Predicting Employee Attrition Using Machine Learning

Description:

Please refer to the Sample Project. Each project has unique content based on its topic. The above PDF sample is for an HRM project.

The study titled “Predicting Employee Attrition Using Machine Learning” focuses on understanding the main reasons why employees choose to stay in or leave an organization. Employee attrition refers to the steady loss of staff over time when employees resign for different reasons—such as dissatisfaction with their job, heavy workload, poor work-life balance, stress, relocation, lack of career growth, or personal issues.

Although some amount of attrition is normal in every company, high or unexpected employee turnover can create serious problems. It affects business continuity, increases hiring costs, and makes human resource planning more difficult. This study aims to identify the factors that influence attrition so organizations can make better decisions and reduce unnecessary employee loss.

OBJECTIVES OF THE STUDY

• To analyze the historical HR data of employees and identify the major factors that influence attrition.

• To build a machine learning model that can predict which employees are at risk of leaving the organization.

• To study how variables such as demographics, job role, compensation, performance, and work environment impact employee turnover.

• To identify attrition trends and patterns that can help management make informed decisions and support strategic workforce planning.

• To evaluate the accuracy and reliability of different machine learning algorithms used for predicting employee attrition.

RESEARCH METHODOLOGY

The methodology adopted for this project involved the following steps:

• Understanding the theoretical concepts related to the study.

• Conducting a questionnaire-based survey.

• Analyzing primary data collected from respondents.

• Analyzing secondary data obtained from published and organizational sources.

RESEARCH DESIGN

A research design outlines how the researcher collects, measures, and analyzes data. In this study, the researcher uses a descriptive research design. Descriptive research helps the researcher understand the characteristics of a group or phenomenon by answering questions such as who, what, when, where, and how.

Using the information collected from respondents and available organizational sources, the researcher draws logical conclusions.

TYPE OF RESEARCH

This study uses a descriptive research approach. Descriptive research presents an accurate profile of individuals, groups, or situations. It requires the researcher to collect data, organize it into meaningful patterns, and interpret it to understand specific behaviors or trends.

In this project, the researcher collects data through a well-structured questionnaire and gathers secondary information from the organization to gain a detailed understanding of employee attrition patterns.

DATA COLLECTION METHODS

Data for the study was collected in two forms:

1. Primary Data

Primary data refers to information collected first-hand for the specific purpose of this study. It was gathered directly from individuals using a structured questionnaire. This type of data is original, reliable, and closely aligned with the project objectives.

2. Secondary Data

Secondary data consists of information that has been previously collected and published by others. This includes books, journals, articles, research papers, and organizational reports relevant to the topic Predicting Employee Attrition Using Machine Learning.