Question

Problem:

Use the credit.csv dataset to build classification model using KNN. The target variable is default which is a binary label to indicate of the loan is default (yes, no). Use all other variables for your feature set.
Read credit.csv into a dataframe credit_df. Display the first 5 rows
hint: Handel the missing value character '?' as na value.*
Print the number of rows and columns in the dataframe
Print the total number of rows that have missing values
Determine categorical and numerical features and assign each into numerical_features and categorical_features

Answer 1

To build a KNN classification model using the 'credit.csv' dataset, read the data into a dataframe with pandas, manage missing values, display the first five rows, determine the number of rows with missing values, and identify categorical and numerical features for preprocessing.

Step-by-step explanation:

To build a classification model using KNN with the credit.csv dataset, you will first need to read this dataset into a pandas dataframe, handling missing values that are represented by the character '?' accordingly. Once the dataframe named credit_df is created, you'll be able to display the first 5 rows using the head() method, print the shape of the dataframe to identify the number of rows and columns, and determine the number of rows with missing values. Identifying categorical and numerical features contributes to the data preprocessing step which is critical in model building in machine learning.

Data Import and Pre-processing

For example, reading the CSV file into a dataframe while treating '?' as NaN (Not a Number) would resemble the following code snippet using pandas:

import pandas as pd
credit_df = pd.read_csv('credit.csv', na_values='?')
To display the first 5 rows:print(credit_df.head())
Check the number of rows and columns:

print(credit_df.shape)
Determining the number of rows with missing values:

print(credit_df.isnull().sum().sum())

Identifying categorical and numerical features involves analyzing the data types of the columns and assigning them to lists:

categorical_features = credit_df.select_dtypes(include=['object']).columns.tolist()
numerical_features = credit_df.select_dtypes(exclude=['object']).columns.tolist()
After these steps, further preprocessing includes handling missing values, encoding categorical features, and scaling numerical features as necessary before applying the KNN algorithm for classification.