During this 2 months internship, I worked on analyzing large datasets related to Moroccan markets. Tasks included data cleaning, processing, and statistical analysis to identify economic and commercial trends. This experience strengthened my skills in data analysis, statistics, and interpreting results to support decision-making.
Intelligent exploitation of Moroccan public markets data: statistical and temporal analysis from the official portal marchespublics.gov.ma
Collect, structure, and analyze public procurement data published, applying techniques such as:
- Descriptive statistics
- Multivariate analysis (PCA, clustering)
- Time series analysis (forecasting, seasonality)
- Interactive visualization (dashboard)
- Web scraping from marchespublics.gov.ma
- Cleaning raw data (Python + Pandas)
- Building a structured dataset
- Encoding qualitative variables
- Global statistics: annual distribution, by region, by buyer
- Visualizations: histograms, heatmaps
- Typology of markets by sector and category
- PCA for dimensionality reduction
- Clustering (K-means, hierarchical) to profile typical markets
- Cross-analysis: buyer-type-object
- Time series analysis (ARIMA, moving averages…)
- Seasonality detection
- Forecasting future market volume
- Creation of an interactive dashboard (Power BI, R, Python)
- Filterable visualizations (region, year, type…)
- Export of results
- Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)
- Web scraping (BeautifulSoup, Requests, Selenium)
- Visualization (Plotly, Streamlit, Power BI)
- Database: CSV / SQLite / PostgreSQL
- Cleaned public markets database
- Analytical report with figures and insights
- Interactive dashboard
- Documented source code
- Final presentation (PPT)
- Education: Engineer in Statistics, Demography, and Big Data