Python for Machine Learning - The Complete Beginners Course - Importing the Dataset

Python for Machine Learning - The Complete Beginners Course - Importing the Dataset

Assessment

Interactive Video

Information Technology (IT), Architecture, Social Studies

University

Practice Problem

Hard

Created by

Wayground Content

FREE Resource

The video tutorial covers the process of using a dataset in Python, focusing on data exploration and preprocessing. It explains the features of the dataset, including customer ID, gender, age, annual income, and spending score. The tutorial introduces the K-means clustering algorithm, emphasizing the need for data preprocessing without a dependent variable. It details the import of necessary libraries like Numpy, Matplotlib, and pandas, and demonstrates converting categorical data into binary vectors using value counts.

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5 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the purpose of the 'spending score' column in the dataset?

To identify the customer's age

To calculate the annual income

To measure how much a customer has spent in the mall

To determine the customer's gender

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which machine learning algorithm is mentioned as unsupervised in the video?

Linear Regression

K-means Clustering

Decision Tree

Support Vector Machine

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which library is NOT mentioned as part of the data preprocessing process?

Pandas

Numpy

Scikit-learn

Matplotlib

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the first step in converting categorical values into binary vectors?

Normalizing the data

Sorting the data

Counting unique values

Calculating the mean

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How many females are present in the dataset according to the output?

88

100

112

120

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