example of dataset preprocessing using the Boston Housing dataset. Exercise $1$ Python Code # Import necessary librariesimport pandas as pdimport numpy as npfrom sklearn.preprocessing import StandardScaler, LabelEncoderfrom sklearn.model$\_$selection import train$\_$test$\_$split # Load the Boston Housing datasetfrom sklearn.datasets import load$\_$bostonboston $=$ load$\_$boston$()$data $=$ pd$.$DataFrame$($boston$.$data, columns$=$boston.feature$\_$names$)$data$[\text{'}$target$\text{'}]=$ boston.target # $1.$ Understand the Dataprint$($data$.$head$())$print$($data$.$info$())$ # $2.$ Handle Missing Values# Check for missing valuesprint$($data$.$isnull$().$sum$())$# There are no missing values in the dataset # $3.$ Encode Categorical Data# The Boston Housing dataset does not contain any categorical features # $4.$ Handle Outliers# Visualize the data to identify outliersimport matplotlib.pyplot as pltdata.plot$($kind$=$'box', subplots$=$True, layout$=(4,4),$ figsize$=(12,12))$plt$.$show$()$ # There are some potential outliers in the 'LSTAT' and $\text{'}$RM$\text{'}$ features # Handle outliers using cappingq$1=$ data$[\text{'}$LSTAT$\text{'}].$quantile$(0\mathrm{.}25)$q$3=$ data$[\text{'}$LSTAT$\text{'}].$quantile$(0\mathrm{.}75)$iqr $=$ q$3-$ q$1$data$[\text{'}$LSTAT$\text{'}]=$ np$.$clip$($data$[\text{'}$LSTAT$\text{'}],$ q$1-1\mathrm{.}5*$ iqr, q$3+1\mathrm{.}5*$ iqr$)$ q$1=$ data$[\text{'}$RM$\text{'}].$quantile$(0\mathrm{.}25)$q$3=$ data$[\text{'}$RM$\text{'}].$quantile$(0\mathrm{.}75)$iqr $=$ q$3-$ q$1$data$[\text{'}$RM$\text{'}]=$ np$.$clip$($data$[\text{'}$RM$\text{'}],$ q$1-1\mathrm{.}5*$ iqr, q$3+1\mathrm{.}5*$ iqr$)$ # $5.$ Scale and Normalize Datascaler $=$ StandardScaler$()$X $=$ scaler.fit$\_$transform$($data$.$drop$(\text{'}$target$\text{'},$ axis$=1))$y $=$ data$[\text{'}$target$\text{'}]$ # $6.$ Feature Engineering# No additional feature engineering is required for this dataset # $7.$ Feature Selection# No feature selection is required for this dataset # $8.$ Data SplittingX$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}2,$ random$\_$state$=42)$ # $9.$ Data Transformation# No additional data transformation is required for this dataset # $10.$ Document the Preprocessing Stepsprint$("$Preprocessing steps:"$)$print$("1.")$print$("2.)$print$("3.")$print$("4.")$print$("5.")$print$("6.")$print$("7.")$ In this example, we: describe the data preprocessing steps $1.2.3.$ This example demonstrates how to handle outliers in a dataset, which is an important step in the preprocessing pipeline. The specific steps you take will depend on the characteristics of your dataset and the requirements of your project.Remember, dataset preprocessing is an iterative process, and you may need to revisit certain steps as you explore the data and develop your models.