Using these packages in python jupyter, import numpy as np import pandas as pd import seaborn as sns import math from sklearn import preprocessing from sklearn import datasets import sklearn from scipy import stats import matplotlib import matplotlib.pyplot as plt %matplotlib inline matplotlib.style.use('ggplot') np.random.seed(1) And this: X = datasets.load_wine(as_frame=True) data = pd.DataFrame(X.data, columns=X.feature_names) data['class'] = pd.Series(X.target) data = data.drop(list(data.columns[5:-1]),axis=1) #Keep only the first five columns and the class label print(" classes ",data['class'].unique()) #The different class labels in the data .. We have three class labels, 0, 1, 2 print(" class distribution ",data['class'].value_counts()) #Shows the number of rows for each class data.info() data.head()

Q3- Part A- Normalize the data such that each attribute has a minimum of 0 and a maximum of 1

Don't change the content of the original dataframe. The final result will be stored in data_scaled

#Normalizing all the columns .. Accessing the columns with the columns' names data_scaled = data.copy() Part B- Standarize the data such that each attribute has a mean 0 and a standard deviation of 1 (unit variance)

Hint: use preprocessing.StandardScaler

Don't change the content of the original dataframe. The final result will be stored in data_scaled

#Standarizing all the columns .. Accessing the columns with the columns' names data_scaled = data.copy() Part C- 

Discretization

Equal-Width Binning, Convert the values in each attribute to discrete values and use 5 bins.

Use the pandas cut method, pd.cut data_discrete = data.copy

Part D- Equal Frequency Binning,Convert the values in each attribute to discrete values and use 5 bins.

Use the pandas qcut method, pd.qcut data_freq = data.copy()