Here are three common evaluation metrics for regression problems: Mean Absolute Error (MAE) is the mean of the absolute value of the errors: Mean Squared Error (MSE) is the mean of the squared errors: Root Mean Squared Error (RMSE) is the square root of the mean of the squared errors: Comparing these metrics: MAE is…
Answered Jan 15, 2020 · 2.0K Views Read answer →
# Multiple Linear Regression # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('50_Startups.csv') X = dataset.iloc[:, :-1].values y = dataset.iloc[:, 4].values # Encoding categorical data from sklearn.preprocessing import…
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# Multiple Linear Regression # Importing the dataset dataset = read.csv('50_Startups.csv') # Encoding categorical data dataset$State = factor(dataset$State, levels = c('New York', 'California', 'Florida'),
Answered Jan 15, 2020 · 912 Views Read answer →
First we import all the libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns USAhousing = pd.read_csv('USA_Housing.csv') X = USAhousing[['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms',
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