2018 Nissan LEAF SV

Introducing the 2018 Nissan LEAF, the perfect combination of efficiency and power. This electric vehicle boasts an impressive 147 horsepower and 236 foot-pounds of torque, making it a dream to drive. And with its 1-speed direct-drive automatic transmission, you'll experience seamless shifting and a smooth ride every time you hit the road. But the 2018 Nissan LEAF isn't just about performance, it's also incredibly eco-friendly. With its electric engine, you'll never have to stop for gas again. Plus, you'll reduce your carbon footprint and save money on fuel costs. It's a win-win for you and the planet. Step inside and you'll be greeted with a spacious and modern interior, complete with advanced technology features. Stay connected with the NissanConnect system, and enjoy the convenience of hands-free calling, navigation, and more. Safety is also a top priority in the 2018 Nissan LEAF. With features like automatic emergency braking, blind spot warning, and rear cross traffic alert, you can drive with peace of mind knowing you and your passengers are protected. Don't miss out on the opportunity to own this cutting-edge electric vehicle. Visit our dealership today and take the 2018 Nissan LEAF for a test drive. Experience the future of driving. <|endoftext|>L'Oréal Paris Infallible Pro-Spray & Set Makeup Extender Setting Spray Lock in your makeup look with the L'Oréal Paris Infallible Pro-Spray & Set Makeup Extender Setting Spray. This lightweight, oil-free setting spray will keep your makeup looking fresh and flawless for up to 24 hours. Formulated with a micro-fine mist that creates a breathable barrier between your skin and your makeup, this setting spray will help prevent makeup from melting, fading, or creasing throughout the day. Simply spritz it on after applying your makeup, and you're ready to take on the day (or night) without worrying about touch-ups.<|endoftext|># -*- coding: utf-8 -*- """ Created on Sun Apr 21 21:19:32 2019 @author: shashi """ import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score from sklearn.preprocessing import LabelEncoder, OneHotEncoder # Importing the dataset dataset = pd.read_csv('50_Startups.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, 4].values # Encoding categorical data labelencoder = LabelEncoder() X[:, 3] = labelencoder.fit_transform(X[:, 3]) onehotencoder = OneHotEncoder(categorical_features = [3]) X = onehotencoder.fit_transform(X).toarray() # Avoiding the Dummy Variable Trap X = X[:, 1:] # 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 = 0) # Fitting Multiple Linear Regression to the Training set regressor = LinearRegression() regressor.fit(X_train, y_train) # Predicting the Test set results y_pred = regressor.predict(X_test) # Building the optimal model using Backward Elimination import statsmodels.formula.api as sm X = np.append(arr = np.ones((50, 1)).astype(int), values = X, axis = 1) X_opt = X[:, [0, 1, 2, 3, 4, 5]] regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit() regressor_OLS.summary() X_opt = X[:, [0, 1, 3, 4, 5]] regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit() regressor_OLS.summary() X_opt = X[:, [0, 3, 4, 5]] regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit() regressor_OLS.summary() X_opt = X[:, [0, 3, 5]] regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit() regressor_OLS.summary() X_opt = X[:, [0, 3]] regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit() regresso