Repository for the machine learning techniques subject.
Can you predict local epidemics of dengue fever? Dengue fever is a mosquito-borne disease that occurs in tropical and sub-tropical parts of the world. In mild cases, symptoms are similar to the flu: fever, rash, and muscle and joint pain. In severe cases, dengue fever can cause severe bleeding, low blood pressure, and even death.
Because it is carried by mosquitoes, the transmission dynamics of dengue are related to climate variables such as temperature and precipitation. Although the relationship to climate is complex, a growing number of scientists argue that climate change is likely to produce distributional shifts that will have significant public health implications worldwide.
In recent years dengue fever has been spreading. Historically, the disease has been most prevalent in Southeast Asia and the Pacific islands. These days many of the nearly half billion cases per year are occurring in Latin America:
Our goal is to predict the total_cases label for each city, year and weekofyear in the test set. There are two cities, San Juan and Iquitos. We will make one submission that contains predictions for both cities.
- Milestone 1: Principal Component Analysis
- Milestone 2: Application of Hierarchical Clustering
- Milestone 3: Application of K-means
- Milestone 4: Decision Trees for Regression
- Milestone 5: Features Engineering (study and selection)
- Milestone 6: Predictive Model Building
- Milestone 7: Model Self-improvement
This project has been coded using Python 2.7, with IDE Spyder for coding and compilation.
- Carlos Córdoba Ruiz
- Álvaro Ángel-Moreno Pinilla
- Julián García Sánchez