📅 2024-09-28 — Session: Data Analysis and Regression Modeling
🕒 05:30–06:50
🏷️ Labels: Data Analysis, Regression Modeling, Economic Forecasting, Poverty Estimation, Python
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary objective of this session was to perform data analysis and regression modeling to predict future economic indicators and estimate poverty levels using linear regression techniques.
Key Activities
- Linear Extrapolation of Economic Data: Implemented linear regression models to predict future values of Canasta Básica Alimentaria, IPC, and RIPTE datasets using the last four months of data.
- Forecasting Labor Statistics: Developed methods for forecasting labor statistics by adjusting for seasonal differences and visualizing results.
- Poverty Prediction Models: Explored and outlined regression models to predict poverty levels based on employment metrics and economic indicators.
- Data Preparation for Regression: Prepared time series datasets for regression modeling by combining economic indicators and handling missing data.
- Quadratic Spline Interpolation: Applied quadratic spline interpolation to convert quarterly data into monthly data.
- Handling Missing Values: Implemented methods for handling missing values in regression models using Python and sklearn.
Achievements
- Successfully developed and implemented several regression models for economic forecasting and poverty estimation.
- Prepared datasets for analysis and handled missing data effectively.
Pending Tasks
- Further validation and testing of the regression models, particularly for poverty estimation.
- Exploration of additional economic indicators for more comprehensive models.