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

Зміст курсу

Logistic Regression Mastering

bookImport 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.

Завдання

  1. Import pandas (as pd) library.
  2. Import the "experiment_data.csv" using pandas.
  3. Display the first 10 rows of the DataFrame.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

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.

Завдання

  1. Import pandas (as pd) library.
  2. Import the "experiment_data.csv" using pandas.
  3. Display the first 10 rows of the DataFrame.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 1. Розділ 2
some-alt