Logistic Regression Mastering

Course Content

Logistic Regression Mastering

Logistic Regression Mastering

Introduction Import Data Exploratory Data Analysis Data Preprocessing Train and Test Split Model Training Model Evaluation

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

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

Mark tasks as Completed

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

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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

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

Mark tasks as Completed

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

Section 1. Chapter 2

AVAILABLE TO ULTIMATE ONLY

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