In 2019 I had the chance of visiting an astonishing country, Sri Lanka. During our stay there, although we were already aware of it and had taken the necessary precautions, we had a few conversations with local people about the phenomenon of Dengue and the risk that one can run every day by being bitten by a particular species of mosquitoes, Aedes mosquitos, that carry this disease. From there my curiosity about this phenomenon grew and I began to wonder if it was possible in some way to use statistical and / or machine learning techniques to analyze and predict it. This project of mine, therefore, is based on a competition held by the site DrivenData.org, called "DengAI: Predicting Disease Spread" (https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/), which focuses on finding a way of predicting the next dengue fever local epidemic in San Juan, Puerto Rico and Iquitos, Peru.
import pandas as pd from pandas.api.types import is_numeric_dtype import numpy as np from numpy import array from math import sqrt from statsmodels.tools import eval_measures import statsmodels.formula.api as smf import statsmodels.api as sm from datetime import * import seaborn as sns sns.set_style('darkgrid') import matplotlib.pyplot as plt from matplotlib.pyplot import figure %matplotlib inline from sklearn.metrics import mean_absolute_error from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score, KFold from sklearn.model_selection import GridSearchCV from sklearn import metrics from sklearn.preprocessing import MinMaxScaler
!pip install xgboost import xgboost as xgb print("xgboost", xgb.version) from xgboost import XGBRegressor from xgboost import plot_importance
!pip install tensorflow !pip install keras==2.3.1 from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.layers import Dropout from keras.layers import Bidirectional %load_ext autoreload %autoreload 2