Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Import Data | Logistic Regression Mastering
Logistic Regression Mastering
course content

Conteúdo do Curso

Logistic Regression Mastering

book
Import Data

We will start by importing our data using the famous pandas library. This is an overview of the features in our dataset:

  • enrollee_id: Unique ID for the candidate;

  • city: City code;

  • city_development _index: Development index of the city (scaled);

  • gender: Gender of the candidate;

  • relevent_experience: Relevant experience of candidate;

  • enrolled_university: Type of University course enrolled, if any;

  • education_level: Education level of the candidate;

  • major_discipline: Education major discipline of the candidate;

  • experience: Candidate's total experience in years;

  • company_size: No of employees in current employer's company;

  • company_type: Type of current employer;

  • lastnewjob: Difference in years between previous job and current job;

  • training_hours: training hours completed;

  • target: 0 – Not looking for a job change, 1 – Looking for a job change.

Methods description

Modules and Methods Used

  • pandas: Module for data manipulation and analysis;
    • `.read_csv()**: Function to read a CSV file into a DataFrame;
    • .head(): Method to display the first n rows of a DataFrame.
Tarefa
test

Swipe to show code editor

  1. Import pandas (as pd) library.

  2. Import the "experiment_data.csv" using pandas.

  3. Display the first 10 rows of the DataFrame.

Mark tasks as Completed
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 1. Capítulo 2
We're sorry to hear that something went wrong. What happened?
some-alt