42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
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#! /usr/bin/python3
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from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import numpy as np
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import requests
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url = 'https://git.wmi.amu.edu.pl/s434695/ium_434695/raw/commit/2301fb86e434734376f73503307a8f3255a75cc6/vgsales.csv'
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r = requests.get(url, allow_redirects=True)
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open('vgsales.csv', 'wb').write(r.content)
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df = pd.read_csv('vgsales.csv')
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def regression_model():
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model = Sequential()
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model.add(Dense(16,activation = "relu", input_shape = (x_train.shape[1],)))
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model.add(Dense(32,activation = "relu"))
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model.add(Dense(1,activation = "relu"))
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model.compile(optimizer = "adam", loss = "mean_squared_error")
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return model
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df['Nintendo'] = df['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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df = df.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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df
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y = df.Nintendo
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df=((df-df.min())/(df.max()-df.min()))
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x = df.drop(['Nintendo'],axis = 1)
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x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
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model = regression_model()
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model.fit(x_train, y_train, epochs = 600, verbose = 1)
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y_pred = model.predict(x_test)
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model.save('model1')
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