Multiple Linear Regression:
Instead of just looking at how one thing relates to another thing (simple linear regression), you can look at the relationship between a lot of different things and the thing you want to predict.
A linear regression model is a statistical model that’s frequently used in data science.
It’s also one of the basic building blocks of machine learning! Multiple linear regression (MLR/multiple regression) is a statistical technique.
It can use several variables to predict the outcome of a different variable.
The goal of multiple regression is to model the linear relationship between your independent variables and your dependent variable.
It looks at how multiple independent variables are related to a dependent variable.
Project | 03
Project | 03 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[:, -1].values
print(X)
# Encoding categorical data
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')
X = np.array(ct.fit_transform(X))
print(X)
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Training the Multiple Linear Regression model on the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
# Predicting the Test set results
y_pred = regressor.predict(X_test)
np.set_printoptions(precision=2)
print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))