Project status

• Finished

File structure

2023, Ecuador: demographic analyst
│
├── data
│   ├── processed_csv # processed .csv files
│   └── raw_csv # raw .csv files
│   │
│   ├── ml_best_models # best ml models
|   |   └── best_model.pkl
│   │
│   ├── images # images used on different stage and files
|   |   └── approximate_location_of_population_age_60_plus.png
|   |   └── census_by_age_2010__2022.png
|   |   └── census_percentage_by_age_2010__2022.png
|   |
│   |__ genered_maps # interactive maps
|       └── heatmap_grouped_marker_density_clusters_map.html
|
├── notebooks
|   |
│   ├── packages # commons function from multiple notebooks
|   |   └── common_functions.py
|   |
│   |-- 1_etl.ipynb
|   |__ 2_eda_enrichment_pre_prosesing.ipynb
|   |__ 3_data_viz.ipynb
|   |__ 4_auto_sklearn.ipynb
|
├── .gitattributes
|
├── .gitignore
│
├── requirements.txt
|
│-- technical_report.md
|
│-- technical_report.pdf
|
│-- LICENSE.md
|
└── README.md

Functions

• Interest Insights on Ecuador, Segmented by Pichincha, Quito, and Sub-segmented by Current Population (2023) Age 50 Plus

  • Population by Educational Level
  • Population Distribution by Age and Educational Level
  • Population by Area
  • Population by Natural Region
  • Population by Education Level and Sex
  • Education Trends Between 2013 and 2014
  • Population Age 60 Plus by approximately geographic Location

Packages used

• Programing language:
  • Python

    • Libraries from data analysis:

      • numpy
      • pandas
      • missingno

    • Libraries from data visualization:

      • matplotlib
      • seaborn
      • folium

    • Libraries from machine learning:

      • scikit-learn
      • joblib

Package installation

pip3 install -r requirements.txt

Team

• Data Scientist and project manager: Yane, Ian Cristian
• Target company: CORPORACIÓN GESTIÓN SOSTENIBLE
• Director of target company: Mantilla, Jhon