Predicting Employee Turnover At Sailsfort Motors

Project Overview: Predicting Employee Turnover at Sailsfort Motors

Introduction
This project aims to analyze the factors contributing to employee turnover at Sailsfort Motors, an automobile company. By leveraging a combination of logistic regression and tree-based models, we will identify key predictors of employee turnover and develop strategies to enhance employee retention.

Objectives

• Predict Turnover: Build models to predict whether an employee will leave the company.
• Identify Key Factors: Determine the most significant factors influencing employee turnover.
• Retention Strategies: Provide actionable insights to improve employee retention.

Data Description
The dataset includes the following attributes:

-Satisfaction Level: Employee satisfaction level.
-Last Evaluation: Last performance evaluation score.
-Number of Projects: Number of projects the employee has worked on.
-Average Monthly Hours: Average monthly working hours.
-Time Spent at Company: Number of years the employee has been with the company.
-Work Accident: Whether the employee has had a work accident (1: Yes, 0: No).
-Left: Whether the employee has left the company (1: Yes, 0: No).
-Promotion in Last 5 Years: Whether the employee has been promoted in the last five years (1: Yes, 0: No).
-Department: Department the employee belongs to.
-Salary: Salary level (Low, Medium, High).

Methodology
-Data Preprocessing: Clean and preprocess the data to handle missing values, categorical variables, and data normalization.
-Exploratory Data Analysis (EDA): Perform EDA to understand the distribution of data and identify patterns and correlations.
-Feature Engineering: Create relevant features to enhance model performance.

Model Building:
-Logistic Regression: Build a logistic regression model to identify the probability of employee turnover.
-Tree-Based Models: Build tree-based models (e.g., Decision Tree, Random Forest) to capture non-linear relationships and interactions between features.
-Model Evaluation: Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score.

-Insights and Recommendations: Analyze the results to identify key factors leading to employee turnover and provide recommendations to improve retention.

Expected Outcomes
-Predictive Models: Accurate models to predict employee turnover.
-Key Insights: Identification of the most significant factors contributing to employee turnover.
-Retention Strategies: Data-driven recommendations to improve employee satisfaction and retention.

By predicting employee turnover and understanding its driving factors, this project aims to provide valuable insights for Sailsfort Motors to enhance their HR strategies and foster a more stable and satisfied workforce.