hello, i need help with step 2 in a lecture for Intro to Statistical Software, in this problem we used the python Jupyter notebook, i put step 1 and step 3 just in case and to give context on step 2.

Steps:

1. Make a python Jupyter notebook

2. Use the following commands to read a breast cancer dataset from python Scilearn datasets and find a dataframe we name "df". There are two classes listed in variable "target".

from sklearn.datasets import load_breast_cancer data = load_breast_cancer()

# Import pandas import pandas as pd# Read the DataFrame, first using the feature data df = pd.DataFrame(data.data, columns=data.feature_names)# Add a target column, and fill it with the target data df['target'] = data.target# Show the first five rows df.head()

Use 75% training data to perform KNN, LDA, QDA, Logistic Regression, random forest and decision tree classification method to classify the observations in the rest of the test data set. Use Confusion matrix to compare the classifier performances.

3. Consider the following data.

Auto.csv Download this (https://canvas.emporia.edu/courses/50680/files/5137714/download?download_frd=1) copy and paste the link in an email to yourself and send, after sending it it will become available to download Load in the data in python and use all clustering methods (KMeans and Hierarchical clustering with different linkages) that you know to cluster observations.

(Note that you have to remove some non-numerical variable(s) to achieve this)

please help me, im stuck in part 2, i just posted part 3 just in case the data in the auto document is important, but please help me with part 2 and i dont know how its supposed to be address, please