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

Contenu du cours

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.
Tâche

Swipe to start coding

  1. Import pandas (as pd) library.

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

  3. Display the first 10 rows of the DataFrame.

Solution

Mark tasks as Completed
Switch to desktopPassez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous
Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 1. Chapitre 2
AVAILABLE TO ULTIMATE ONLY
We're sorry to hear that something went wrong. What happened?
some-alt