# creation de la ligne de regression
plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
plt.style.use(['dark_background', 'fast'])
plt.title(col)
plt.xlabel(col)
plt.ylabel('prix')
# Fractionnement des donnees entre train et test set
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=0)
scaler = StandardScaler()
scaler.fit(x_train)
scaler.fi(x_test)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
#contruction de notre model de regerssion
regressor = LinearRegression()
regressor.fit(x_train, y_train) # initialize & fit the model
y_pred = regressor.predict(x_test) # now predic
#j'adapte le model de regression lineaire a l'ensemble de donnees d'apprentissagege
#regressor.fit(x_train, y_train)
#faire de nouvelle prediction
#y_pred = regressor.predict(x_test)
