de croissance 2025-2039. Monte Carlo simulation with 1000 paths under 3 scenarios:

Optimistic (green): 5.5% growth, GDP reaches $250B by 2039

Base (blue): 4.0% growth, GDP reaches $200B by 2039

Pessimistic (red): 2.5% growth, GDP reaches $150B by 2039

The fan chart shows 90% confidence bands. Morocco's GDP is highly sensitive to rainfall (agriculture = 12% GDP) and global commodity prices.

7. Machine Learning

K-Means Clustering

Regimes economiques (K-means, k=3). Three distinct economic regimes identified:

Cluster 0 (red): High growth periods (2000-2008, 2021-2024) — GDP growth > 4%, low inflation

Cluster 1 (green): Moderate growth (2009-2015) — GDP growth 2-4%, stable conditions

Cluster 2 (blue): Crisis periods (1999, 2016, 2020) — GDP growth < 2%, high volatility

These clusters inform regime-switching models for better forecasting.

PCA Variance Explained

Analyse en Composantes Principales. PCA reduces 40+ indicators to ~8 components explaining 90% of variance. PC1 captures overall economic development (GDP, trade, investment). PC2 captures social indicators (education, health). This dimensionality reduction improves ML model efficiency.

8. Deep Learning / Machine Learning (Modeles de prediction)

Qu'est-ce que le Deep Learning dans ce pipeline ?

Le modele ML predit la croissance du PIB reel (%) du Maroc a partir de 15 indicateurs macroeconomiques. La cible est la croissance (stationnaire) et non le PIB absolu (non-stationnaire), ce qui est plus realiste pour un modele statistique.

Note importante : Avec seulement 54 annnees de donnees, les modeles ML/DL ont des performances limitees. Le PIB reel du Maroc est principalement determine par des facteurs structurels (demographie, investissements, politique monetaire) difficilement capturables par des indicateurs annuels. Ces modeles sont indicatifs mais pas des substitutes a des modeles economiques structurels.

Architecture du modele

Parametre	Ridge	Lasso
Alpha	10	0.1
Features	4/4 gardees	4/4 gardees
Regularisation	L2 (penalise gros coefficients)	L1 (peut supprimer des features)
Train R2	0.022	0.016
Test R2	-0.249	-0.319
Gap	0.27	0.34
RMSE	4.05%	4.16%
Surapprentissage	NON (gap < 0.15)	FAIBLE (gap < 0.35)

Pourquoi Ridge + Lasso ?

Ridge : regularisation L2, garde toutes les features, penalise les gros coefficients
Lasso : regularisation L1, peut supprimer des features (selection automatique)
Les deux sont anti-overfit grace a la regularisation
54 annnees = trop peu pour des modeles complexes (RF, DL, GBR)

Regularisation (Ridge vs Lasso)

Selection de regularisation. Le R2 train diminue avec alpha. Ridge garde les 4 features, Lasso peut les supprimer si alpha est trop grand. Alpha optimal: Ridge=10, Lasso=0.1.

Prediction vs Realite

Metrique	Ridge	Lasso
Train R2	0.022	0.016
Test R2	-0.249	-0.319
Gap	0.27	0.34
RMSE	4.05%	4.16%
Features gardees	4/4	4/4

Interpretation honnete :

Les deux modeles sont intentionnellement simples pour eviter le surapprentissage
Train R2 proche de 0 = modeles conservateurs (anti-overfit)
Test R2 negatif = la croissance du PIB est imprevisible avec ces 4 indicateurs
Ridge est legerement meilleur (gap plus faible)
C'est la realite : la croissance du PIB est determinee par des facteurs structurels pas capturables par des indicateurs annuels

Analyse des residus

Analyse des residus. Les residus montrent que le modele sous-estime les fortes croissances et surestime les faibles. La distribution n'est pas parfaitement normale, indiquant des periodes non capturees.

Importance des variables

Variable	Ridge (alpha=10)	Lasso (alpha=0.1)
Inflation	0.37	0.31
Chomage	-0.46	-0.33
Commerce/PIB	-0.24	-0.01
Dette/PIB	-0.07	-0.01

Interpretation :

Ridge : garde les 4 coefficients, chomage = plus important
Lasso : supprime commerce et dette (proches de 0), garde inflation + chomage
Lasso fait de la selection automatique de features

Croissance predite dans le temps

Croissance predite vs reelle. Le modele Ridge est intentionnellement conservateur : il predit proche de la moyenne historique (3-4%). Il ne capte pas les crises (COVID 2020) ni les rebonds forts. C'est le comportement attendu d'un modele regularise.

Resume du modele ML :

Anti-overfit : Ridge gap=0.27, Lasso gap=0.34 (pas de surapprentissage)
Performance : RMSE ~4% (erreur de 4 points de croissance)
Limite : la croissance du PIB est imprevisible avec 54 annnees de donnees
Usage : indicateur qualitatif, pas de forecast fiable
Amelioration : donnees trimestrielles, modeles structurels (VAR, SVAR), plus de features

9. Benchmark

Benchmark: Maroc vs Region/Monde. Morocco outperforms Sub-Saharan Africa on GDP per capita ($3,500 vs $1,600) but lags behind MENA average ($6,500). Morocco ranks 2nd in North Africa for FDI inflows. Healthcare spending (3.5% GDP) is below WHO recommended 5%. Education spending (5.8% GDP) is regional leader.

10. Domaines Specifiques

Social - Pauvrete et Inegalite

Pauvrete et Gini. Le taux de pauvrete a baisse de 15% (2000) a 4% (2020). L'indice de Gini fluctue entre 0.39-0.46, avec des pics durant les annees de secheresse touchant le milieu rural.

Social - Chomage par genre

Chomage par genre. Le chomage masculin reste inferieur au feminin (8% vs 14%). L'ecart se reduit progressivement mais reste significatif, refletant les defis d'insertion professionnelle feminine.

Social - Dynamique demographique

Dynamique demographique. La population passe de 12M (1960) a 37M (2024). Le taux de fecondite chute de 7 a 2.2 enfants/femme. L'urbanisation atteint 65%, drivant la demande de logements et services.

Social - Pauvrete : Reel vs Predit

Pauvrete au Maroc. Tendance descendante continue de 15% (1990) a 4% (2020). Les variables trendees (pauvrete) ne sont pas predictibles par ML avec un split temporel — le modele ne peut pas generaliser sur des valeurs systematiquement differentes.

Education - Taux d'inscription (donnees reelles World Bank)

Inscriptions scolaires (donnees reelles). L'inscription primaire depasse 116% (taux brut, incluant les eleves ages), secondaire 88%, tertiaire 48%. La massification educationnelle progresse fortement depuis 2000.

Education - Depenses et alphabetisation (donnees reelles)

Depenses et alphabetisation (reel). Les depenses d'education oscillent entre 4.5-6.3% du PIB. Le taux d'alphabetisation passe de 30% (1982) a 75% (2024). Progres significatif mais des lacunes persistent.

Sante - Mortalite et esperance de vie

Sante: mortalite et esperance de vie. L'esperance de vie passe de 52 ans (1960) a 77 ans (2024). La mortalite infantile chute de 150 a 18/1000. Les depenses de sante restent faibles (3.5% PIB).

Sante - Reel vs Predit

Esperance de vie. Progression constante de 55 ans (1971) a 77 ans (2023). L'amelioration des conditions sanitaires est un processus structurel lent, pas predictable par des indicateurs macroeconomiques annuels.

Bourse - Prix et volatilite

Bourse de Casablanca. Le prix moyen des actions bancaires montre une tendance haussiere post-2015. La volatilite est elevee durant les crises (2008, 2020) mais se stabilise en periode normale.

Bourse - Regimes

Regimes boursiers. Classification en 3 regimes: haussier (vert), stable (bleu), baissier (rouge). Les periodes de baissent correspondent aux crises economiques mondiales.

Bourse - Reel vs Predit

Prix boursier predit. Le modele capture les tendances principales mais les pics de volatilite restent difficiles a predire, typique des marches financiers.

Inflation et stabilite des prix

Inflation au Maroc. L'inflation CPI se stabilise autour de 2-3% apres les annees 1990. La volatilite de l'inflation (ecart-type glissant) montre une convergence vers la stabilite monetaire.

Inflation : Reel vs Predit

Inflation predite. Le modele Random Forest predit correctement les phases d'inflation, utile pour la politique monetaire et les decisions d'investissement.

Data Quality & Interpolation

Problem: Fake Straight Lines

The raw dataset (indicators_clean.csv) contains sparse data for early years (1960-1980). When missing values are filled with linear interpolation (zoo::na.approx), this creates fake straight horizontal lines in charts.

1960 ─────────────────── 1980 ───── 1990 ───── 2000
      [interpolated]      [real data starts here]
      straight lines      actual trends

Solution: Filter by Data Availability

Charts now exclude years where data is interpolated, showing only years with real observations:

Chart	Before	After	Reason
Trends (4 indicators)	1960-2024	1980-2024	GDP, unemployment data sparse before 1980
Sector Composition	1960-2024	1980-2024	Sector shares incomplete before 1980
Gini Coefficient	1960-2024	1990-2024	Gini data only available from 1990
Education	1960-2024	1971-2024	World Bank education data starts 1971

Data Availability by Indicator

Indicator	Code	1960s	1970s	1980s	1990s	2000s	2010s	2020s
GDP (nominal)	NGDPD	⚠️	⚠️	✅	✅	✅	✅	✅
GDP Growth	NGDP_RPCH	⚠️	⚠️	✅	✅	✅	✅	✅
Inflation	PCPIPCH	⚠️	✅	✅	✅	✅	✅	✅
Unemployment	LUR	⚠️	⚠️	✅	✅	✅	✅	✅
Debt/GDP	GGXWDG_NGDP	⚠️	⚠️	⚠️	✅	✅	✅	✅
Trade Balance	BCA_NGDPD	⚠️	⚠️	✅	✅	✅	✅	✅
Gini	SI.POV.GINI	❌	❌	❌	✅	✅	✅	✅
Primary Enrollment	SE.PRM.ENRR	❌	✅	✅	✅	✅	✅	✅
Literacy Rate	SE.ADT.LITR.ZS	❌	❌	✅	✅	✅	✅	✅

✅ = real data | ⚠️ = partial/interpolated | ❌ = no data

Interpolation Method

# Original: linear interpolation (creates fake straight lines)
macro[[c]] <- zoo::na.approx(v, na.rm = FALSE)

# Problem: if values [10, NA, NA, NA, 50], result is [10, 20, 30, 40, 50]
# This creates a perfect straight line that never existed in reality

# Solution: filter charts to years with real data
master %>% filter(year >= 1980)  # exclude interpolated years

Why This Matters

Issue	Impact	Solution
Interpolation creates fake trends	Charts misleading	Filter to real data years
1960-1980 data mostly missing	Straight lines in plots	Start charts from 1980
Gini only from 1990	Empty early years	Start Gini chart from 1990
Education from 1971	World Bank coverage	Use real education CSV

Data Sources

Source	Coverage	Indicators
World Bank (WDI)	1960-2024	GDP, inflation, debt, trade, population, education
World Bank (Education)	1971-2024	Primary/secondary/tertiary enrollment, spending, literacy
Wikidata (Q1028)	1960-2024	Population, HDI, life expectancy, GDP, unemployment
IMF (WEO)	1980-2029	GDP forecasts, fiscal balance, current account
Our World in Data	1960-2023	Energy, CO2, health, demographics
UNDP	2000-2021	HDI, inequality, poverty
Casablanca Stock Exchange	2010-2024	Bank stock prices (Attijariwafa, BMCE, CIH)

Bulk Collection — 1GB of Real Morocco Data

Domain	Files	Size	Source
Geography (roads, buildings, POIs)	`osm/morocco-latest.osm.pbf`	243 MB	Geofabrik OSM
Geography (GIS-ready)	`osm/morocco-latest-free.shp.zip`	468 MB	Geofabrik OSM
Elevation 30m (Casa, Marrakech, Tanger, Agadir)	`geo/dem/*.tif` (4 tiles)	130 MB	Copernicus DEM
Admin boundaries	`geo/gadm41_MAR.*`	11 MB	GADM v4.1
Press text (Darija/FR, 145k+65k+57k articles)	`text/press*`	264 MB	HF Darija org
YouTube subtitles (Darija)	`text/yt_subs`	6 MB	HF bourbouh
ASR + Wikipedia Darija	`text/dakh`, `text/wiki_darija`, `text/asr_snousnou`	4 MB	HF community
Climate daily (15 cities, partial)	`climate_morocco_cities.csv`	1 MB	Open-Meteo
Trade detail HS chapters (partial)	`comtrade_morocco_ag2.csv`	growing	UN Comtrade
WDI full Morocco slice (1498 indicators)	`wdi_morocco_full.csv`	1 MB	World Bank
HCP official tables (63 XLSX)	`hcp_real/`	2 MB	data.gov.ma

Total: ~1.1 GB — all real, no synthetic data. Fetcher scripts in zenml/ (fetch_geo.py, fetch_wdi_bulk.py, fetch_climate.py, fetch_comtrade.py, fetch_wikidata.py, fetch_education.py).

Key Results

Metric	Ridge (alpha=10)	Lasso (alpha=0.1)	ARIMA
Train R2	0.022	0.016	~0.85
Test R2	-0.249	-0.319	~0.10
Gap (overfit)	0.27 (faible)	0.34 (acceptable)	~0.75
RMSE	4.05%	4.16%	~3.5%
Features	4/4	4/4 (L1 selection)	Univarie
Anti-overfit	Oui (L2)	Oui (L1)	Non

Conclusion : Ridge est le meilleur modele anti-overfit (gap=0.27). Lasso fait de la selection de features mais est legerement moins performant. ARIMA est meilleur en univarie mais ne capture pas les interactions. Ces modeles sont indicatifs — les vrais modeles economiques (HCP, BMCE) utilisent des donnees trimestrielles et des modeles structurels (VAR, DSGE).

v22 Update: Le notebook R utilise maintenant les donnees education reelles du World Bank (education_real.csv). Les graphiques montrent de vraies courbes d'inscription (primaire 117%, secondaire 88%, tertiaire 48% en 2023) au lieu de donnees interpolatees.

ZenML MLOps Pipeline (Kubernetes)

End-to-end ML pipeline running on Kubernetes with MLflow experiment tracking.

Architecture

World Bank API  -->  K8s Pod (fetch_and_prepare)  -->  K8s Pod (train_and_log)  -->  MLflow
                       |                                    |
                       Fetch 12 indicators               Ridge Regression
                       (1999-2026, 28 years)             alpha=100, StandardScaler

Stack

Component	Name	Config
Orchestrator	`k8s_orch`	Kind cluster `zenml-cluster`
Artifact Store	`shared_store_linux`	`/mnt/data` (hostPath volume)
Container Registry	`local_registry`	`localhost:5001`
Experiment Tracker	`mlflow_tracker`	`http://localhost:5000`

Results

Metric	Basic (WB)	Enhanced (HCP+WB+IMF)	Optimal (Honest)
R2	-0.1183	+0.1985	+0.3957
RMSE	4.1327	3.9659	3.4436
Samples	27	28	28
Features	8	40+	31
Best Model	Ridge(a=100)	Ridge(a=10)	SVR_linear
Sources	World Bank	HCP+WB+IMF	HCP+WB+IMF

Note: R2=0.91 was achieved using GDP components as features — this is data leakage (identity function). The honest R2 with external predictors is ~0.40.

Actual vs Predicted (GDP Real Growth %) - Honest (SVR_linear, R2=0.40)

Year	Actual	Predicted	Error
2020	-7.18%	-6.68%	+0.50
2021	8.15%	0.53%	-7.63
2022	1.81%	-1.89%	-3.71
2023	3.66%	2.71%	-0.95
2024	3.79%	1.41%	-2.38
2025	4.60%	5.08%	+0.49
2026	4.60%	2.58%	-2.02

Interpretation: With only external predictors (World Bank + IMF), R2=0.40 is the realistic ceiling. GDP growth is driven by rainfall, global trade shocks, and geopolitics — factors not captured in standard economic indicators.

Dashboard

ZenML: http://localhost:8080
MLflow: http://localhost:5000

Run Locally

cd zenml
pip install zenml
zenml init
zenml integration install kubernetes mlflow
python run_k8s.py

Available Pipelines

Pipeline	Script	Data Sources	Features	Best R2
Basic K8s	`run_k8s.py`	World Bank	8	-0.12
Enhanced	`run_enhanced.py`	HCP+WB+IMF	40+	+0.20
Optimal	`optimize_honest.py`	HCP+WB+IMF (no leakage)	31	+0.40

How to Run

On Kaggle (recommended)

Go to maroc-pipeline-r
Click Run All
Dataset auto-detected from /kaggle/input/economie-maroc-rasd/

Locally

# 1. Install dependencies
pip install kaggle pandas pyspark
R -e "install.packages(c('tidyverse','forecast','randomForest','caret','glmnet','corrplot','psych','ineq','DescTools','scales','rmarkdown'))"

# 2. Run ETL
python run_all.py

# 3. Run R analysis
Rscript kaggle_kernel/maroc_pipeline.R

Charts & Insights Gallery

Real data visualizations from Morocco economic analysis.

Charts Dataset on Kaggle

Enhanced Data Charts (HCP+WB+IMF)

GDP Growth	Inflation	Trade

Unemployment	Population	Fiscal

Actual vs Predicted	Correlation	Dashboard

Model Performance

Real Data Charts (World Bank Education)

Education Enrollment	Education Spending	GDP (real)

Unemployment (real)	Inflation (real)	Trade (real)

Dashboard (real)

License

Apache 2.0

Name		Name	Last commit message	Last commit date
.github		.github
data_ops		data_ops
economic_data		economic_data
kaggle_kernel		kaggle_kernel
kaggle_model		kaggle_model
zenml		zenml
.gitignore		.gitignore
README.md		README.md
chart_quarterly.py		chart_quarterly.py
clean.py		clean.py
config.py		config.py
extract.py		extract.py
fetch_imf2.py		fetch_imf2.py
fetch_owid.py		fetch_owid.py
fetch_quarterly.py		fetch_quarterly.py
fetch_wb.py		fetch_wb.py
get_log.py		get_log.py
load.py		load.py
merge.py		merge.py
publish_outputs.py		publish_outputs.py
run_all.py		run_all.py
spark_etl.py		spark_etl.py
transform.py		transform.py

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History

Folders and files

Repository files navigation

Tech Stack

Kaggle Resources

Model Variations

Architecture

ETL Pipeline (Python)

ZenML Scripts (zenml/)

R Analysis Pipeline (maroc_pipeline.R)

1. Ingestion

2. Cleaning

3. Join (Economy x Finance)

4. Exploratory Data Analysis (EDA)

Macroeconomic Trends

Sectoral Composition

Evolution sectorielle (lignes)

Tableau de composition sectorielle (tous les 10 ans)

Correlation Matrix

Gini Coefficient Trends

5. Statistical Models

ARIMA Forecast

6. Growth Scenarios

7. Machine Learning

K-Means Clustering

PCA Variance Explained

8. Deep Learning / Machine Learning (Modeles de prediction)

Qu'est-ce que le Deep Learning dans ce pipeline ?

Architecture du modele

Regularisation (Ridge vs Lasso)

Prediction vs Realite

Analyse des residus

Importance des variables

Croissance predite dans le temps

9. Benchmark

10. Domaines Specifiques

Social - Pauvrete et Inegalite

Social - Chomage par genre

Social - Dynamique demographique

Social - Pauvrete : Reel vs Predit

Education - Taux d'inscription (donnees reelles World Bank)

Education - Depenses et alphabetisation (donnees reelles)

Sante - Mortalite et esperance de vie

Sante - Reel vs Predit

Bourse - Prix et volatilite

Bourse - Regimes

Bourse - Reel vs Predit

Inflation et stabilite des prix

Inflation : Reel vs Predit

Data Quality & Interpolation

Problem: Fake Straight Lines

Solution: Filter by Data Availability

Data Availability by Indicator

Interpolation Method

Why This Matters

Data Sources

Bulk Collection — 1GB of Real Morocco Data

Key Results

ZenML MLOps Pipeline (Kubernetes)

Architecture

Stack

Results

Actual vs Predicted (GDP Real Growth %) - Honest (SVR_linear, R2=0.40)

Dashboard

Run Locally

Available Pipelines

How to Run

On Kaggle (recommended)

Locally

Charts & Insights Gallery

Charts Dataset on Kaggle

Enhanced Data Charts (HCP+WB+IMF)

Real Data Charts (World Bank Education)

License

About

Topics

Resources

Stars

Watchers

ZenML Scripts (`zenml/`)

R Analysis Pipeline (`maroc_pipeline.R`)