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import pandas as pd def download_data(): import os import urllib if os.path.exists('cars.csv'): print('Data already downloaded') return urllib.request.urlretrieve('https://think.cs.vt.edu/corgis/datasets/csv/cars/cars.csv','cars.csv') print('Download Complete') download_data() df = pd.read_csv('cars.csv') df.head() df = df[df['Make'] == 'Honda'] df = df[df['Fuel Information.Fuel Types'] == 'Gasoline'] df = df[df['Fuel Information.Combined MPG'] < df['Fuel Information.Combined MPG'].quantile(0.9)] features = ['Fuel Information.Highway MPG', 'Engine Information.Engine Statistics.Horsepower'] x = df[features] x = x.values x = x.astype('float32') x = (x-x.min())/(x.max()-x.min()) y = df['Fuel Information.Combined MPG'] y = y.astype('float32') y = (y-y.min())/(y.max()-y.min()) y = y.values import matplotlib.pyplot as plt plt.scatter(x[:,0], y, c='r',label='data') plt.show() plt.scatter(x[:,1], y, c='r',label='data') plt.show() from keras.models import Sequential from keras.layers import Dense model = Sequential() model.add(Dense(1, input_dim=2, kernel_initializer='normal', activation='linear')) model.compile(loss='mse', optimizer='adam', metrics=['mse']) model.fit(x, y, epochs=100, batch_size=1, verbose=1) scores = model.evaluate(x, y, verbose=0) print(model.get_weights())