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Import Data | Logistic Regression Mastering
Logistic Regression Mastering
course content

Course Content

Logistic Regression Mastering

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.

Task

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

Task

  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 desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

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.

Task

  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 desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 2
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