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