Data preprocessing is a data mining technique that is used to transform the raw data in a useful and efficient format. The preprocessing process can be divided into 3 major parts namely data Cleaning, Data Transformation and Data Reduction. The Figure 1 below presents the techniques that can be used to preprocess the raw data. Data Data Cleaning Data Reduction Transformation Missing Data Remove the rows Fill in the missing values Noisy Data Binning Method Regression Clustering Normalization Attribute Selection Discretization Hierarchy Generation Data Cube Aggregation Attribute Subset Selection Numerosity Reduction Dimensionality Reduction . . Preprocess the boston dataset using Jupyther notebook. The steps used should be given with good explanation and justification. Load the dataset using the codes given below: from sklearn import datasets dir(datasets) import pandas as pd data = pd.DataFrame(datasets.load_boston().data) data.columns = datasets.load_boston().feature_names data.head(5) Criteria Outstanding Data Cleaning Able to clean the dataset using the techniques learnt to handle missing data and noisy data with good explanation. Criteria Outstanding Data Transformation Able to transform the data with good explanation with supportive arguments. Criteria Outstanding Data Reduction Able to reduce the data with good explanation with supportive arguments