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Multiple regression is a statistical technique used to understand the relationship between one dependent variable and two or more independent variables. In Python, implementing multiple regression can be done using libraries such as `pandas` for data manipulation, `matplotlib` for visualization, and `scikit-learn` for building the regression model. Here, we will work with the well-known `Boston Housing dataset` from `sklearn.datasets`, which contains data about housing prices and various factors that affect them, like crime rate, average number of rooms, and distance to employment centers. First, you need to install the required libraries if you haven't already. You can install them using `pip`: ```bash pip install pandas scikit-learn matplotlib seaborn ``` ### Step 1: Import the necessary libraries ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score ``` ### Step 2: Load the dataset The Boston dataset is available in `sklearn.datasets`, and we can load it directly into a pandas DataFrame for easier manipulation. ```python # Load the Boston dataset boston = load_boston() # Create a pandas DataFrame from the dataset _______ ____ ________ ______ __________ ___.
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