import numpy as np import pandas as pd import tensorflow as tf import sys import wget from tensorflow import keras from sklearn.metrics import r2_score, mean_squared_error from math import sqrt from sklearn.model_selection import train_test_split from sklearn import preprocessing # Importing the dataset url = 'https://git.wmi.amu.edu.pl/s434804/ium_434804/raw/branch/master/country_vaccinations.csv' wget.download(url, out='country_vaccinations.csv', bar=None) df = pd.read_csv('country_vaccinations.csv').dropna() dataset = df.iloc[:, 3:-3] sys.stdout=open("prediction_output.txt","w") print(dataset.head()) dataset = df.groupby(by=["country"], dropna=True).sum() X = dataset.loc[:,dataset.columns != "daily_vaccinations"] y = dataset.loc[:,dataset.columns == "daily_vaccinations"] # Splitting the dataset into the Training set and Test set X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42) # Feature Scaling model = keras.Sequential([ keras.layers.Dense(512,input_dim = X_train.shape[1],kernel_initializer='normal', activation='relu'), keras.layers.Dense(512,kernel_initializer='normal', activation='relu'), keras.layers.Dense(256,kernel_initializer='normal', activation='relu'), keras.layers.Dense(256,kernel_initializer='normal', activation='relu'), keras.layers.Dense(128,kernel_initializer='normal', activation='relu'), keras.layers.Dense(1,kernel_initializer='normal', activation='linear'), ]) model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error']) model.fit(X_train, y_train, epochs=50, validation_split = 0.3) prediction = model.predict(X_test) print(prediction) sys.stdout.close() model.save('vaccines_model')