Overview: This repository contains code for a regression task focused on predicting Dengue fever cases based on various environmental and socio-economic factors. Dengue fever is a mosquito-borne viral infection that causes flu-like illness, and its incidence is influenced by factors such as temperature, precipitation, population density, and sanitation conditions.
Dataset: The dataset used for this task is sourced from Kaggle. It contains historical data on Dengue fever cases as well as various environmental and socio-economic features such as temperature, humidity, precipitation, population density, and GDP. The dataset is divided into training and testing sets, with the training set used for model training and the testing set used for evaluation.
Goal: The goal of this task is to build a regression model that accurately predicts the number of Dengue fever cases based on the available features. The model aims to help public health officials and policymakers better understand and anticipate Dengue outbreaks, allowing for proactive measures to mitigate the spread of the disease.